Chapter 2 Agents & Environments - Courses.cs.washington.edu

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Chapter 2Agents & Environments CSE AI FacultyOutline Agents and environmentsRationalityPEAS specificationEnvironment typesAgent types21

Agents An agent is any entity that can perceive itsenvironment through sensors and act uponthat environment through actuators Human agent:Sensors: Eyes, ears, and other organsActuators: Hands, legs, mouth, etc. Robotic agent:Sensors: Cameras, laser range finders, etc.Actuators: Motorized limbs, wheels, etc.3Types of Agents Immobots (Immobile Robots)Intelligent buildingsIntelligent forestsAutonomous spacecraft SoftbotsJango (early softbot forshopping)Microsoft ClippyAskjeeves.com (now Ask.com)Expert Systems Cardiologist2

Intelligent Agents Have sensors and actuators (effectors) Implement mapping from percept sequenceto actionsperceptsEnvironmentAgentactions Maximize a Performance MeasurePerformance Measures Performance measure An objectivecriterion for success of an agent'sbehavior E.g., vacuum cleaner agentperformance measure: amount of dirtcleaned up, amount of time taken,amount of electricity consumed, amountof noise generated, etc.63

Rational Agent“For each possible percept sequence, doeswhatever action is expected to maximize itsperformance measure on the basis of evidenceperceived so far and built-in knowledge.'' Rationality vs. omniscienceRationality maximizes expected performanceOmniscience maximizes actual performance (but impossible to achieve inreality) Need to use information gathering actions and learningAutonomyA rational agent is autonomous if it can learnto compensate for partial or incorrect priorknowledgeWhy is this important?4

Task Environments The “task environment” for an agent iscomprised of PEAS (Performance measure,Environment, Actuators, Sensors) E.g., Consider the task of designing anautomated taxi driver:Performance measure ?Environment ?Actuators ?Sensors ?9PEAS PEAS for Automated taxi driver Performance measure:Safe, fast, legal, comfortable trip, maximize profits Environment:Roads, other traffic, pedestrians, customers Actuators:Steering wheel, accelerator, brake, signal, horn Sensors:Cameras, sonar, speedometer, GPS, odometer, enginesensors, keyboard105

PEAS PEAS for Medical diagnosis system Performance measure:Healthy patient, minimize costs, lawsuits Environment:Patient, hospital, staff Actuators:Screen display (questions, tests, diagnoses, treatments,referrals) Sensors:Keyboard (entry of symptoms, findings, patient's answers)11Properties of Environments Observability: full vs. partialSensors detect all aspects of state of environmentrelevant to choice of action? Deterministic vs. stochasticNext state completely determined by current state andaction? Episodic vs. sequentialCurrent action independent of previous actions? Static vs. dynamicCan environment change over time? Discrete vs. continuousState of environment, time, percepts, and actionsdiscrete or continuous-valued? Single vs. multiagent6

Properties of Environments Observability: full vs. partialDeterministic vs. stochasticEpisodic vs. sequentialStatic vs. dynamicDiscrete vs. continuousSingle vs. multiagent Chess Poker Coffee delivery mobile robotAgent Functions and Agent Programs An agent’s behavior can be described by anagent function mapping percept sequences toactions taken by the agent An implementation of an agent functionrunning on the agent architecture (e.g., arobot) is called an agent program Our goal: Develop concise agent programs forimplementing rational agents147

Example15How should the agent be designed if It has location and dirt sensors, but no internal state? It has no sensors, but knows the starting state? It has no sensors, and does not know the starting state?168

Implementing Rational Agents Table lookup based on percept sequencesInfeasible Agent programs:Simple reflex agentsAgents with memory Reflex agent with internal state Goal-based agents Utility-based agentsSimple Reflex AgentsAGENTSensorsPerceptwhat actionshould I do now?ENVIRONMENTCondition-Action rulesEffectors9

Simple Reflex AgentsReflex Agent with Internal StatestateHow world evolvesSensorswhat world islike nowCondition-Action rulesAGENTwhat actionshould I do now?ENVIRONMENTWhat my actions doEffectors10

Goal-Based AgentsSensorsstateHow world evolveswhat it’ll be likeif I do action Awhat actionshould I do now?GoalsAGENTENVIRONMENTWhat my actions dowhat world islike nowEffectorsUtility-Based AgentsSensorsstatewhat world islike nowWhat my actions dowhat it’ll be likeif I do action AHow happy would Ibe in such a state?Utility functionAGENTwhat actionshould I do now?ENVIRONMENTHow world evolvesEffectors11

Learning AgentsPerformance IRONMENTLearningelementEffectorsWhile driving, what’s the best policy? Always stop at a stop sign Never stop at a stop sign Look around for other cars and stop only if yousee one approaching Look around for a cop and stop only if you see one What kind of agent are you?– reflex, goal-based, utility-based?12

Chess Poker Coffee delivery mobile robot 14 Agent Functions and Agent Programs An agent's behavior can be described by an agent function mapping percept sequences to actions taken by the agent An implementation of an agent function running on the agent architecture (e.g., a robot) is called an agent program

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