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AIMA4e Annotations

A companion to the great white brick.

As of November 23, 2022

(Start date: November 21, 2022.)

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PREFACE

* The phenomenon: intelligent agents * The discipline: artificial intelligence "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 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 worlds; logic, world models and `possible worlds'; 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 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, nondeterministic, adversarial); belief state grows big and unlikely fast (384); agents still need a way to act; absolute certainty is impossible; 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. * 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 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.

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); this chapter is concerned with one-shot or episodic decsions problems (as opposed to sequential) (cf. 562).

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

Sequential decision problems (as opposed to one-shot episodic): 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: 20: 21: deep neural networks: for reasoning about images, sounds, other data (chpt. 21). 22:

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

23: 24: 25: 26:

VII CONCLUSIONS--"Conclusions"

27: 28:

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References

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