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INTELLIGENT AGENTS

INTELLIGENT AGENTS. An agent is anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors. Humans Robots Programs. Structure of an INTELLIGENT AGENTS. Vacuum-Cleaner. Rational Agent.

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INTELLIGENT AGENTS

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  1. INTELLIGENT AGENTS

  2. An agent is anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors Humans Robots Programs

  3. Structure of an INTELLIGENT AGENTS

  4. Vacuum-Cleaner

  5. Rational Agent A rational agent is one that does the right thing

  6. Rationality • Rational Agent • Performance Measures • Rationality • Performance Measure that defines the criterion of success • Prior Knowledge of the Agent • Actions that can be performed by the Agent • Percept Sequence of the Agent up to date

  7. Rational Agent For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the Agent has.

  8. Environments • The first step in designing an Agent • Task Environment • Performance Environment Actuators Sensors description • Automated Taxi Driver • P : safe, fast, legal, comfort, maximize profit • E : roads, other vehicles-traffics, pedestrians, customers • A : steering, accelerator, brake, signal, clutch, horn, display, lights • S : cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard

  9. Other PEAS… • Medical Diagnosis System • Satellite Image Analysis System • Part-picking Robot • Refinery Controller • Interactive English Tutor

  10. Properties of the Task Environment • Fully vs. Partially Observable • Deterministic vs. Stochastic • Episodic vs. Sequential • Static vs. Dynamic • Discrete vs. Continuous • Single Agent vs. Multi Agent

  11. Structure of an Agent • Simple Reflex Agents • Model based Agents • Goal based Agents • Utility based Agents • Learning Agents

  12. Simple Reflex Agents

  13. Model based Agents

  14. Goal based Agents

  15. Utility based Agents

  16. 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.

  17. Summary • 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

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