This episode of Techsplainers explores utility-based agents, sophisticated AI systems that use mathematical utility functions to make optimal decisions by weighing multiple competing objectives. We examine the five key components of these agents: utility functions, sensors, internal models, action selection mechanisms, and actuators. The episode walks through their decision-making workflow and highlights applications in smart homes, self-driving cars, healthcare, and e-commerce. While utility-based agents offer advantages in adaptability, flexibility, and reliability over simpler AI systems, they also present challenges in computational requirements and the ethical considerations of translating human values into mathematical formulas. Understanding these advanced agents provides insight into how AI can make complex trade-offs in uncertain environments. 

Find more information at https://www.ibm.com/think/podcasts/techsplainers 

Narrated by Matt Finio