Agent Agent Summary Learning agents Utility-based agents rete?singleUtility-based agents Agent Environment Sensors How happy I will be if I do action A What it will be like in such a state should do now What action I State How the world evolves What my actions do Utility Actuators is like now What the world Chapter 2 25 Learning agents Performance standard Agent Environment Sensors element Performance changes knowledge goals learning generator Problem feedback element Learning Critic Actuators Chapter 2 26 Summary Agents interact with environments through actuators and sensors The agent function describes what the agent does in all circumstances The performance measure evaluates the environment sequence A perfectly rational agent maximizes expected performance Agent programs implement (some) agent functions PEAS descriptions define task environments Environments are categorized along several dimensions: observable? deterministic? episodic? static? discrete? single-agent? Several basic agent architectures exist: reflex, reflex with state, goal-based, utility-based Chapter 2 27