Agents and Environments in AI

Agents and Environments in AI Systems

• An AI system consists of an agent and its environment, where agents act and environments may contain other agents.
• An agent is anything that perceives its environment through sensors and acts on it through effectors, such as a human, robotic, or software agent.
• Agent terminology includes performance measure, behavior, percept, percept sequence, and agent function.
• Rationality in AI refers to the status of being reasonable, sensible, and having good judgment based on expected actions and results.
• An ideal rational agent maximizes its performance measure based on its percept sequence and built-in knowledge base.
• Simple reflex agents choose actions solely based on current percepts and are rational only if correct decisions are made based on those percepts.
• Model-based reflex agents use a model of the world to choose actions and maintain an internal state.
• Goal-based agents choose actions to achieve goals and are more flexible than reflex agents.
• Utility-based agents choose actions based on a preference for each state and are useful when there are conflicting or uncertain goals.
• Environments in AI can be entirely artificial or rich and unlimited, with some agents existing in both real and artificial environments.
• The Turing Test measures the success of an intelligent behavior in a system by testing it against human intelligence.
• Properties of environments in AI include discrete/continuous, observable/partially observable, static/dynamic, single/multiple agents, accessible/inaccessible, deterministic/non-deterministic, and episodic/non-episodic.