ICS-171: 1 Intelligent Agents Chapter 2 ICS 171, Fall 2009ICS-171: 2 Discussion • Why is the Chinese room argument impractical and how would we would we have to change the Turing test so that it is not subject to this criticism? • Godel’s theorem assures us that humans will always be superior to machines. • A robot/agent can never be aware of itself (be self-conscious).ICS-171: 3 Agents • 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: 4 Agents 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: 5 Vacuum-cleaner world • Percepts: location and state of the environment, e.g., [A,Dirty], [B,Clean] • Actions: Left, Right, Suck, NoOpICS-171: 6 Rational agents • Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected 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: 7 Rational 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: 8 Task Environment • Before we design an intelligent agent, we must specify its “task environment”: PEAS: Performance measure Environment Actuators SensorsICS-171: 9 PEAS • 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: 10 PEAS • 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: 11 PEAS • 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: 12 Environment 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: 13 Environment 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: 14 task environm. observable determ./ stochastic episodic/ sequential static/ dynamic discrete/ continuous agents crossword puzzle fully determ. sequential static discrete single chess with clock fully strategic sequential semi discrete multi poker back gammon taxi driving partial stochastic sequential dynamic continuous multi medical diagnosis partial stochastic sequential dynamic continuous single image analysis fully determ. episodic semi continuous single partpicking robot partial stochastic episodic dynamic continuous single refinery controller partial stochastic sequential dynamic continuous single interact. Eng. tutor partial stochastic sequential dynamic discrete multiICS-171: 15 task environm. observable determ./ stochastic episodic/ sequential static/ dynamic discrete/ continuous agents crossword puzzle fully determ. sequential static discrete single chess with clock fully strategic sequential semi discrete multi poker partial stochastic sequential static discrete multi back gammon taxi driving partial stochastic sequential dynamic continuous multi medical diagnosis partial stochastic sequential dynamic continuous single image analysis fully determ. episodic semi continuous single partpicking robot partial stochastic episodic dynamic continuous single refinery controller partial stochastic sequential dynamic continuous single interact. Eng. tutor partial stochastic sequential dynamic discrete multiICS-171: 16 task environm. observable determ./ stochastic episodic/ sequential static/ dynamic discrete/ continuous agents crossword puzzle fully determ. sequential static discrete single chess with clock fully strategic sequential semi discrete multi poker partial stochastic sequential static discrete multi back gammon fully stochastic sequential static discrete multi taxi driving partial stochastic sequential dynamic continuous multi medical diagnosis partial stochastic sequential dynamic continuous single image analysis fully
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