Notes by Retraice

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Re66: TABLE-DRIVEN-AGENT Part 2 (AIMA4e p. 48)

retraice.com

A basic agent program in both AIMA4e pseudocode and Python.

Air date: Wednesday, 30th Nov. 2022, 11:00 PM Eastern/US.

We're focusing on the math and code of Russell & Norvig (2020).

Terminology tweaks from yesterday

The below definitions and descriptions are still works in progress, especially the last one. * We `initialize' objects in memory, not `initiate' them; * dictionary: in Python, a type of container (an alternative to a variable that organizes data) e.g. lists, tuples, dictionaries;^1 an implementation of the `associative array' data structure.^2 * `memory object': should have probably said `data in memory' or `file' or whatever. Cf. Althoff: "In Python, each data value, like 2 or `Hello, World!' [data type str for string], is called an object." Objects, in this context, have three properties:

1. identity (location in memory, never changes);

2. data type (category of data which determines properties it has, never changes);

3. value (the data it represents, number 2 represents value 2).^3

* computer program function: A software input-output machine: "A program unit that given values for input parameters computes a value."^4 * mathematical function: A relation between two sets: "[A mapping] from one set X to another set Y . A relation R defined on the Cartesian product x×y in which for each element x in X there is precisely one element y in Y with the property that (x,y) is a member of R."^5 * *CORRECTION*: The `agent function' is not `the smart part' of the agent program. "The agent function is an abstract mathematical description.... that maps any percept sequence to an action"; "the agent program is a concrete implementation [of the agent function], running within some physical system." (100!black!//Russell & Norvig (2020) pp. 37-37) Function: math. Program: code. [Correction at Jan 2nd, 2023.] * intelligent agent function: The input-output (percept:action) table (or whatever) that makes a dumb program (in which the program steps are specified by the programmer to yield a specific result in isolation) smart^6 (in which the program steps include an element, the implemented agent function, that makes the result dependent upon percepts from the environment and seem to reflect intelligence).^7

Working TABLE-DRIVEN-AGENT code

See figure below.

ABC: Always be coding

If the computer control game is player-oriented, reading code is to writing code what X's-and-O's is to on-field gameplay. The only way to calibrate, to see if you can do what you think you can do, is to code it. ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

PIC TABLE-DRIVEN-AGENT pseudocode, AIMA Python, and three Retraice iPython demonstrations ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

__

References

Althoff, C. (2017). The Self-Taught Programmer: The Definitive Guide to Programming Professionally. Triangle Connection. ISBN: 978-0999685907. Searches: https://www.amazon.com/s?k=9780999685907 https://www.google.com/search?q=isbn+9780999685907 https://www.theselftaughtprogrammer.io/

Barlow, H. B. (2004). Guessing and intelligence. (pp. 382-384). In Gregory (2004).

Butterfield, A., & Ngondi, G. E. (Eds.) (2016). A Dictionary of Computer Science. Oxford University Press, Kindle, 7th ed. ISBN: 978-0191002885. Searches: https://www.amazon.com/s?k=9780191002885 https://www.google.com/search?q=isbn+9780191002885 https://lccn.loc.gov/2015952805

Gregory, R. L. (Ed.) (2004). The Oxford Companion to the Mind. Oxford University Press, 2nd ed. ISBN: 0198662246. Searches: https://www.amazon.com/s?k=0198662246 https://www.google.com/search?q=isbn+0198662246 https://lccn.loc.gov/2004275127

Kissinger, H. A., Schmidt, E., & Huttenlocher, D. (2021). The Age of AI. Little, Brown and Company. ISBN: 978-0316273800. Searches: https://www.amazon.com/s?k=9780316273800 https://www.google.com/search?q=isbn+9780316273800 https://lccn.loc.gov/2021943914

Macphail, E. M. (1982). Brain and Intelligence in Vertebrates. Oxford. ISBN 0198545517. Searches: https://www.amazon.com/s?k=0198545517 https://www.google.com/search?q=isbn+0198545517 https://lccn.loc.gov/82166301

Retraice (2020/11/02). Re10: Living to Guess Another Day. retraice.com. https://www.retraice.com/segments/re10Retrieved 2nd Nov. 2020.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Althoff (2017) pp. 67, 76.

^2 https://realpython.com/python-dicts/

^3 Althoff (2017) p. 18.

^4 Butterfield & Ngondi (2016) p. 229.

^5 Butterfield & Ngondi (2016) p. 229

^6 Russell & Norvig (2020) p. 48.

^7 Cf. Kissinger et al. (2021) p. 58 on the difference between classical and machine learning algorithms. NOTE AND CORRECTION: I've been saying AI programs `improve on imprecise results', an idea I got from Kissinger et al. (2021) p. 58, but really they say that about machine learning algorithms, vs. classical algorithms. Can this be generalized to all AI code? Probably not. Especially if Barlow (2004) "says intelligence is about perception, not learning" (Retraice (2020/11/02)) and Macphail (1982) says "[there is no apparent correlation between learning and intelligence in many species]" (Retraice (2020/11/02)), quoting Retraice Notes, not Macphail or Barlow.

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Re65: TABLE-DRIVEN-AGENT Part 1 (AIMA4e p. 48)

retraice.com

A basic agent program in both AIMA4e pseudocode and Python.

Air date: Tuesday, 29th Nov. 2022, 11:00 PM Eastern/US.

Agents

Examples of kinds of agents in AIMA4e (pp. 48-58): table-driven, reflex, model-based reflex, goal-based, utility-based, learning.

We should also remember that `intelligent agents' of these kinds need not necessarily be only the computer+software kind. They might occur in nature, or in other architectures (see below).

TABLE-DRIVEN-AGENT

If this is your first exposure to this stuff, relax. Give it time and you'll get familiar with it. Don't try to learn it faster than your brain can go. From Russell & Norvig (2020) p. 48 and the associated GitHub repository: ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

PIC TABLE-DRIVEN-AGENT in pseudocode and Python. Highlighted are inputs and output. ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

http://aima.cs.berkeley.edu/algorithms.pdf

https://github.com/aimacode/aima-python/blob/master/agents4e.py

Program functions, agent functions, agent programs

*CORRECTION*: The `agent function' is not `the smart part' of the agent program. "The agent function is an abstract mathematical description.... that maps any percept sequence to an action"; "the agent program is a concrete implementation [of the agent function], running within some physical system." (100!black!//Russell & Norvig (2020) pp. 37-37) Function: math. Program: code. [Correction at Jan 2nd, 2023.]

Here is a first-pass attempt at explaining the key ideas: * program (programming) function: a piece of software that takes an input and returns an output. * agent program: an AIMA4e technical term that stands for the whole, mostly dumb, computer program to be run, within which is the smart part called the `agent function'. * agent function: an AIMA4e technical term that stands for the part of the agent program that makes the agent behave intelligently--in the TABLE-DRIVEN-AGENT, it's the `table' file in memory, which is a Python dictionary (`associative array' data structure), which we'll provide to the dumb program in advance so it can `look-up' the smart action that corresponds to the latest percept sequence after receiving a new percept via its architecture (in this case, us typing its name and an argument such as `red' on the command line and hitting `return'). * agent = architecture (hardware) + program (software) (p. 47). * architecture (hardware): makes percepts from sensors available to program, runs program, feeds programs action choices to actuators (p. 47). * program = control structures + memory + agent function. * memory (`persistent'): representations, e.g. atomic, factored, structured (p. 58).

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References

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

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Re64: Math, Code and Deadlines (AIMA4e planning)

retraice.com

Coming to terms with the hard things first.

Air date: Monday, 28th Nov. 2022, 11:00 PM Eastern/US.

How long do we have? Deadlines

We're going to do a deep-dive on Russell & Norvig (2020). Mastery should be of a discipline, not a particular book, and will require returning again and again to AIMA4e as real-world tests reveal more and more of its utility for that purpose. How long to deep-dive? * A year? No, six months, with prereq month, December, for the hard stuff (math, code); * Reality isn't going to wait; recall the deadline problem;^1 and we should probably never make a plan longer than six months.

To do what? Calibration by tests

Tests can be self-imposed or world-imposed, and toward calibration (Do I know what I think I know?) or toward gatekeeping (school, graduation, etc.).

Our self-imposed tests... * competence as tested by the online exercises, and comprehension of the technical details in the text itself; * a survey of the whole field of AI is the larger objective, as tested by published paper comprehension.

Later, world-imposed tests... * real-world use tests will be toward mastery; * mastery of a subfield (specialization) will be tested by action in the world--`play', `games', wins and losses.

And Retraice? Learning vs. teaching

I'll be learning, not teaching. "What's going on out there" is computer control. I'll be like a remote correspondent(?), or beat reporter(?) on that current history. But we shouldn't decide the best form Retraice can take, we should be open-minded and discover it.

What strategy? Hard first: Get out of English quickly

We're not expecting difficulty with the English in the book. It's the code and the math.

Code: ABC: always be coding. Math: resources at the ready (the best books). * Code: TABLE-DRIVEN-AGENT, p. 48; * Math: fate of the table agent, p. 48; * Environments (Linux, Python, LaTeX, Github, etc.) for: + study (see, and report back); + output (do, and report back).

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References

Retraice (2022/10/16). Re20: The Deadline Problem. retraice.com. https://www.retraice.com/segments/re20 Retrieved 17th Oct. 2022.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Retraice (2022/10/16).

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Re63: Seventeen Reasons to Learn AI

retraice.com

A reminder for when motivation is lacking.

Air date: Sunday, 27th Nov. 2022, 11:00 PM Eastern/US.

Human action

Learning AI sometimes requires high motivation. The economist Ludwig von Mises gives three prerequisites for human action:^1 1. Uneasiness (with the present); 2. An image (of a desirable future); 3. The belief (expectation) that action has the power to yield the image. AI is becoming more necessary to achieve desirable futures, because enough humans have been picking low-hanging fruit for enough time that most of the fruit is now high-hanging, where we can't reach without AI.

The following are some causes for uneasiness in all of us, young and old.

Life

1. The practical: livelihood, money, wealth, destitution.^2

2. The proximal: what observations by individuals happen to reveal the importance of AI. In the case of Retraice it was the need for AI to work seriously on our hypotheses about what's going on out there, and then noticing the computer control game and the differences between outsiders and players.

3. The principled: whatever larger purpose or value one perceives in life. In the case of the author, a `local enclave'^3 of good stuff like humans, farms, cities and engineering against a universe that's generally trying to be crap.^4

Biology

Things fall apart.^5

4. You might reverse aging, turning back time to get second chances.^6 (Though, is death adaptive in terms of evolution and reproduction?).

5. You might repair injuries.^7

6. You might save your own life.^8

7. We need it to stabilize and repair the planet.

Fear

8. FOMO: fear of missing out.

9. Young people always use cutting edge tech (and old people usually don't), so your world is going to be perpetually shaped by young people who know AI.^9

10. FUD: fear, uncertainty, doubt.

11. Criminals always use cutting edge tech. There's arguably more pressure on them to do so, or at least less inhibition in them. Note that criminals and bad guys are not perfectly overlapping groups.

12. The bad guys are learning it, and they'll be able to use it to prevent you from learning it. Consider China.^10

13. You or your loved ones may be enslaved by those who learn it. Perhaps a good definition of power is `the relative capacity to protect loved ones'.

14. Collapse into technological tyrannical, even at the hands of benevolent overlords.

15. Whatever we cause to happen by AI is probably going to happen to you, in your lifetime. You're not going to understand what's happening to you, what's being done to you.^11

Love

16. If you've ever been in love, you want more.

17. If you've never been in love, you want some.

AI makes more of just about anything possible--if not love itself, than the many things that make love more likely. Though the same is true of bad things. "In nature, necessity and desire are always linked."^12

_

References

Ben-Naim, A. (2008). A Farewell To Entropy: Statistical Thermodynamics Based On Information. World Scientific. ISBN: 978-9812707079. Searches: https://www.amazon.com/s?k=9789812707079 https://www.google.com/search?q=isbn+9789812707079 https://lccn.loc.gov/

Brockman, J. (Ed.) (2019). Possible Minds: Twenty-Five Ways of Looking at AI. Penguin. ISBN: 978-0525557999. Searches: https://www.amazon.com/s?k=978-0525557999 https://www.google.com/search?q=isbn+978-0525557999 https://lccn.loc.gov/2018032888

Gawande, A. (2014). Being Mortal: Medicine and What Matters in the End. Metropolitan Books. ISBN: 978-0805095159. Searches: https://www.amazon.com/s?k=9780805095159 https://www.google.com/search?q=isbn+9780805095159 https://catalog.loc.gov/vwebv/search?searchArg=9780805095159

Knowles, E. (Ed.) (1999). The Oxford Dictionary of Quotations. Oxford University Press, 5th ed. ISBN: 0198601735. Searches: https://www.amazon.com/s?k=0198601735 https://www.google.com/search?q=isbn+0198601735 https://lccn.loc.gov/99012096

Koch, C. G. (2007). The Science of Success. Wiley. ISBN: 978-0470139882. Searches: https://www.amazon.com/s?k=9780470139882 https://www.google.com/search?q=isbn+9780470139882 https://lccn.loc.gov/2007295977

Retraice (2022/10/16). Re20: The Deadline Problem. retraice.com. https://www.retraice.com/segments/re20 Retrieved 17th Oct. 2022.

Retraice (2022/11/03). Re39: News -- Space, Technology, Death. retraice.com. https://www.retraice.com/segments/re39 Retrieved 6th Nov. 2022.

Retraice (2022/11/05). Re41: News -- Betterment, Intelligence, Darkness. retraice.com. https://www.retraice.com/segments/re41 Retrieved 8th Nov. 2022.

Retraice (2022/11/06). Re42: News -- Wealth, Wildcards, Computers. retraice.com. https://www.retraice.com/segments/re42 Retrieved 8th Nov. 2022.

Retraice (2022/11/13). Re49: China is Not F-ing Around. retraice.com. https://www.retraice.com/segments/re49 Retrieved 15th Nov. 2022.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Sawyer, W. W. (1955). Prelude to Mathematics. Dover Publications, 1982 revised ed. ISBN: 0486244016. Searches: https://www.amazon.com/s?k=0486244016 https://www.google.com/search?q=isbn+0486244016 https://lccn.loc.gov/82004567

Schneier, B. (2015). Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. W. W. Norton. ISBN: 9780393244816. Searches: https://www.amazon.com/s?k=9780393244816 https://www.google.com/search?q=isbn+9780393244816 https://lccn.loc.gov/2014048365

Strittmatter, K. (2018). We Have Been Harmonized: Life in China's Surveillance State. Custom House, revised, updated ed. ISBN: 978-0063027305. Published in Germany, 2018. This paperback edition 2021. Searches: https://www.amazon.com/s?k=9780063027305 https://www.google.com/search?q=isbn+9780063027305 https://lccn.loc.gov/2020288922

von Mises, L. (1949). Human Action: A Treatise on Economics. Ludwig von Mises Institute, 2010 reprint ed. ISBN: 978-1610161459. Searches: https://www.amazon.com/s?k=9781610161459 https://www.google.com/search?q=isbn+9781610161459 https://lccn.loc.gov/50002445

Wiener, N. (1954). The Human Use Of Human Beings: Cybernetics and Society. Da Capo, 2nd ed. ISBN: 978-0306803208. This 1954 ed. missing `The Voices of Rigidity' chapter of the original 1950 ed. See 1st ed.: https://archive.org/details/humanuseofhumanb00wien/page/n11/mode/2up. See also Brockman (2019) p. xviii. Searches for the 2nd ed.: https://www.amazon.com/s?k=9780306803208 https://www.google.com/search?q=isbn+9780306803208 https://lccn.loc.gov/87037102

Footnotes

^1 von Mises (1949) pp. 13-14. See also Koch (2007) p. 144.

^2 See Russell & Norvig (2020) p. 1 on the plenty of opportunities in AI.

^3 Wiener (1954) pp. 12, 26.

^4 On entropy and information, see Ben-Naim (2008) p. xvii.

^5 Gawande (2014) chpt. 2.

^6 Cf. Retraice (2022/11/03) on "making cells young again".

^7 See Retraice (2022/11/05) on "The Lego blocks of life" and Retraice (2022/11/06) on "protein folding".

^8 See Retraice (2022/10/16) on "the deadline problem".

^9 Planck: "A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.". From his Scientific Autobiography (1949), quoted in Knowles (1999) p. 577.

^10 Retraice (2022/11/13), Horesh: "could soon find themselves entering a vise from which they never escape".

^11 This is already happening in the domain of computer control and, to a significant extent, AI itself: hacking, social media and search algorithms, big data profiling and market segmentation, etc. Most people are not aware of the arsenals of machines running software that are deployed by companies and governments to monitor and change their behavior. See Strittmatter (2018); Schneier (2015).

^12 Sawyer (1955) p. 12.

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AIMA4e Annotations

A companion to the great white brick.

As of November 27, 2022

(Start date: November 21, 2022.)

[1]retraice.com

Version notes: [2]Retraice ([3]2022/11/21) (Re57), first draft, covered Preface, Sections I, II; [4]Retraice ([5]2022/11/22) (Re58), no footnotes, covered Sections III, IV; [6]Retraice ([7]2022/11/22) (Re58) again, moved some notes from Re57 and Re58 notes to footnotes here; [8]Retraice ([9]2022/11/23) (Re59), covered Sections V, VI, VII; [10]Retraice ([11]2022/11/24) (Re60), covered Appendix A; [12]Retraice ([13]2022/11/25) (Re61), covered Appendix B, added quotation marks to chapter titles that aren't abbreviated or paraphrased; [14]Retraice ([15]2022/11/26) (Re62), added synopsis.

SYNOPSIS

Not everything in this synopsis is actually mentioned in AIMA4e, but 99% of it is.

PREFACE: Our phenomenon is intelligent agents--their `agent functions', goals, uncertainty, learning, and performance behavior. Our science, which includes building computing-machine agents, is called `artificial intelligence'. The goals we put into computing-machine agents should be fundamentally uncertain, to avoid the extreme unintended consequences of their growing power. If goals are provided without uncertainty, the machines' behavior will not be compatible with human life.[16]^1

1 INTELLIGENCE: Agents have structure and do good or bad performance behavior toward goals in environments.

2 SOLVING: Agents pursue goals by searching for solutions to goal-related problems, which can have degrees of constraints, in environments that sometimes contain other agents.

3 THINKING (KNOWLEDGE, REASONING AND PLANNING): Agents use internal representations[17]^2 of the world and how it works (logics, task nets, probability nets, Markov models, neural nets), before they act, to improve performance behavior toward goals.

4 UNCERTAIN THINKING: Most environments cannot be known completely, and so have a quantifiable element of uncertainty; doing pure logic in such environments exhausts resources and presumes knowledge which is unavailable; Bayes nets can be used in uncertain environments to simplify causal relationships of the past, present and future, shifting thinking from what's possible to what's probable; programming languages that incorporate uncertainty and probability can be made from pure logic or from traditional programming languages; no matter what the environment or representations of it, agents must have rules for making decisions, either singly or sequentially; other agents, in the same environment, face the same challenges, and the relationships between agents make all the difference to the outcomes.

5 LEARNING: Observations of the environment can improve behavior and make future environments `better'[18]^3 for the agent; this happens when the representations (models) built based on observations, are `good',[19]^4 and are usable as software to solve problems that obstruct goals; multiple observation techniques, including teacher-provided information, and information provided by reward and punishment of trial behavior, can yield good, usable representations, especially if they account for uncertainty by forming and testing hypotheses about evidence-data (instantiations of `random variables' of the relevant part of the world [domain]); hypotheses can be represented by "complex algebraic circuits with tunable connection strengths" (deep neural nets), which are somewhat analogous to animal brain tissue.

6 INTERACTING: Computing-machine agents can be connected to networks, databases, sensors and effectors, enabling them to learn from human language and the physical world outside themselves, for purposes of achieving goals; human language is complex; `language models' describe the probability of a string in a given language, and deep neural networks are the current best tool for building them; photons and other particles and waves (quantum field phenomena) can provide abundant information about environments to agents, via cameras and other sensors; electro-mechanical devices enable agent-movement in and of the physical world.

7 CONCLUSIONS: The hardest part of dealing with highly intelligent agents is knowing who, what and when to trust. It is not clear that humans will change sufficiently, or quickly enough, to adapt to the environments of the future which will contain highly intelligence computing-machine agents. Similarly, it is not clear that humans can overcome all present and future existential threats without such agents helping them.[20]^5

PREFACE

* The phenomenon: intelligent agents[21]^6 * The discipline: artificial intelligence,[22]^7 "the study of agents that receive percepts from the environment and perform actions." (vii)

* Aspects of the phenomenon: + Agent function: "Each ...agent implements a function that maps percept sequences to actions" (vii) o Ways to represent agent functions include: "reactive agents, real-time planners, decision-theoretic systems, and deep learning systems." (vii) + Learning o "a construction method for competent systems" (viii) o "a way of extending the reach of the designer into unknown environments." (viii) + Goals o Robotics and vision: # "not ...independently defined problems" # "[things] in the service of achieving goals."

I INTELLIGENCE --"Artificial Intelligence"

1 Introduction

Definitions, foundations, history, philosophy, state of the art, risks-benefits.

2 Agents

Environments, `good' behavior, agent structure and types.

II SOLVING--"Problem-solving"

3 Searching: Looking ahead to find a sequence

Algorithms, strategies, informed/heuristic[23]^8 strategies.

4 Complex Environments: More realistic environments

Local search, optimization, continuous spaces, nondeterministic actions, partially observable env.s, online search and unknown env.s.

5 Adversarial Games: Other agents competing against us

Theory, optimal decisions, alpha-beta tree search, Monte Carlo tree search, stochastic g.s, partially observable g.s, limitations.

6 Constraints: States as domains, solutions as allowable combinations of states

Constraint propagation, inference, backtracking search, local search, structure of problems

III THINKING--"Knowledge, reasoning, and planning"

7 "Logical Agents": Forming representations and reasoning before acting

Knowledge-based agents; representing[24]^9 worlds; logic, world models and `possible worlds';[25]^10 logic without objects.

8 "First-Order Logic": A formal language for objects and their relations

`Ontological commitment' (what is assumed about reality); syntax, semantics; knowledge engineering (building formal representations of important[26]^11 objects and relations in a domain).

9 First-Order Inference: Reasoning about objects and their relations

Algorithms to answer any 1st-order logic question.

10 "Knowledge Representation": Representing the real world for problem solving

What content to put into a knowledge base.

Knowledge representation languages and their uses (315): * First-order logic: reasoning about a world of objects and relations; * Hierarchical task networks: for reasoning about plans (chpt. 11); * Bayesian networks: for reasoning with uncertainty (chpt. 13); * Markov models: for reasoning over time (chpt. 17); * Deep neural networks: for reasoning about images, sounds, other data (chpt. 21).

11 "Automated Planning": Hierarchical task networks

Planning for spacecraft, factories, military campaigns; representing actions and states; efficient algorithms and heuristics.

IV UNCERTAINTY--"Uncertain knowledge and reasoning"

12 "Quantifying Uncertainty": An answer to the laziness and ignorance that kill formal logic

Causes of uncertainty are environment types (partially observable,[27]^12 nondeterministic, adversarial[28]^13 ); belief state grows big and unlikely fast (384); agents still need a way to act; absolute certainty is impossible;[29]^14 it comes down to importance, likelihood and degree of success (385-386).

Logic fails because laziness and ignorance; probability theory solves the qualification problem by summarizing the uncertainty.[30]^15 * Laziness: too much work to list everything, or use such a list; * Ignorance: (theoretical) there are no complete theories; (practical) we can never run all the tests.

13 "Probabilistic Reasoning" [big]: Bayesian networks

For reasoning with uncertainty by representing causal independence (398) and conditional independence (401) relationships to simplify probabilistic representations of the world.

14 "Probabilistic Reasoning Over Time": Comprehending the uncertain past, present and future

Belief state[31]^16 plus transition model yields prediction (chpt 4, 7, 11); percepts and sensor model yield updated belief state; add probability theory to switch from possible states to probable states.[32]^17

15 "Probabilistic Programming": Universal formal languages to represent any computable probability model, and they come with algorithms

Using formal logic and traditional programming languages to represent probabilistic information.

16 "Making Simple Decisions": Agents getting what they want in an uncertain world--as much as possible, on average

Beliefs, desires; utility theory; utility functions; decision networks; the value of information (547);[33]^18 this chapter is concerned with one-shot or episodic decisions problems (as opposed to sequential) (cf. 562, below).

17 "Making Complex Decisions": What to do today given decisions to be made tomorrow

Sequential decision problems (as opposed to one-shot episodic, cf. above): the agent's utility depends on a sequence of decisions in stochastic (explicitly probabilistic (45)) and partially observable environments. Markov models (563; cf. 463) for reasoning over time (chpt. 17).

18 "Multiagent Decision Making" [big]: When there's more than one agent in the environment

The nature of such environments and the strategies for problem-solving depend on the relationships between agents: non-cooperative and cooperative game theory; collective decision-making.

V LEARNING--"Machine learning"

19 "Learning From Examples" [big]: Improving behavior by observing the present (past?) and predicting the future

Learning is improving performance (behavior) after making observations.[34]^19

If the agent is a computer: Machine learning: "a computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems." (651)

Subsections: * supervised learning; * learning decision trees; * model selection and optimization; * theory of learning; * linear regression (finding the best-fit line, i.e. predicting `future' [dependent] values based on plotting `past' [independent] values), classification;[35]^20 * nonparametric models (which retain all the examples, aka `instance-based' or `memory-based' learning, which is more true to large datasets [scalable?] than parametric, which summarize, and then discard, training data in fixed numbers of parameters), * ensemble learning (using multiple hypotheses instead of one, and averaging or voting--`base' models are combined into an `ensemble' model); * ML system development, the practice (software engineering and design patters in ML ops).

20 "Learning Probabilistic Models": View `learning' as "uncertain reasoning from observations" and model the world accordingly

Agents can't use probability and decision theories until they learn them from experience: treat learning itself as an inference process in a probabilistic world. Use Bayesian networks. Key concepts: data and hypotheses. "Here, the data are evidence ...instantiations of some or all of the random variables describing the domain."[36]^21

21 "Deep Learning": represent hypotheses as "complex algebraic circuits with tunable connection strengths"

The circuits are orginzed into layers, a multi-step computation path. Ideal for recognizing, translating and generating images (including objects in images) and speech; `neural networks'.

From chpt. 10 on knowledge rep. languages, above notes: "deep neural networks: for reasoning about images, sounds, other data."

Think: gradient descent, back-propagation, convolutional neural networks.

22 "Reinforcement Learning": Learning from experiences of reward and punishment instead of correct examples from a supervisor

Passive and active RL., Q-learning, apprenticeships and inverse RL.

Cf. Reward is Enough, May 2021: [37]https://www.deepmind.com/publications/reward-is-enough

VI INTERACTING--"Communicating, perceiving, and acting"

23 "Natural Language Processing": Communicating with humans and learning from what they've written

Language model: "a probability distribution describing the likelihood of any string." (824)

N-grams, grammar, syntax, semantics, parsing, vagueness, ambiguity, quantification.

24 Deep Learning NLP: Using neural nets on natural language to effectively handle the complexity

"[R]epresenting words as points in a high-dimensional space." RNNs for "long-distance context."

Cf. Attention Is All You Need, 2017: [38]https://arxiv.org/abs/1706.03762 and AIMA4e p. 868, transformer architecture, self-attention.

25 "Computer Vision": Connecting AI to cameras

Photons provide a lot of valuable information to agents--too much information.

Surveillance cameras--good and bad; cars. Lots of machines do better if they can see.

From the Preface: Robotics and vision: "not ...independently defined problems"..."[things] in the service of achieving goals."

26 "Robotics": Connecting AI to sensors, effectors and actuators

To enable movement in-and of--the physical world. Cars, spacecraft, surgeons, submarines, delivery bots.

From the Preface: Robotics and vision: "not ...independently defined problems"..."[things] in the service of achieving goals."

VII CONCLUSIONS--"Conclusions"

27 "Philosophy, Ethics, and Safety of AI": What is AI? What should we do with it? What might it do with us?

Trust--of systems, humans, ourselves, each other.

The human use of human beings. Usefulness of human beings at all?

Medicine. War.

28 "The Future of AI": Our tools will improve dramatically; our ends might remain the same.

Our preferences, our tools, our architectures. They're ours, for now.

Minimize the negative impacts, don't maximize the positive?

A: MATH--"Appendix A: Mathematical Background"

A.1 [SOLVING per §II]: "Complexity Analysis and O() Notation": Problem and algorithm analysis (computer science math)

Asymptotic and worst-case analysis of algorithms:

Approximately predicting the performance (and efficiency) of algorithms based on their steps in worst-case (or best or average) and infinite-case (asymptotic) input scenarios, in order to avoid actually implementing them, and to enable comparison of algorithms.[39]^22

Abstract over the input, and then the implementation, to find the key factors (string length; lines of code) that make the space/time difference. Ignore constants, usually; focus on the key variables.

Complexity analysis of problems:

Polynomial time O(n^k) problems, class P.

Non-polynomial time problems.

Nondeterministic polynomial problems: class NP. A problem with some algorithm that can guess and check a solution in polynomial time.

A.2 [THINKING per §III]: "Vectors, Matrices, and Linear Algebra": Line equation probing (`unknowns' math)

A vector is a pile of numbers (or unknowns or variables), a matrix is a pile of piles of numbers; some of the questions we can ask are `linear problems'[40]^23 (think prediction, interpolation, extrapolation), and algebra (finding unknowns by repairing [or completion] and balancing) on these things is `linear algebra'.

Vectors: Ordered sequences of values--represent something in the real world as just a set of values measuring specific aspects of that thing.[41]^24

Linear algebra: Doing algebra (finding unknowns by repairing [or completion] and balancing) on systems of equations of lines in planes instead of single equations and equations of points on lines (algebra). Think: finding line or plane intersections or bounded regions (based on inequalities instead of equations),[42]^25 and changing lines without affecting intersections[43]^26 --that sort of thing.

Thinking about higher dimensional objects: left-right x, up-down y, forward-backward z, wrist-watch value (time) t, color spectrum p, texture q, weight r, etc.

A.3 [UNCERTAINTY per §IV] "Probability Distributions": Quantifying `probably' (uncertainty math)

Probability is a controversial concept.[44]^27

Experiments yield outcomes; a set of outcomes is an event; the set of all possible outcomes is the sample space.[45]^28

"A `probability' is a measure over a set of events...." A probability model: sample space plus the probability measure for each outcome.

Cf. `random variable' note above.

B: CODE--"Appendix B: Notes on Languages and Algorithms"

B.1 "Defining Languages with Backus-Naur Form (BNF)": Defining formal languages

Languages: * propositional logic; * first-order logic; * English;

Formal language: strings, symbols, infinite strings, grammar, Chomsky hierarchy (context free).

BNF elements: * Terminals: symbols / words; * Non terminals that categorize: NounPhrase; * Start symbol: Sentence (English) or Expr (math) or Program (computing); * Rewrite rules: Sentence -> Expr Operator Expr | (Expr) | Number.

Think compilers:[46]^29 source language to target language, first syntax then semantics, reconstruct source logic in target logic.

1. Syntax analysis module: tokenizing + parsing (rule matching);

2. Code generation module: data translation and command translation.

Think also: (universal?) generative grammar, generating new strings, chat bots, GPT-3, Deep Blue, AlphaCode, Turing test.

B.2 "Describing Algorithms with Pseudocode": Code formatting and conventions

* Persistent variables: global state, side effects, OOP vs functional programming; agents use persistent variables for memory; implementation in OOP vs FP languages; * Functions as variable values: f(9)=3; * Indentation is significant: it scopes control structures (loops and cobditionals), also functions; but objects? Not really. They use `persistent' variable as memory, which can be implemented as an object, or a `functional closure', cf braces and `end'; * Destructuring assignment notation; * Default values for parameters: y=0; * Yield: generates an element of sequence; * Loops: for, while, repeat until; * Lists notation; * Sets notation; * Arrays index starts at 1 as in math, not code.

__

References

Aleksandrov, A. D., Kolmogorov, A. N., & Lavrent'ev, M. A. (1969). Mathematics: Its Content, Methods and Meaning (3 Volumes in One). Dover, 1999 reprint ed. ISBN: 0486409163. Searches: [47]https://www.amazon.com/s?k=0486409163 [48]https://www.google.com/search?q=isbn+0486409163 [49]https://lccn.loc.gov/99033023

Anderson, R. F. V. (1986). Introduction to Linear Algebra. Holt, Rinehard and Winston. ISBN: 003921835X. Searches: [50]https://www.amazon.com/s?k=003921835X [51]https://www.google.com/search?q=isbn+003921835X

Bittinger, M. L., & Ellenbogen, D. J. (2006). Intermediate Algebra: Concepts and Applications. Addison-Wesley, 7th ed. ISBN: 0321233867. Searches: [52]https://www.amazon.com/s?k=0321233867 [53]https://www.google.com/search?q=isbn+0321233867 [54]https://lccn.loc.gov/2004062480

Bostrom, N., & Cirkovic, M. M. (Eds.) (2008). Global Catastrophic Risks. Oxford University Press. ISBN: 978-0199606504. Searches: [55]https://www.amazon.com/s?k=978-0199606504 [56]https://www.google.com/search?q=isbn+978-0199606504 [57]https://lccn.loc.gov/2008006539

Bulmer, M. G. (1967). Principles of Statistics. Dover, 1979 reprint ed. ISBN: 0486637603. Searches: [58]https://www.amazon.com/s?k=0486637603 [59]https://www.google.com/search?q=isbn+0486637603 [60]https://lccn.loc.gov/78072991

Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. ISBN: 978-0300209570. Searches: [61]https://www.amazon.com/s?k=9780300209570 [62]https://www.google.com/search?q=isbn+9780300209570 [63]https://lccn.loc.gov/2020947842

Frankfurt, H. G. (1988). The Importance of What We Care About. Cambridge. ISBN: 978-0521336116. Searches: [64]https://www.amazon.com/s?k=978-0521336116 [65]https://www.google.com/search?q=isbn+978-0521336116 [66]https://lccn.loc.gov/87026941

Gerrish, S. (2018). How Smart Machines Think. The MIT Press. ISBN: 978-0262038409. Searches: [67]https://www.amazon.com/s?k=9780262038409 [68]https://www.google.com/search?q=isbn+9780262038409 [69]https://lccn.loc.gov/2017059862

Good, I. J. (1965). Speculations concerning the first ultraintelligent machine. Advances in Computers, 6, 31-88. [70]https://exhibits.stanford.edu/feigenbaum/catalog/gz727rg3869 Retrieved 27th Oct. 2020.

Horwich, P. (1982). Probability and Evidence. Cambridge. First published 1982; first paperback 2011; this Cambridge Philosophy Classics edition 2016. ISBN: 978-1316507018. Searches: [71]https://www.amazon.com/s?k=978-1316507018 [72]https://www.google.com/search?q=isbn+978-1316507018 [73]https://lccn.loc.gov/2015049717

Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. ISBN: 978-0374533557. Searches: [74]https://www.amazon.com/s?k=978-0374533557 [75]https://www.google.com/search?q=isbn+978-0374533557 [76]https://lccn.loc.gov/2012533187

Keynes, J. M. (1920). A Treatise on Probability: The Connection Between Philosophy and the History of Science. Wildside Press. ISBN: 978-1434406965. Searches: [77]https://www.amazon.com/s?k=9781434406965 [78]https://www.google.com/search?q=isbn+9781434406965 [79]https://lccn.loc.gov/2004041359

Kramer, E. E. (1970). The Nature and Growth of Modern Mathematics. Hawthorn Books. No ISBN. Searches: [80]https://www.amazon.com/s?k=kramer+nature+and+growth+of+modern+mathematics [81]https://www.google.com/search?q=kramer+nature+and+growth+of+modern+mathematics [82]https://lccn.loc.gov/73091317

Larson, R., Hostetler, R. P., & Edwards, B. H. (2005). College Algebra: A Graphing Approach. Houghton Mifflin, 4th ed. ISBN: 0618394370. Searches: [83]https://www.amazon.com/s?k=0618394370 [84]https://www.google.com/search?q=isbn+0618394370 [85]https://lccn.loc.gov/00104769

Macphail, E. M. (1982). Brain and Intelligence in Vertebrates. Oxford. ISBN 0198545517. Searches: [86]https://www.amazon.com/s?k=0198545517 [87]https://www.google.com/search?q=isbn+0198545517 [88]https://lccn.loc.gov/82166301

Manber, U. (1989). Introduction to Algorithms: A Creative Approach. Addison-Wesley, 1st ed. ISBN: 0201120372. Searches: [89]https://www.amazon.com/s?k=0201120372 [90]https://www.google.com/search?q=isbn+0201120372 [91]https://lccn.loc.gov/88002186

Nisan, N., & Schocken, S. (2005). The Elements of Computing Systems: Building a Modern Computer from First Principles. MIT. ISBN: 978-0262640688. Searches: [92]https://www.amazon.com/s?k=978-0262640688 [93]https://www.google.com/search?q=isbn+978-0262640688 [94]https://lccn.loc.gov/2005042807

Okasha, S. (2002). Philosophy of Science: A Very Short Introduction. Oxford University Press. ISBN: 0192802836. Searches: [95]https://www.amazon.com/s?k=0192802836 [96]https://www.google.com/search?q=isbn+0192802836 [97]https://lccn.loc.gov/2002510456

Retraice (2020/09/07). Re1: Three Kinds of Intelligence. retraice.com. [98]https://www.retraice.com/segments/re1 Retrieved 22nd Sep. 2020.

Retraice (2020/11/02). Re10: Living to Guess Another Day. retraice.com. [99]https://www.retraice.com/segments/re10 Retrieved 2nd Nov. 2020.

Retraice (2020/11/10). Re13: The Care Factor. retraice.com. [100]https://www.retraice.com/segments/re13 Retrieved 10th Nov. 2020.

Retraice (2020/11/25). Re15: Trust and Sources. retraice.com. [101]https://www.retraice.com/segments/re15 Retrieved 28th Feb. 2022.

Retraice (2022/03/07). Re18: Plan of Attack. retraice.com. [102]https://www.retraice.com/segments/re18 Retrieved 25th Mar. 2022.

Retraice (2022/10/10). Re19: Nature Is Not F-ing Around. retraice.com. [103]https://www.retraice.com/segments/re19 Retrieved 12th Oct. 2022.

Retraice (2022/10/19). Re23: You Need a World Model. retraice.com. [104]https://www.retraice.com/segments/re23 Retrieved 20th Oct. 2022.

Retraice (2022/10/23). Re27: Now That's a World Model - WM4. retraice.com. [105]https://www.retraice.com/segments/re27 Retrieved 24th Oct. 2022.

Retraice (2022/10/24). Re28: What's Good? RTFM. retraice.com. [106]https://www.retraice.com/segments/re28 Retrieved 25th Oct. 2022.

Retraice (2022/10/31). Re36: Notes on Conspiracy. retraice.com. [107]https://www.retraice.com/segments/re36 Retrieved 4th Nov. 2022.

Retraice (2022/11/12). Re48: From Drugs to Mao to Money. retraice.com. [108]https://www.retraice.com/segments/re48 Retrieved 14th Nov. 2022.

Retraice (2022/11/16). Re52: Big Questions About AI. retraice.com. [109]https://www.retraice.com/segments/re52 Retrieved 17th Nov. 2022.

Retraice (2022/11/21). Re57: AI, Agents, Problem-solving, Searching, Environments, Games (AIMA4e chpts. 1-6). retraice.com. [110]https://www.retraice.com/segments/re57 Retrieved 22nd Nov. 2022.

Retraice (2022/11/22). Re58: Thinking and Uncertainty (AIMA4e chpts. 7-18). retraice.com. [111]https://www.retraice.com/segments/re58 Retrieved 23rd Nov. 2022.

Retraice (2022/11/23). Re59: Learning, Interacting, Conclusions (AIMA4e chpts. 19-28). retraice.com. [112]https://www.retraice.com/segments/re59 Retrieved 24th Nov. 2022.

Retraice (2022/11/24). Re60: Complexity, Linear Algebra, Probability (AIMA4e Appendix A). retraice.com. [113]https://www.retraice.com/segments/re60 Retrieved 25th Nov. 2022.

Retraice (2022/11/25). Re61: Formal Languages, Pseudocode (AIMA4e Appendix B). retraice.com. [114]https://www.retraice.com/segments/re61 Retrieved 26th Nov. 2022.

Retraice (2022/11/26). Re62: AIMA4e in 5 Minutes. retraice.com. [115]https://www.retraice.com/segments/re62 Retrieved 27th Nov. 2022.

Russell, B. (1948). Human Knowledge: Its Scope and Limits. Routledge. First published in 1948. This edition 1992. ISBN: 0415083028. Searches: [116]https://archive.org/search.php?query=Human%20Knowledge%3A%20Its%20Scope%20and%20Limits [117]https://www.amazon.com/s?k=0415083028 [118]https://www.google.com/search?q=isbn+0415083028 [119]https://lccn.loc.gov/94209784

Russell, B. (1959). My Philosophical Development. Wnwin Brothers. No ISBN. [120]https://archive.org/details/myphilosophicald0000russ/page/n11/mode/2up Retrieved 06 Jun. 2021. Searches: [121]https://www.amazon.com/s?k=Russell+My+Philosophical+Development [122]https://www.google.com/search?q=Russell+My+Philosophical+Development [123]https://lccn.loc.gov/59003496

Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking. ISBN: 978-0525558613. Searches: [124]https://www.amazon.com/s?k=978-0525558613 [125]https://www.google.com/search?q=isbn+978-0525558613 [126]https://lccn.loc.gov/2019029688

Thompson, K. (1984). Reflections on trusting trust. Communications of the ACM, 27(8), 761-763. Aug. 1984. [127]https://doi.org/10.1145/358198.358210 Also available at: [128]https://www.cs.cmu.edu/~rdriley/487/papers/Thompson_1984_ReflectionsonTrustingTrust.pdf Retrieved 4th Dec. 2020.

Vallee, J. (1979). Messengers of Deception: UFO Contacts and Cults. And/Or Press. ISBN: 0915904381. Different edition and searches: [129]https://archive.org/details/MessengersOfDeceptionUFOContactsAndCultsJacquesValle1979/mode/2up [130]https://www.amazon.com/s?k=0915904381 [131]https://www.google.com/search?q=isbn+0915904381 [132]https://catalog.loc.gov/vwebv/search?searchArg=0915904381

Yudkowsky, E. (2008). Artificial intelligence as a positive and negative factor in global risk. (pp. 308-345). In [133]Bostrom & Cirkovic ([134]2008).

References

1. https://retraice.com/ 2. XReSeg57 3. XReSeg57 4. XReSeg58 5. XReSeg58 6. XReSeg58 7. XReSeg58 8. XReSeg59 9. XReSeg59 10. XReSeg60 11. XReSeg60 12. XReSeg61 13. XReSeg61 14. XReSeg62 15. XReSeg62 16. fn1x0 17. fn2x0 18. fn3x0 19. fn4x0 20. fn5x0 21. fn6x0 22. fn7x0 23. fn8x0 24. fn9x0 25. fn10x0 26. fn11x0 27. fn12x0 28. fn13x0 29. fn14x0 30. fn15x0 31. fn16x0 32. fn17x0 33. fn18x0 34. fn19x0 35. fn20x0 36. fn21x0 37. https://www.deepmind.com/publications/reward-is-enough 38. https://arxiv.org/abs/1706.03762 39. fn22x0 40. fn23x0 41. fn24x0 42. fn25x0 43. fn26x0 44. fn27x0 45. fn28x0 46. fn29x0 47. https://www.amazon.com/s?k=0486409163 48. https://www.google.com/search?q=isbn+0486409163 49. https://lccn.loc.gov/99033023 50. https://www.amazon.com/s?k=003921835X 51. https://www.google.com/search?q=isbn+003921835X 52. https://www.amazon.com/s?k=0321233867 53. https://www.google.com/search?q=isbn+0321233867 54. https://lccn.loc.gov/2004062480 55. https://www.amazon.com/s?k=978-0199606504 56. https://www.google.com/search?q=isbn+978-0199606504 57. https://lccn.loc.gov/2008006539 58. https://www.amazon.com/s?k=0486637603 59. https://www.google.com/search?q=isbn+0486637603 60. https://lccn.loc.gov/78072991 61. https://www.amazon.com/s?k=9780300209570 62. https://www.google.com/search?q=isbn+9780300209570 63. https://lccn.loc.gov/2020947842 64. https://www.amazon.com/s?k=978-0521336116 65. https://www.google.com/search?q=isbn+978-0521336116 66. https://lccn.loc.gov/87026941 67. https://www.amazon.com/s?k=9780262038409 68. https://www.google.com/search?q=isbn+9780262038409 69. https://lccn.loc.gov/2017059862 70. https://exhibits.stanford.edu/feigenbaum/catalog/gz727rg3869 71. https://www.amazon.com/s?k=978-1316507018 72. https://www.google.com/search?q=isbn+978-1316507018 73. https://lccn.loc.gov/2015049717 74. https://www.amazon.com/s?k=978-0374533557 75. https://www.google.com/search?q=isbn+978-0374533557 76. https://lccn.loc.gov/2012533187 77. https://www.amazon.com/s?k=9781434406965 78. https://www.google.com/search?q=isbn+9781434406965 79. https://lccn.loc.gov/2004041359 80. https://www.amazon.com/s?k=kramer+nature+and+growth+of+modern+mathematics 81. https://www.google.com/search?q=kramer+nature+and+growth+of+modern+mathematics 82. https://lccn.loc.gov/73091317 83. https://www.amazon.com/s?k=0618394370 84. https://www.google.com/search?q=isbn+0618394370 85. https://lccn.loc.gov/00104769 86. https://www.amazon.com/s?k=0198545517 87. https://www.google.com/search?q=isbn+0198545517 88. https://lccn.loc.gov/82166301 89. https://www.amazon.com/s?k=0201120372 90. https://www.google.com/search?q=isbn+0201120372 91. https://lccn.loc.gov/88002186 92. https://www.amazon.com/s?k=978-0262640688 93. https://www.google.com/search?q=isbn+978-0262640688 94. https://lccn.loc.gov/2005042807 95. https://www.amazon.com/s?k=0192802836 96. https://www.google.com/search?q=isbn+0192802836 97. https://lccn.loc.gov/2002510456 98. https://www.retraice.com/segments/re1 99. https://www.retraice.com/segments/re10 100. https://www.retraice.com/segments/re13 101. https://www.retraice.com/segments/re15 102. https://www.retraice.com/segments/re18 103. https://www.retraice.com/segments/re19 104. https://www.retraice.com/segments/re23 105. https://www.retraice.com/segments/re27 106. https://www.retraice.com/segments/re28 107. https://www.retraice.com/segments/re36 108. https://www.retraice.com/segments/re48 109. https://www.retraice.com/segments/re52 110. https://www.retraice.com/segments/re57 111. https://www.retraice.com/segments/re58 112. https://www.retraice.com/segments/re59 113. https://www.retraice.com/segments/re60 114. https://www.retraice.com/segments/re61 115. https://www.retraice.com/segments/re62 116. https://archive.org/search.php?query=Human%20Knowledge%3A%20Its%20Scope%20and%20Limits 117. https://www.amazon.com/s?k=0415083028 118. https://www.google.com/search?q=isbn+0415083028 119. https://lccn.loc.gov/94209784 120. https://archive.org/details/myphilosophicald0000russ/page/n11/mode/2up 121. https://www.amazon.com/s?k=Russell+My+Philosophical+Development 122. https://www.google.com/search?q=Russell+My+Philosophical+Development 123. https://lccn.loc.gov/59003496 124. https://www.amazon.com/s?k=978-0525558613 125. https://www.google.com/search?q=isbn+978-0525558613 126. https://lccn.loc.gov/2019029688 127. https://doi.org/10.1145/358198.358210 128. https://www.cs.cmu.edu/~rdriley/487/papers/Thompson_1984_ReflectionsonTrustingTrust.pdf 129. https://archive.org/details/MessengersOfDeceptionUFOContactsAndCultsJacquesValle1979/mode/2up 130. https://www.amazon.com/s?k=0915904381 131. https://www.google.com/search?q=isbn+0915904381 132. https://catalog.loc.gov/vwebv/search?searchArg=0915904381 133. XGCR 134. XGCR

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Re62: AIMA4e in 5 Minutes

retraice.com

A synopsis of Artificial Intelligence: A Modern Approach, 4th ed., as covered by Re57-Re61.

Air date: Saturday, 26th Nov. 2022, 11:00 PM Eastern/US.

Day 6: review

The latest version of our notes on AIMA4e (Russell & Norvig (2020)) will be at: https://aima4e.retraice.com.

Older versions will be preserved in the Retraice Notes feed, https://notes.retraice.com, or at the page for this segment, https://www.retraice.com/segments/re62.

Synopsis of AIMA4e

See https://aima4e.retraice.com.

_

References

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

(The below text version of the notes is for search purposes and convenience. See the PDF version for proper formatting such as bold, italics, etc., and graphics where applicable. Copyright: 2022 Retraice, Inc.)

AIMA4e Annotations

A companion to the great white brick.

As of November 26, 2022

(Start date: November 21, 2022.)

[1]retraice.com

Version notes: [2]Retraice ([3]2022/11/21) (Re57), first draft, covered Preface, Sections I, II; [4]Retraice ([5]2022/11/22) (Re58), no footnotes, covered Sections III, IV; [6]Retraice ([7]2022/11/22) (Re58) again, moved some notes from Re57 and Re58 notes to footnotes here; [8]Retraice ([9]2022/11/23) (Re59), covered Sections V, VI, VII; [10]Retraice ([11]2022/11/24) (Re60), covered Appendix A; [12]Retraice ([13]2022/11/25) (Re61), covered Appendix B.

PREFACE

* The phenomenon: intelligent agents[14]^1 * The discipline: artificial intelligence,[15]^2 "the study of agents that receive percepts from the environment and perform actions." (vii)

* Aspects of the phenomenon: + Agent function: "Each ...agent implements a function that maps percept sequences to actions" (vii) o Ways to represent agent functions include: "reactive agents, real-time planners, decision-theoretic systems, and deep learning systems." (vii) + Learning o "a construction method for competent systems" (viii) o "a way of extending the reach of the designer into unknown environments." (viii) + Goals o Robotics and vision: # "not ...independently defined problems" # "[things] in the service of achieving goals."

I INTELLIGENCE --"Artificial Intelligence"

1 Introduction

Definitions, foundations, history, philosophy, state of the art, risks-benefits.

2 Agents

Environments, `good' behavior, agent structure and types.

II SOLVING--"Problem-solving"

3 Searching: Looking ahead to find a sequence

Algorithms, strategies, informed/heuristic[16]^3 strategies.

4 Complex Environments: More realistic environments

Local search, optimization, continuous spaces, nondeterministic actions, partially observable env.s, online search and unknown env.s.

5 Adversarial Games: Other agents competing against us

Theory, optimal decisions, alpha-beta tree search, Monte Carlo tree search, stochastic g.s, partially observable g.s, limitations.

6 Constraints: States as domains, solutions as allowable combinations of states

Constraint propagation, inference, backtracking search, local search, structure of problems

III THINKING--"Knowledge, reasoning, and planning"

7 "Logical Agents": Forming representations and reasoning before acting

Knowledge-based agents; representing[17]^4 worlds; logic, world models and `possible worlds';[18]^5 logic without objects.

8 "First-Order Logic": A formal language for objects and their relations

`Ontological commitment' (what is assumed about reality); syntax, semantics; knowledge engineering (building formal representations of important[19]^6 objects and relations in a domain).

9 First-Order Inference: Reasoning about objects and their relations

Algorithms to answer any 1st-order logic question.

10 "Knowledge Representation": Representing the real world for problem solving

What content to put into a knowledge base.

Knowledge representation languages and their uses (315): * First-order logic: reasoning about a world of objects and relations; * Hierarchical task networks: for reasoning about plans (chpt. 11); * Bayesian networks: for reasoning with uncertainty (chpt. 13); * Markov models: for reasoning over time (chpt. 17); * Deep neural networks: for reasoning about images, sounds, other data (chpt. 21).

11 "Automated Planning": Hierarchical task networks

Planning for spacecraft, factories, military campaigns; representing actions and states; efficient algorithms and heuristics.

IV UNCERTAINTY--"Uncertain knowledge and reasoning"

12 "Quantifying Uncertainty": An answer to the laziness and ignorance that kill formal logic

Causes of uncertainty are environment types (partially observable,[20]^7 nondeterministic, adversarial[21]^8 ); belief state grows big and unlikely fast (384); agents still need a way to act; absolute certainty is impossible;[22]^9 it comes down to importance, likelihood and degree of success (385-386).

Logic fails because laziness and ignorance; probability theory solves the qualification problem by summarizing the uncertainty.[23]^10 * Laziness: too much work to list everything, or use such a list; * Ignorance: (theoretical) there are no complete theories; (practical) we can never run all the tests.

13 "Probabilistic Reasoning" [big]: Bayesian networks

For reasoning with uncertainty by representing causal independence (398) and conditional independence (401) relationships to simplify probabilistic representations of the world.

14 "Probabilistic Reasoning Over Time": Comprehending the uncertain past, present and future

Belief state[24]^11 plus transition model yields prediction (chpt 4, 7, 11); percepts and sensor model yield updated belief state; add probability theory to switch from possible states to probable states.[25]^12

15 "Probabilistic Programming": Universal formal languages to represent any computable probability model, and they come with algorithms

Using formal logic and traditional programming languages to represent probabilistic information.

16 "Making Simple Decisions": Agents getting what they want in an uncertain world--as much as possible, on average

Beliefs, desires; utility theory; utility functions; decision networks; the value of information (547);[26]^13 this chapter is concerned with one-shot or episodic decisions problems (as opposed to sequential) (cf. 562, below).

17 "Making Complex Decisions": What to do today given decisions to be made tomorrow

Sequential decision problems (as opposed to one-shot episodic, cf. above): the agent's utility depends on a sequence of decisions in stochastic (explicitly probabilistic (45)) and partially observable environments. Markov models (563; cf. 463) for reasoning over time (chpt. 17).

18 "Multiagent Decision Making" [big]: When there's more than one agent in the environment

The nature of such environments and the strategies for problem-solving depend on the relationships between agents: non-cooperative and cooperative game theory; collective decision-making.

V LEARNING--"Machine learning"

19 "Learning From Examples" [big]: Improving behavior by observing the present (past?) and predicting the future

Learning is improving performance (behavior) after making observations.[27]^14

If the agent is a computer: Machine learning: "a computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems." (651)

Subsections: * supervised learning; * learning decision trees; * model selection and optimization; * theory of learning; * linear regression (finding the best-fit line, i.e. predicting `future' [dependent] values based on plotting `past' [independent] values), classification;[28]^15 * nonparametric models (which retain all the examples, aka `instance-based' or `memory-based' learning, which is more true to large datasets [scalable?] than parametric, which summarize, and then discard, training data in fixed numbers of parameters), * ensemble learning (using multiple hypotheses instead of one, and averaging or voting--`base' models are combined into an `ensemble' model); * ML system development, the practice (software engineering and design patters in ML ops).

20 "Learning Probabilistic Models": View `learning' as "uncertain reasoning from observations" and model the world accordingly

Agents can't use probability and decision theories until they learn them from experience: treat learning itself as an inference process in a probabilistic world. Use Bayesian networks. Key concepts: data and hypotheses. "Here, the data are evidence ...instantiations of some or all of the random variables describing the domain."[29]^16

21 "Deep Learning": represent hypotheses as "complex algebraic circuits with tunable connection strengths"

The circuits are orginzed into layers, a multi-step computation path. Ideal for recognizing, translating and generating images (including objects in images) and speech; `neural networks'.

From chpt. 10 on knowledge rep. languages, above notes: "deep neural networks: for reasoning about images, sounds, other data."

Think: gradient descent, back-propagation, convolutional neural networks.

22 "Reinforcement Learning": Learning from experiences of reward and punishment instead of correct examples from a supervisor

Passive and active RL., Q-learning, apprenticeships and inverse RL.

Cf. Reward is Enough, May 2021: [30]https://www.deepmind.com/publications/reward-is-enough

VI INTERACTING--"Communicating, perceiving, and acting"

23 "Natural Language Processing": Communicating with humans and learning from what they've written

Language model: "a probability distribution describing the likelihood of any string." (824)

N-grams, grammar, syntax, semantics, parsing, vagueness, ambiguity, quantification.

24 Deep Learning NLP: Using neural nets on natural language to effectively handle the complexity

"[R]epresenting words as points in a high-dimensional space." RNNs for "long-distance context."

Cf. Attention Is All You Need, 2017: [31]https://arxiv.org/abs/1706.03762 and AIMA4e p. 868, transformer architecture, self-attention.

25 "Computer Vision": Connecting AI to cameras

Photons provide a lot of valuable information to agents--too much information.

Surveillance cameras--good and bad; cars. Lots of machines do better if they can see.

From the Preface: Robotics and vision: "not ...independently defined problems"..."[things] in the service of achieving goals."

26 "Robotics": Connecting AI to sensors, effectors and actuators

To enable movement in-and of--the physical world. Cars, spacecraft, surgeons, submarines, delivery bots.

From the Preface: Robotics and vision: "not ...independently defined problems"..."[things] in the service of achieving goals."

VII CONCLUSIONS--"Conclusions"

27 "Philosophy, Ethics, and Safety of AI": What is AI? What should we do with it? What might it do with us?

Trust--of systems, humans, ourselves, each other.

The human use of human beings. Usefulness of human beings at all?

Medicine. War.

28 "The Future of AI": Our tools will improve dramatically; our ends might remain the same.

Our preferences, our tools, our architectures. They're ours, for now.

Minimize the negative impacts, don't maximize the positive?

A: MATH--"Appendix A: Mathematical Background"

A.1 [SOLVING per §II]: "Complexity Analysis and O() Notation": Problem and algorithm analysis (computer science math)

Asymptotic and worst-case analysis of algorithms:

Approximately predicting the performance (and efficiency) of algorithms based on their steps in worst-case (or best or average) and infinite-case (asymptotic) input scenarios, in order to avoid actually implementing them, and to enable comparison of algorithms.[32]^17

Abstract over the input, and then the implementation, to find the key factors (string length; lines of code) that make the space/time difference. Ignore constants, usually; focus on the key variables.

Complexity analysis of problems:

Polynomial time O(n^k) problems, class P.

Non-polynomial time problems.

Nondeterministic polynomial problems: class NP. A problem with some algorithm that can guess and check a solution in polynomial time.

A.2 [THINKING per §III]: "Vectors, Matrices, and Linear Algebra": Line equation probing (`unknowns' math)

A vector is a pile of numbers (or unknowns or variables), a matrix is a pile of piles of numbers; some of the questions we can ask are `linear problems'[33]^18 (think prediction, interpolation, extrapolation), and algebra (finding unknowns by repairing [or completion] and balancing) on these things is `linear algebra'.

Vectors: Ordered sequences of values--represent something in the real world as just a set of values measuring specific aspects of that thing.[34]^19

Linear algebra: Doing algebra (finding unknowns by repairing [or completion] and balancing) on systems of equations of lines in planes instead of single equations and equations of points on lines (algebra). Think: finding line or plane intersections or bounded regions (based on inequalities instead of equations),[35]^20 and changing lines without affecting intersections[36]^21 --that sort of thing.

Thinking about higher dimensional objects: left-right x, up-down y, forward-backward z, wrist-watch value (time) t, color spectrum p, texture q, weight r, etc.

A.3 [UNCERTAINTY per §IV] "Probability Distributions": Quantifying `probably' (uncertainty math)

Probability is a controversial concept.[37]^22

Experiments yield outcomes; a set of outcomes is an event; the set of all possible outcomes is the sample space.[38]^23

"A `probability' is a measure over a set of events...." A probability model: sample space plus the probability measure for each outcome.

Cf. `random variable' note above.

B: CODE--"Appendix B: Notes on Languages and Algorithms"

B.1 "Defining Languages with Backus-Naur Form (BNF)": Defining formal languages

Languages: * propositional logic; * first-order logic; * English;

Formal language: strings, symbols, infinite strings, grammar, Chomsky hierarchy (context free).

BNF elements: * Terminals: symbols / words; * Non terminals that categorize: NounPhrase; * Start symbol: Sentence (English) or Expr (math) or Program (computing); * Rewrite rules: Sentence -> Expr Operator Expr | (Expr) | Number.

Think compilers:[39]^24 source language to target language, first syntax then semantics, reconstruct source logic in target logic.

1. Syntax analysis module: tokenizing + parsing (rule matching);

2. Code generation module: data translation and command translation.

Think also: (universal?) generative grammar, generating new strings, chat bots, GPT-3, Deep Blue, AlphaCode, Turing test.

B.2 "Describing Algorithms with Pseudocode": Code formatting and conventions

* Persistent variables: global state, side effects, OOP vs functional programming; agents use persistent variables for memory; implementation in OOP vs FP languages; * Functions as variable values: f(9)=3; * Indentation is significant: it scopes control structures (loops and cobditionals), also functions; but objects? Not really. They use `persistent' variable as memory, which can be implemented as an object, or a `functional closure', cf braces and `end'; * Destructuring assignment notation; * Default values for parameters: y=0; * Yield: generates an element of sequence; * Loops: for, while, repeat until; * Lists notation; * Sets notation; * Arrays index starts at 1 as in math, not code.

__

References

Aleksandrov, A. D., Kolmogorov, A. N., & Lavrent'ev, M. A. (1969). Mathematics: Its Content, Methods and Meaning (3 Volumes in One). Dover, 1999 reprint ed. ISBN: 0486409163. Searches: [40]https://www.amazon.com/s?k=0486409163 [41]https://www.google.com/search?q=isbn+0486409163 [42]https://lccn.loc.gov/99033023

Anderson, R. F. V. (1986). Introduction to Linear Algebra. Holt, Rinehard and Winston. ISBN: 003921835X. Searches: [43]https://www.amazon.com/s?k=003921835X [44]https://www.google.com/search?q=isbn+003921835X

Bittinger, M. L., & Ellenbogen, D. J. (2006). Intermediate Algebra: Concepts and Applications. Addison-Wesley, 7th ed. ISBN: 0321233867. Searches: [45]https://www.amazon.com/s?k=0321233867 [46]https://www.google.com/search?q=isbn+0321233867 [47]https://lccn.loc.gov/2004062480

Bulmer, M. G. (1967). Principles of Statistics. Dover, 1979 reprint ed. ISBN: 0486637603. Searches: [48]https://www.amazon.com/s?k=0486637603 [49]https://www.google.com/search?q=isbn+0486637603 [50]https://lccn.loc.gov/78072991

Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. ISBN: 978-0300209570. Searches: [51]https://www.amazon.com/s?k=9780300209570 [52]https://www.google.com/search?q=isbn+9780300209570 [53]https://lccn.loc.gov/2020947842

Frankfurt, H. G. (1988). The Importance of What We Care About. Cambridge. ISBN: 978-0521336116. Searches: [54]https://www.amazon.com/s?k=978-0521336116 [55]https://www.google.com/search?q=isbn+978-0521336116 [56]https://lccn.loc.gov/87026941

Gerrish, S. (2018). How Smart Machines Think. The MIT Press. ISBN: 978-0262038409. Searches: [57]https://www.amazon.com/s?k=9780262038409 [58]https://www.google.com/search?q=isbn+9780262038409 [59]https://lccn.loc.gov/2017059862

Horwich, P. (1982). Probability and Evidence. Cambridge. First published 1982; first paperback 2011; this Cambridge Philosophy Classics edition 2016. ISBN: 978-1316507018. Searches: [60]https://www.amazon.com/s?k=978-1316507018 [61]https://www.google.com/search?q=isbn+978-1316507018 [62]https://lccn.loc.gov/2015049717

Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. ISBN: 978-0374533557. Searches: [63]https://www.amazon.com/s?k=978-0374533557 [64]https://www.google.com/search?q=isbn+978-0374533557 [65]https://lccn.loc.gov/2012533187

Keynes, J. M. (1920). A Treatise on Probability: The Connection Between Philosophy and the History of Science. Wildside Press. ISBN: 978-1434406965. Searches: [66]https://www.amazon.com/s?k=9781434406965 [67]https://www.google.com/search?q=isbn+9781434406965 [68]https://lccn.loc.gov/2004041359

Kramer, E. E. (1970). The Nature and Growth of Modern Mathematics. Hawthorn Books. No ISBN. Searches: [69]https://www.amazon.com/s?k=kramer+nature+and+growth+of+modern+mathematics [70]https://www.google.com/search?q=kramer+nature+and+growth+of+modern+mathematics [71]https://lccn.loc.gov/73091317

Larson, R., Hostetler, R. P., & Edwards, B. H. (2005). College Algebra: A Graphing Approach. Houghton Mifflin, 4th ed. ISBN: 0618394370. Searches: [72]https://www.amazon.com/s?k=0618394370 [73]https://www.google.com/search?q=isbn+0618394370 [74]https://lccn.loc.gov/00104769

Macphail, E. M. (1982). Brain and Intelligence in Vertebrates. Oxford. ISBN 0198545517. Searches: [75]https://www.amazon.com/s?k=0198545517 [76]https://www.google.com/search?q=isbn+0198545517 [77]https://lccn.loc.gov/82166301

Manber, U. (1989). Introduction to Algorithms: A Creative Approach. Addison-Wesley, 1st ed. ISBN: 0201120372. Searches: [78]https://www.amazon.com/s?k=0201120372 [79]https://www.google.com/search?q=isbn+0201120372 [80]https://lccn.loc.gov/88002186

Nisan, N., & Schocken, S. (2005). The Elements of Computing Systems: Building a Modern Computer from First Principles. MIT. ISBN: 978-0262640688. Searches: [81]https://www.amazon.com/s?k=978-0262640688 [82]https://www.google.com/search?q=isbn+978-0262640688 [83]https://lccn.loc.gov/2005042807

Okasha, S. (2002). Philosophy of Science: A Very Short Introduction. Oxford University Press. ISBN: 0192802836. Searches: [84]https://www.amazon.com/s?k=0192802836 [85]https://www.google.com/search?q=isbn+0192802836 [86]https://lccn.loc.gov/2002510456

Retraice (2020/09/07). Re1: Three Kinds of Intelligence. retraice.com. [87]https://www.retraice.com/segments/re1 Retrieved 22nd Sep. 2020.

Retraice (2020/11/02). Re10: Living to Guess Another Day. retraice.com. [88]https://www.retraice.com/segments/re10 Retrieved 2nd Nov. 2020.

Retraice (2020/11/10). Re13: The Care Factor. retraice.com. [89]https://www.retraice.com/segments/re13 Retrieved 10th Nov. 2020.

Retraice (2020/11/25). Re15: Trust and Sources. retraice.com. [90]https://www.retraice.com/segments/re15 Retrieved 28th Feb. 2022.

Retraice (2022/03/07). Re18: Plan of Attack. retraice.com. [91]https://www.retraice.com/segments/re18 Retrieved 25th Mar. 2022.

Retraice (2022/10/10). Re19: Nature Is Not F-ing Around. retraice.com. [92]https://www.retraice.com/segments/re19 Retrieved 12th Oct. 2022.

Retraice (2022/10/19). Re23: You Need a World Model. retraice.com. [93]https://www.retraice.com/segments/re23 Retrieved 20th Oct. 2022.

Retraice (2022/10/23). Re27: Now That's a World Model - WM4. retraice.com. [94]https://www.retraice.com/segments/re27 Retrieved 24th Oct. 2022.

Retraice (2022/10/31). Re36: Notes on Conspiracy. retraice.com. [95]https://www.retraice.com/segments/re36 Retrieved 4th Nov. 2022.

Retraice (2022/11/12). Re48: From Drugs to Mao to Money. retraice.com. [96]https://www.retraice.com/segments/re48 Retrieved 14th Nov. 2022.

Retraice (2022/11/16). Re52: Big Questions About AI. retraice.com. [97]https://www.retraice.com/segments/re52 Retrieved 17th Nov. 2022.

Retraice (2022/11/21). Re57: AI, Agents, Problem-solving, Searching, Environments, Games (AIMA4e chpts. 1-6). retraice.com. [98]https://www.retraice.com/segments/re57 Retrieved 22nd Nov. 2022.

Retraice (2022/11/22). Re58: Thinking and Uncertainty (AIMA4e chpts. 7-18). retraice.com. [99]https://www.retraice.com/segments/re58 Retrieved 23rd Nov. 2022.

Retraice (2022/11/23). Re59: Learning, Interacting, Conclusions (AIMA4e chpts. 19-28). retraice.com. [100]https://www.retraice.com/segments/re59 Retrieved 24th Nov. 2022.

Retraice (2022/11/24). Re60: Complexity, Linear Algebra, Probability (AIMA4e Appendix A). retraice.com. [101]https://www.retraice.com/segments/re60 Retrieved 25th Nov. 2022.

Retraice (2022/11/25). Re61: Formal Languages, Pseudocode (AIMA4e Appendix B). retraice.com. [102]https://www.retraice.com/segments/re61 Retrieved 26th Nov. 2022.

Russell, B. (1948). Human Knowledge: Its Scope and Limits. Routledge. First published in 1948. This edition 1992. ISBN: 0415083028. Searches: [103]https://archive.org/search.php?query=Human%20Knowledge%3A%20Its%20Scope%20and%20Limits [104]https://www.amazon.com/s?k=0415083028 [105]https://www.google.com/search?q=isbn+0415083028 [106]https://lccn.loc.gov/94209784

Russell, B. (1959). My Philosophical Development. Wnwin Brothers. No ISBN. [107]https://archive.org/details/myphilosophicald0000russ/page/n11/mode/2up Retrieved 06 Jun. 2021. Searches: [108]https://www.amazon.com/s?k=Russell+My+Philosophical+Development [109]https://www.google.com/search?q=Russell+My+Philosophical+Development [110]https://lccn.loc.gov/59003496

Thompson, K. (1984). Reflections on trusting trust. Communications of the ACM, 27(8), 761-763. Aug. 1984. [111]https://doi.org/10.1145/358198.358210 Also available at: [112]https://www.cs.cmu.edu/~rdriley/487/papers/Thompson_1984_ReflectionsonTrustingTrust.pdf Retrieved 4th Dec. 2020.

Vallee, J. (1979). Messengers of Deception: UFO Contacts and Cults. And/Or Press. ISBN: 0915904381. Different edition and searches: [113]https://archive.org/details/MessengersOfDeceptionUFOContactsAndCultsJacquesValle1979/mode/2up [114]https://www.amazon.com/s?k=0915904381 [115]https://www.google.com/search?q=isbn+0915904381 [116]https://catalog.loc.gov/vwebv/search?searchArg=0915904381

References

1. https://retraice.com/ 2. XReSeg57 3. XReSeg57 4. XReSeg58 5. XReSeg58 6. XReSeg58 7. XReSeg58 8. XReSeg59 9. XReSeg59 10. XReSeg60 11. XReSeg60 12. XReSeg61 13. XReSeg61 14. fn1x0 15. fn2x0 16. fn3x0 17. fn4x0 18. fn5x0 19. fn6x0 20. fn7x0 21. fn8x0 22. fn9x0 23. fn10x0 24. fn11x0 25. fn12x0 26. fn13x0 27. fn14x0 28. fn15x0 29. fn16x0 30. https://www.deepmind.com/publications/reward-is-enough 31. https://arxiv.org/abs/1706.03762 32. fn17x0 33. fn18x0 34. fn19x0 35. fn20x0 36. fn21x0 37. fn22x0 38. fn23x0 39. fn24x0 40. https://www.amazon.com/s?k=0486409163 41. https://www.google.com/search?q=isbn+0486409163 42. https://lccn.loc.gov/99033023 43. https://www.amazon.com/s?k=003921835X 44. https://www.google.com/search?q=isbn+003921835X 45. https://www.amazon.com/s?k=0321233867 46. https://www.google.com/search?q=isbn+0321233867 47. https://lccn.loc.gov/2004062480 48. https://www.amazon.com/s?k=0486637603 49. https://www.google.com/search?q=isbn+0486637603 50. https://lccn.loc.gov/78072991 51. https://www.amazon.com/s?k=9780300209570 52. https://www.google.com/search?q=isbn+9780300209570 53. https://lccn.loc.gov/2020947842 54. https://www.amazon.com/s?k=978-0521336116 55. https://www.google.com/search?q=isbn+978-0521336116 56. https://lccn.loc.gov/87026941 57. https://www.amazon.com/s?k=9780262038409 58. https://www.google.com/search?q=isbn+9780262038409 59. https://lccn.loc.gov/2017059862 60. https://www.amazon.com/s?k=978-1316507018 61. https://www.google.com/search?q=isbn+978-1316507018 62. https://lccn.loc.gov/2015049717 63. https://www.amazon.com/s?k=978-0374533557 64. https://www.google.com/search?q=isbn+978-0374533557 65. https://lccn.loc.gov/2012533187 66. https://www.amazon.com/s?k=9781434406965 67. https://www.google.com/search?q=isbn+9781434406965 68. https://lccn.loc.gov/2004041359 69. https://www.amazon.com/s?k=kramer+nature+and+growth+of+modern+mathematics 70. https://www.google.com/search?q=kramer+nature+and+growth+of+modern+mathematics 71. https://lccn.loc.gov/73091317 72. https://www.amazon.com/s?k=0618394370 73. https://www.google.com/search?q=isbn+0618394370 74. https://lccn.loc.gov/00104769 75. https://www.amazon.com/s?k=0198545517 76. https://www.google.com/search?q=isbn+0198545517 77. https://lccn.loc.gov/82166301 78. https://www.amazon.com/s?k=0201120372 79. https://www.google.com/search?q=isbn+0201120372 80. https://lccn.loc.gov/88002186 81. https://www.amazon.com/s?k=978-0262640688 82. https://www.google.com/search?q=isbn+978-0262640688 83. https://lccn.loc.gov/2005042807 84. https://www.amazon.com/s?k=0192802836 85. https://www.google.com/search?q=isbn+0192802836 86. https://lccn.loc.gov/2002510456 87. https://www.retraice.com/segments/re1 88. https://www.retraice.com/segments/re10 89. https://www.retraice.com/segments/re13 90. https://www.retraice.com/segments/re15 91. https://www.retraice.com/segments/re18 92. https://www.retraice.com/segments/re19 93. https://www.retraice.com/segments/re23 94. https://www.retraice.com/segments/re27 95. https://www.retraice.com/segments/re36 96. https://www.retraice.com/segments/re48 97. https://www.retraice.com/segments/re52 98. https://www.retraice.com/segments/re57 99. https://www.retraice.com/segments/re58 100. https://www.retraice.com/segments/re59 101. https://www.retraice.com/segments/re60 102. https://www.retraice.com/segments/re61 103. https://archive.org/search.php?query=Human%20Knowledge%3A%20Its%20Scope%20and%20Limits 104. https://www.amazon.com/s?k=0415083028 105. https://www.google.com/search?q=isbn+0415083028 106. https://lccn.loc.gov/94209784 107. https://archive.org/details/myphilosophicald0000russ/page/n11/mode/2up 108. https://www.amazon.com/s?k=Russell+My+Philosophical+Development 109. https://www.google.com/search?q=Russell+My+Philosophical+Development 110. https://lccn.loc.gov/59003496 111. https://doi.org/10.1145/358198.358210 112. https://www.cs.cmu.edu/~rdriley/487/papers/Thompson_1984_ReflectionsonTrustingTrust.pdf 113. https://archive.org/details/MessengersOfDeceptionUFOContactsAndCultsJacquesValle1979/mode/2up 114. https://www.amazon.com/s?k=0915904381 115. https://www.google.com/search?q=isbn+0915904381 116. https://catalog.loc.gov/vwebv/search?searchArg=0915904381

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Re61: Formal Languages, Pseudocode (AIMA4e Appendix B)

retraice.com

Artificial Intelligence: A Modern Approach, 4th ed., pages 1030-1032.

Air date: Friday, 25th Nov. 2022, 11:00 PM Eastern/US.

Day 5: code

The latest version of our notes on AIMA4e (Russell & Norvig (2020)) will be at: https://aima4e.retraice.com.

Older versions will be preserved in the Retraice Notes feed, https://notes.retraice.com, or at the page for this segment, https://www.retraice.com/segments/re61.

B.1 Defining formal languages

B.2 Code formatting and conventions

AI engineering

Our aim is not to master AIMA4e, which is a book, a tool for mastery of something else. Our aim is to master artificial intelligence engineering,^1 the combination of contemporary AI and the advanced discipline of software engineering (writing code with its lifecycle in mind^2).

_

References

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Winters, T., Manshreck, T., & Wright, H. (2020). Software Engineering at Google: Lessons Learned from Programming Over Time. O'Reilly Media. ISBN: 978-1492082798. Free version and Searches: https://abseil.io/resources/swe-book https://www.amazon.com/s?k=9781492082798 https://www.google.com/search?q=isbn+9781492082798 https://www.oreilly.com/library/view/software-engineering-at/9781492082781/

Footnotes

^1 Russell & Norvig (2020) p. 1021.

^2 Winters et al. (2020)

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AIMA4e Annotations

A companion to the great white brick.

As of November 25, 2022

(Start date: November 21, 2022.)

[1]retraice.com

Version notes: [2]Retraice ([3]2022/11/21) (Re57), first draft, covered Preface, Sections I, II; [4]Retraice ([5]2022/11/22) (Re58), no footnotes, covered Sections III, IV; [6]Retraice ([7]2022/11/22) (Re58) again, moved some notes from Re57 and Re58 notes to footnotes here; [8]Retraice ([9]2022/11/23) (Re59), covered Sections V, VI, VII; [10]Retraice ([11]2022/11/24) (Re60), covered Appendix A.

PREFACE

* The phenomenon: intelligent agents[12]^1 * The discipline: artificial intelligence,[13]^2 "the study of agents that receive percepts from the environment and perform actions." (vii)

* Aspects of the phenomenon: + Agent function: "Each ...agent implements a function that maps percept sequences to actions" (vii) o Ways to represent agent functions include: "reactive agents, real-time planners, decision-theoretic systems, and deep learning systems." (vii) + Learning o "a construction method for competent systems" (viii) o "a way of extending the reach of the designer into unknown environments." (viii) + Goals o Robotics and vision: # "not ...independently defined problems" # "[things] in the service of achieving goals."

I INTELLIGENCE --"Artificial Intelligence"

1 Intro:

definitions, foundations, history, philosophy, state of the art, risks-benefits

2 Agents:

environments, `good' behavior, agent structure and types

II SOLVING--"Problem-solving"

3 Searching: Looking ahead to find a sequence.

Algorithms, strategies, informed/heuristic[14]^3 strategies.

4 Complex Environments: More realistic environments.

Local search, optimization, continuous spaces, nondeterministic actions, partially observable env.s, online search and unknown env.s.

5 Adversarial Games: Other agents competing against us.

Theory, optimal decisions, alpha-beta tree search, Monte Carlo tree search, stochastic g.s, partially observable g.s, limitations.

6 Constraint Satisfaction Problems: States as domains, solutions as allowable combinations of states.

Constraint propagation, inference, backtracking search, local search, structure of problems

III THINKING--"Knowledge, reasoning, and planning"

7 Logical Agents: Forming representations and reasoning before acting.

Knowledge-based agents; representing[15]^4 worlds; logic, world models and `possible worlds';[16]^5 logic without objects.

8 First-Order Logic: A formal language for objects and their relations.

`Ontological commitment' (what is assumed about reality); syntax, semantics; knowledge engineering (building formal representations of important[17]^6 objects and relations in a domain).

9 First-Order Inference: Reasoning about objects and their relations.

Algorithms to answer any 1st-order logic question.

10 Knowledge Representation: Representing the real world for problem solving.

What content to put into a knowledge base.

Knowledge representation languages and their uses (315): * First-order logic: reasoning about a world of objects and relations; * Hierarchical task networks: for reasoning about plans (chpt. 11); * Bayesian networks: for reasoning with uncertainty (chpt. 13); * Markov models: for reasoning over time (chpt. 17); * Deep neural networks: for reasoning about images, sounds, other data (chpt. 21).

11 Automated Planning: Hierarchical task networks.

Planning for spacecraft, factories, military campaigns; representing actions and states; efficient algorithms and heuristics.

IV UNCERTAINTY--"Uncertain knowledge and reasoning"

12 Quantifying Uncertainty: An answer to the laziness and ignorance that kill formal logic.

Causes of uncertainty are environment types (partially observable,[18]^7 nondeterministic, adversarial[19]^8 ); belief state grows big and unlikely fast (384); agents still need a way to act; absolute certainty is impossible;[20]^9 it comes down to importance, likelihood and degree of success (385-386).

Logic fails because laziness and ignorance; probability theory solves the qualification problem by summarizing the uncertainty.[21]^10 * Laziness: too much work to list everything, or use such a list; * Ignorance: (theoretical) there are no complete theories; (practical) we can never run all the tests.

13 Probabilistic Reasoning [big]: Bayesian networks.

For reasoning with uncertainty by representing causal independence (398) and conditional independence (401) relationships to simplify probabilistic representations of the world.

14 Probabilistic Reasoning Over Time: Comprehending the uncertain past, present and future.

[22]^11

Belief state plus transition model yields prediction (chpt 4, 7, 11); percepts and sensor model yield updated belief state; add probability theory to switch from possible states to probable states.[23]^12

15 Probabilistic Programming: Universal formal languages to represent any computable probability model, and they come with algorithms.

Using formal logic and traditional programming languages to represent probabilistic information.

16 Making Simple Decisions: Agents getting what they want in an uncertain world--as much as possible, on average.

Beliefs, desires; utility theory; utility functions; decision networks; the value of information (547);[24]^13 this chapter is concerned with one-shot or episodic decisions problems (as opposed to sequential) (cf. 562, below).

17 Making Complex Decisions: What to do today given decisions to be made tomorrow.

Sequential decision problems (as opposed to one-shot episodic, cf. above): the agent's utility depends on a sequence of decisions in stochastic (explicitly probabilistic (45)) and partially observable environments. Markov models (563; cf. 463) for reasoning over time (chpt. 17).

18 Multiagent Decision Making [big]: When there's more than one agent in the environment.

The nature of such environments and the strategies for problem-solving depend on the relationships between agents: non-cooperative and cooperative game theory; collective decision-making.

V LEARNING--"Machine learning"

19 Learning From Examples [big]: Improving behavior by observing the present (past?) and predicting the future.

Learning is improving performance (behavior) after making observations.[25]^14

If the agent is a computer: Machine learning: "a computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems." (651)

Subsections: * supervised learning; * learning decision trees; * model selection and optimization; * theory of learning; * linear regression (finding the best-fit line, i.e. predicting `future' [dependent] values based on plotting `past' [independent] values), classification;[26]^15 * nonparametric models (which retain all the examples, aka `instance-based' or `memory-based' learning, which is more true to large datasets [scalable?] than parametric, which summarize, and then discard, training data in fixed numbers of parameters), * ensemble learning (using multiple hypotheses instead of one, and averaging or voting--`base' models are combined into an `ensemble' model); * ML system development, the practice (software engineering and design patters in ML ops).

20 Learning Probabilistic Models: View `learning' as "uncertain reasoning from observations" and model the world accordingly.

Agents can't use probability and decision theories until they learn them from experience: treat learning itself as an inference process in a probabilistic world. Use Bayesian networks. Key concepts: data and hypotheses. "Here, the data are evidence ...instantiations of some or all of the random variables describing the domain."[27]^16

21 Deep Learning: represent hypotheses as "complex algebraic circuits with tunable connection strengths."

The circuits are orginzed into layers, a multi-step computation path. Ideal for recognizing, translating and generating images (including objects in images) and speech; `neural networks'.

From chpt. 10 on knowledge rep. languages, above notes: "deep neural networks: for reasoning about images, sounds, other data."

Think: gradient descent, back-propagation, convolutional neural networks.

22 Reinforcement Learning: Learning from experiences of reward and punishment instead of correct examples from a supervisor

Passive and active RL., Q-learning, apprenticeships and inverse RL.

Cf. Reward is Enough, May 2021: [28]https://www.deepmind.com/publications/reward-is-enough

VI INTERACTING--"Communicating, perceiving, and acting"

23 Natural Language Processing: Communicating with humans and learning from what they've written.

Language model: "a probability distribution describing the likelihood of any string." (824)

N-grams, grammar, syntax, semantics, parsing, vagueness, ambiguity, quantification.

24 Deep Learning for Natural Language Processing: Using neural nets on natural language to effectively handle the complexity.

"[R]epresenting words as points in a high-dimensional space." RNNs for "long-distance context."

Cf. Attention Is All You Need, 2017: [29]https://arxiv.org/abs/1706.03762 and AIMA4e p. 868, transformer architecture, self-attention.

25 Computer Vision: Connecting AI to cameras.

Photons provide a lot of valuable information to agents--too much information.

Surveillance cameras--good and bad; cars. Lots of machines do better if they can see.

From the Preface: Robotics and vision: "not ...independently defined problems"..."[things] in the service of achieving goals."

26 Robotics: Connecting AI to sensors, effectors and actuators

To enable movement in-and of--the physical world. Cars, spacecraft, surgeons, submarines, delivery bots.

From the Preface: Robotics and vision: "not ...independently defined problems"..."[things] in the service of achieving goals."

VII CONCLUSIONS--"Conclusions"

27 Philosophy, Ethics, and Safety of AI: What is AI? What should we do with it? What might it do with us?

Trust--of systems, humans, ourselves, each other.

The human use of human beings. Usefulness of human beings at all?

Medicine. War.

28 The Future of AI: Our tools will improve dramatically; our ends might remain the same.

Our preferences, our tools, our architectures. They're ours, for now.

Minimize the negative impacts, don't maximize the positive?

A: MATH--"Appendix A: Mathematical Background"

A.1 [SOLVING per §II]: Complexity Analysis and O() Notation: problem and algorithm analysis (computer science math)

Asymptotic and worst-case analysis of algorithms:

Approximately predicting the performance (and efficiency) of algorithms based on their steps in worst-case (or best or average) and infinite-case (asymptotic) input scenarios, in order to avoid actually implementing them, and to enable comparison of algorithms.[30]^17

Abstract over the input, and then the implementation, to find the key factors (string length; lines of code) that make the space/time difference. Ignore constants, usually; focus on the key variables.

Complexity analysis of problems:

Polynomial time O(n^k) problems, class P.

Non-polynomial time problems.

Nondeterministic polynomial problems: class NP. A problem with some algorithm that can guess and check a solution in polynomial time.

A.2 [THINKING per §III]: Vectors, Matrices, and Linear Algebra: line equation probing (`unknowns' math)

A vector is a pile of numbers (or unknowns or variables), a matrix is a pile of piles of numbers; some of the questions we can ask are `linear problems'[31]^18 (think prediction, interpolation, extrapolation), and algebra (finding unknowns by repairing [or completion] and balancing) on these things is `linear algebra'.

Vectors: Ordered sequences of values--represent something in the real world as just a set of values measuring specific aspects of that thing.[32]^19

Linear algebra: Doing algebra (finding unknowns by repairing [or completion] and balancing) on systems of equations of lines in planes instead of single equations and equations of points on lines (algebra). Think: finding line or plane intersections or bounded regions (based on inequalities instead of equations),[33]^20 and changing lines without affecting intersections[34]^21 --that sort of thing.

Thinking about higher dimensional objects: left-right x, up-down y, forward-backward z, wrist-watch value (time) t, color spectrum p, texture q, weight r, etc.

A.3 [UNCERTAINTY per §IV] Probability Distributions: quantifying `probably' (uncertainty math)

Probability is a controversial concept.[35]^22

Experiments yield outcomes; a set of outcomes is an event; the set of all possible outcomes is the sample space.[36]^23

"A `probability' is a measure over a set of events...." A probability model: sample space plus the probability measure for each outcome.

Cf. `random variable' note above.

B: CODE--"Appendix B: Notes on Languages and Algorithms"

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References

Aleksandrov, A. D., Kolmogorov, A. N., & Lavrent'ev, M. A. (1969). Mathematics: Its Content, Methods and Meaning (3 Volumes in One). Dover, 1999 reprint ed. ISBN: 0486409163. Searches: [37]https://www.amazon.com/s?k=0486409163 [38]https://www.google.com/search?q=isbn+0486409163 [39]https://lccn.loc.gov/99033023

Anderson, R. F. V. (1986). Introduction to Linear Algebra. Holt, Rinehard and Winston. ISBN: 003921835X. Searches: [40]https://www.amazon.com/s?k=003921835X [41]https://www.google.com/search?q=isbn+003921835X

Bittinger, M. L., & Ellenbogen, D. J. (2006). Intermediate Algebra: Concepts and Applications. Addison-Wesley, 7th ed. ISBN: 0321233867. Searches: [42]https://www.amazon.com/s?k=0321233867 [43]https://www.google.com/search?q=isbn+0321233867 [44]https://lccn.loc.gov/2004062480

Bulmer, M. G. (1967). Principles of Statistics. Dover, 1979 reprint ed. ISBN: 0486637603. Searches: [45]https://www.amazon.com/s?k=0486637603 [46]https://www.google.com/search?q=isbn+0486637603 [47]https://lccn.loc.gov/78072991

Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. ISBN: 978-0300209570. Searches: [48]https://www.amazon.com/s?k=9780300209570 [49]https://www.google.com/search?q=isbn+9780300209570 [50]https://lccn.loc.gov/2020947842

Frankfurt, H. G. (1988). The Importance of What We Care About. Cambridge. ISBN: 978-0521336116. Searches: [51]https://www.amazon.com/s?k=978-0521336116 [52]https://www.google.com/search?q=isbn+978-0521336116 [53]https://lccn.loc.gov/87026941

Gerrish, S. (2018). How Smart Machines Think. The MIT Press. ISBN: 978-0262038409. Searches: [54]https://www.amazon.com/s?k=9780262038409 [55]https://www.google.com/search?q=isbn+9780262038409 [56]https://lccn.loc.gov/2017059862

Horwich, P. (1982). Probability and Evidence. Cambridge. First published 1982; first paperback 2011; this Cambridge Philosophy Classics edition 2016. ISBN: 978-1316507018. Searches: [57]https://www.amazon.com/s?k=978-1316507018 [58]https://www.google.com/search?q=isbn+978-1316507018 [59]https://lccn.loc.gov/2015049717

Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. ISBN: 978-0374533557. Searches: [60]https://www.amazon.com/s?k=978-0374533557 [61]https://www.google.com/search?q=isbn+978-0374533557 [62]https://lccn.loc.gov/2012533187

Keynes, J. M. (1920). A Treatise on Probability: The Connection Between Philosophy and the History of Science. Wildside Press. ISBN: 978-1434406965. Searches: [63]https://www.amazon.com/s?k=9781434406965 [64]https://www.google.com/search?q=isbn+9781434406965 [65]https://lccn.loc.gov/2004041359

Kramer, E. E. (1970). The Nature and Growth of Modern Mathematics. Hawthorn Books. No ISBN. Searches: [66]https://www.amazon.com/s?k=kramer+nature+and+growth+of+modern+mathematics [67]https://www.google.com/search?q=kramer+nature+and+growth+of+modern+mathematics [68]https://lccn.loc.gov/73091317

Larson, R., Hostetler, R. P., & Edwards, B. H. (2005). College Algebra: A Graphing Approach. Houghton Mifflin, 4th ed. ISBN: 0618394370. Searches: [69]https://www.amazon.com/s?k=0618394370 [70]https://www.google.com/search?q=isbn+0618394370 [71]https://lccn.loc.gov/00104769

Macphail, E. M. (1982). Brain and Intelligence in Vertebrates. Oxford. ISBN 0198545517. Searches: [72]https://www.amazon.com/s?k=0198545517 [73]https://www.google.com/search?q=isbn+0198545517 [74]https://lccn.loc.gov/82166301

Manber, U. (1989). Introduction to Algorithms: A Creative Approach. Addison-Wesley, 1st ed. ISBN: 0201120372. Searches: [75]https://www.amazon.com/s?k=0201120372 [76]https://www.google.com/search?q=isbn+0201120372 [77]https://lccn.loc.gov/88002186

Okasha, S. (2002). Philosophy of Science: A Very Short Introduction. Oxford University Press. ISBN: 0192802836. Searches: [78]https://www.amazon.com/s?k=0192802836 [79]https://www.google.com/search?q=isbn+0192802836 [80]https://lccn.loc.gov/2002510456

Retraice (2020/09/07). Re1: Three Kinds of Intelligence. retraice.com. [81]https://www.retraice.com/segments/re1 Retrieved 22nd Sep. 2020.

Retraice (2020/11/02). Re10: Living to Guess Another Day. retraice.com. [82]https://www.retraice.com/segments/re10 Retrieved 2nd Nov. 2020.

Retraice (2020/11/10). Re13: The Care Factor. retraice.com. [83]https://www.retraice.com/segments/re13 Retrieved 10th Nov. 2020.

Retraice (2020/11/25). Re15: Trust and Sources. retraice.com. [84]https://www.retraice.com/segments/re15 Retrieved 28th Feb. 2022.

Retraice (2022/03/07). Re18: Plan of Attack. retraice.com. [85]https://www.retraice.com/segments/re18 Retrieved 25th Mar. 2022.

Retraice (2022/10/10). Re19: Nature Is Not F-ing Around. retraice.com. [86]https://www.retraice.com/segments/re19 Retrieved 12th Oct. 2022.

Retraice (2022/10/19). Re23: You Need a World Model. retraice.com. [87]https://www.retraice.com/segments/re23 Retrieved 20th Oct. 2022.

Retraice (2022/10/23). Re27: Now That's a World Model - WM4. retraice.com. [88]https://www.retraice.com/segments/re27 Retrieved 24th Oct. 2022.

Retraice (2022/10/31). Re36: Notes on Conspiracy. retraice.com. [89]https://www.retraice.com/segments/re36 Retrieved 4th Nov. 2022.

Retraice (2022/11/12). Re48: From Drugs to Mao to Money. retraice.com. [90]https://www.retraice.com/segments/re48 Retrieved 14th Nov. 2022.

Retraice (2022/11/16). Re52: Big Questions About AI. retraice.com. [91]https://www.retraice.com/segments/re52 Retrieved 17th Nov. 2022.

Retraice (2022/11/21). Re57: AI, Agents, Problem-solving, Searching, Environments, Games (AIMA4e chpts. 1-6). retraice.com. [92]https://www.retraice.com/segments/re57 Retrieved 22nd Nov. 2022.

Retraice (2022/11/22). Re58: Thinking and Uncertainty (AIMA4e chpts. 7-18). retraice.com. [93]https://www.retraice.com/segments/re58 Retrieved 23rd Nov. 2022.

Retraice (2022/11/23). Re59: Learning, Interacting, Conclusions (AIMA4e chpts. 19-28). retraice.com. [94]https://www.retraice.com/segments/re59 Retrieved 24th Nov. 2022.

Retraice (2022/11/24). Re60: Complexity, Linear Algebra, Probability (AIMA4e Appendix A). retraice.com. [95]https://www.retraice.com/segments/re60 Retrieved 25th Nov. 2022.

Russell, B. (1948). Human Knowledge: Its Scope and Limits. Routledge. First published in 1948. This edition 1992. ISBN: 0415083028. Searches: [96]https://archive.org/search.php?query=Human%20Knowledge%3A%20Its%20Scope%20and%20Limits [97]https://www.amazon.com/s?k=0415083028 [98]https://www.google.com/search?q=isbn+0415083028 [99]https://lccn.loc.gov/94209784

Russell, B. (1959). My Philosophical Development. Wnwin Brothers. No ISBN. [100]https://archive.org/details/myphilosophicald0000russ/page/n11/mode/2up Retrieved 06 Jun. 2021. Searches: [101]https://www.amazon.com/s?k=Russell+My+Philosophical+Development [102]https://www.google.com/search?q=Russell+My+Philosophical+Development [103]https://lccn.loc.gov/59003496

Vallee, J. (1979). Messengers of Deception: UFO Contacts and Cults. And/Or Press. ISBN: 0915904381. Different edition and searches: [104]https://archive.org/details/MessengersOfDeceptionUFOContactsAndCultsJacquesValle1979/mode/2up [105]https://www.amazon.com/s?k=0915904381 [106]https://www.google.com/search?q=isbn+0915904381 [107]https://catalog.loc.gov/vwebv/search?searchArg=0915904381

References

1. https://retraice.com/ 2. XReSeg57 3. XReSeg57 4. XReSeg58 5. XReSeg58 6. XReSeg58 7. XReSeg58 8. XReSeg59 9. XReSeg59 10. XReSeg60 11. XReSeg60 12. fn1x0 13. fn2x0 14. fn3x0 15. fn4x0 16. fn5x0 17. fn6x0 18. fn7x0 19. fn8x0 20. fn9x0 21. fn10x0 22. fn11x0 23. fn12x0 24. fn13x0 25. fn14x0 26. fn15x0 27. fn16x0 28. https://www.deepmind.com/publications/reward-is-enough 29. https://arxiv.org/abs/1706.03762 30. fn17x0 31. fn18x0 32. fn19x0 33. fn20x0 34. fn21x0 35. fn22x0 36. fn23x0 37. https://www.amazon.com/s?k=0486409163 38. https://www.google.com/search?q=isbn+0486409163 39. https://lccn.loc.gov/99033023 40. https://www.amazon.com/s?k=003921835X 41. https://www.google.com/search?q=isbn+003921835X 42. https://www.amazon.com/s?k=0321233867 43. https://www.google.com/search?q=isbn+0321233867 44. https://lccn.loc.gov/2004062480 45. https://www.amazon.com/s?k=0486637603 46. https://www.google.com/search?q=isbn+0486637603 47. https://lccn.loc.gov/78072991 48. https://www.amazon.com/s?k=9780300209570 49. https://www.google.com/search?q=isbn+9780300209570 50. https://lccn.loc.gov/2020947842 51. https://www.amazon.com/s?k=978-0521336116 52. https://www.google.com/search?q=isbn+978-0521336116 53. https://lccn.loc.gov/87026941 54. https://www.amazon.com/s?k=9780262038409 55. https://www.google.com/search?q=isbn+9780262038409 56. https://lccn.loc.gov/2017059862 57. https://www.amazon.com/s?k=978-1316507018 58. https://www.google.com/search?q=isbn+978-1316507018 59. https://lccn.loc.gov/2015049717 60. https://www.amazon.com/s?k=978-0374533557 61. https://www.google.com/search?q=isbn+978-0374533557 62. https://lccn.loc.gov/2012533187 63. https://www.amazon.com/s?k=9781434406965 64. https://www.google.com/search?q=isbn+9781434406965 65. https://lccn.loc.gov/2004041359 66. https://www.amazon.com/s?k=kramer+nature+and+growth+of+modern+mathematics 67. https://www.google.com/search?q=kramer+nature+and+growth+of+modern+mathematics 68. https://lccn.loc.gov/73091317 69. https://www.amazon.com/s?k=0618394370 70. https://www.google.com/search?q=isbn+0618394370 71. https://lccn.loc.gov/00104769 72. https://www.amazon.com/s?k=0198545517 73. https://www.google.com/search?q=isbn+0198545517 74. https://lccn.loc.gov/82166301 75. https://www.amazon.com/s?k=0201120372 76. https://www.google.com/search?q=isbn+0201120372 77. https://lccn.loc.gov/88002186 78. https://www.amazon.com/s?k=0192802836 79. https://www.google.com/search?q=isbn+0192802836 80. https://lccn.loc.gov/2002510456 81. https://www.retraice.com/segments/re1 82. https://www.retraice.com/segments/re10 83. https://www.retraice.com/segments/re13 84. https://www.retraice.com/segments/re15 85. https://www.retraice.com/segments/re18 86. https://www.retraice.com/segments/re19 87. https://www.retraice.com/segments/re23 88. https://www.retraice.com/segments/re27 89. https://www.retraice.com/segments/re36 90. https://www.retraice.com/segments/re48 91. https://www.retraice.com/segments/re52 92. https://www.retraice.com/segments/re57 93. https://www.retraice.com/segments/re58 94. https://www.retraice.com/segments/re59 95. https://www.retraice.com/segments/re60 96. https://archive.org/search.php?query=Human%20Knowledge%3A%20Its%20Scope%20and%20Limits 97. https://www.amazon.com/s?k=0415083028 98. https://www.google.com/search?q=isbn+0415083028 99. https://lccn.loc.gov/94209784 100. https://archive.org/details/myphilosophicald0000russ/page/n11/mode/2up 101. https://www.amazon.com/s?k=Russell+My+Philosophical+Development 102. https://www.google.com/search?q=Russell+My+Philosophical+Development 103. https://lccn.loc.gov/59003496 104. https://archive.org/details/MessengersOfDeceptionUFOContactsAndCultsJacquesValle1979/mode/2up 105. https://www.amazon.com/s?k=0915904381 106. https://www.google.com/search?q=isbn+0915904381 107. https://catalog.loc.gov/vwebv/search?searchArg=0915904381

(The below text version of the notes is for search purposes and convenience. See the PDF version for proper formatting such as bold, italics, etc., and graphics where applicable. Copyright: 2022 Retraice, Inc.)

Re60: Complexity, Linear Algebra, Probability (AIMA4e Appendix A)

retraice.com

Artificial Intelligence: A Modern Approach, 4th ed., pages 1023-1029.

Air date: Thursday, 24th Nov. 2022, 11:00 PM Eastern/US.

Day 4: math

The latest version of our notes on AIMA4e (Russell & Norvig (2020)) will be at: https://aima4e.retraice.com.

Older versions will be preserved in the Retraice Notes feed, https://notes.retraice.com, or at the page for this segment, https://www.retraice.com/segments/re60.

Math is unfamiliar to the math-outsider the way car engines, computers and human bodies are unfamiliar to outsiders of these subjects. And yet, to be competent, we must deal with the hard-reality, right-or-wrong nature of real things by first becoming familiar with them.

Unlike concrete and tangible things, math is all in the mind. And abbreviations and elisions and assumptions and heavy use of unfamiliar notations all make math harder for some. But these issues can be overcome with time and reasonable effort.

A.1 problem and algorithm analysis

A.2 line equation probing

A.3 quantifying `probably'

__

References

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498