Intelligent AgentsAgentsAgents and environmentsVacuum-cleaner worldRational agentsSlide 6Task EnvironmentPEASSlide 9Slide 10Environment typesSlide 12Slide 13Slide 14Agent typesTable Driven Agent.Simple reflex agentsModel-based reflex agentsGoal-based agentsUtility-based agentsLearning agentsSlide 22ICS-171: Notes 2: 1Intelligent AgentsChapter 2ICS 171, spring 2007ICS-171: Notes 2: 2Agents•An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators Human agent: eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for actuators•Robotic agent: cameras and infrared range finders for sensors; various motors for actuatorsICS-171: Notes 2: 3Agents and environments•The agent function maps from percept histories to actions:[f: P* A]•The agent program runs on the physical architecture to produce f•agent = architecture + programICS-171: Notes 2: 4Vacuum-cleaner world•Percepts: location and state of the environment, e.g., [A,Dirty], [B,Clean]•Actions: Left, Right, Suck, NoOpICS-171: Notes 2: 5Rational agents•Rational Agent: For each possible percept sequence, a rational agent should select an action that is exp ected to maximize its performance measure, based on the evidence provided by the percept sequence and whatever built-in knowledge the agent has.•Performance measure: An objective criterion for success of an agent's behavior•E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.ICS-171: Notes 2: 6Rational agents•Rationality is distinct from omniscience (all-knowing with infinite knowledge)•Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration)•An agent is autonomous if its behavior is determined by its own percepts & experience (with ability to learn and adapt) without depending solely on build-in knowledgeICS-171: Notes 2: 7Task Environment•Before we design an intelligent agent, we must specify its “task environment”: PEAS: Performance measure Environment Actuators SensorsICS-171: Notes 2: 8PEAS•Example: Agent = 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, engine sensors, keyboardICS-171: Notes 2: 9PEAS•Example: Agent = 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)ICS-171: Notes 2: 10PEAS•Example: Agent = Part-picking robot•Performance measure: Percentage of parts in correct bins•Environment: Conveyor belt with parts, bins•Actuators: Jointed arm and hand•Sensors: Camera, joint angle sensorsICS-171: Notes 2: 11Environment types•Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time.•Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic)•Episodic (vs. sequential): An agent’s action is divided into atomic episodes. Decisions do not depend on previous decisions/actions.ICS-171: Notes 2: 12Environment types•Static (vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does)•Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions. How do we represent or abstract or model the world?•Single agent (vs. multi-agent): An agent operating by itself in an environment. Does the other agent interfere with my performance measure?ICS-171: Notes 2: 13task environm.observable determ./stochasticepisodic/sequentialstatic/dynamicdiscrete/continuousagentscrosswordpuzzlefully determ. sequential static discrete singlechess withclockfully strategic sequential semi discrete multipokerbackgammontaxidrivingpartial stochastic sequential dynamic continuous multimedicaldiagnosispartial stochastic sequential dynamic continuous singleimage analysisfully determ. episodic semi continuous singlepartpickingrobotpartial stochastic episodic dynamic continuous singlerefinery controllerpartial stochastic sequential dynamic continuous singleinteract.Eng. tutorpartial stochastic sequential dynamic discrete multiICS-171: Notes 2: 14task environm.observable determ./stochasticepisodic/sequentialstatic/dynamicdiscrete/continuousagentscrosswordpuzzlefully determ. sequential static discrete singlechess withclockfully strategic sequential semi discrete multipoker partial stochastic sequential static discrete multibackgammonfully stochastic sequential static discrete multitaxidrivingpartial stochastic sequential dynamic continuous multimedicaldiagnosispartial stochastic sequential dynamic continuous singleimage analysisfully determ. episodic semi continuous singlepartpickingrobotpartial stochastic episodic dynamic continuous singlerefinery controllerpartial stochastic sequential dynamic continuous singleinteract.Eng. tutorpartial stochastic sequential dynamic discrete multiICS-171: Notes 2: 15Agent types•Five basic types in order of increasing generality:•Table Driven agent•Simple reflex agents•Model-based reflex agents•Goal-based agents•Utility-based agentsICS-171: Notes 2: 16Table Driven Agent.current state of decision processtable lookupfor entire historyICS-171: Notes 2: 17Simple reflex agentsexample: vacuum cleaner worldNO MEMORYFails if environmentis partially observableICS-171: Notes 2: 18Model-based reflex agentsModel the state of the world by:modeling how the world chanceshow it’s actions change the world description ofcurrent world state•This can work even with partial information•It’s is unclear what to do without a clear goalICS-171: Notes 2: 19Goal-based agentsGoals provide reason to prefer one action over the
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