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Agents and Intelligent Agents An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators An intelligent agent acts further for its own interests. Artificial Intelligence, Lecturer #8 Example of Agents Human agent: Sensors: eyes, ears, nose…. Actuators: hands, legs, mouth, … Robotic agent: Sensors: cameras and infrared range finders Actuators: various motors Agents include humans, robots, thermostats, etc Perceptions: Vision, speech reorganization, etc. Agent Function & program An agent is specified by an agent function f sequences of percepts Y to actions A: that maps Y { y0 , y1 ,..., yT } A {a0 , a1 ,..., aT } f :Y A The agent program runs on the physical architecture to produce f agent = architecture + program “Easy” solution: table that maps every possible sequence Y to an action A Agents and Environments The agent function maps from percept histories (sequences of percepts) to actions: [f: P* A] Example: A Vacuum-Cleaner Agent A B Percepts: location and contents, e.g., (A,dust) • (Idealization: locations are discrete) Actions: move, clean, do nothing: LEFT, RIGHT, SUCK, NOP Example: A Vacuum-Cleaner Agent Properties of Agent Mobility: the ability of an agent to move around in an environment. Veracity: an agent will not knowingly communicate false information Benevolence: agents do not have conflicting goals, and that every agent will therefore always try to do what is asked of it Rationality: agent will act in order to achieve its goals, and will not act in such a way as to prevent its goals being achieved. Learning/adoption: agents improve performance over time Agents Vs. Objects Agents are autonomous agents embody stronger notion of autonomy than objects, and in particular, t hey decide for themselves whether or not to perform an action on request fr om another agent Agents are smart capable of flexible (reactive, pro-active, social) behavior, and the standard obj ect model has nothing to say about such types of behavior Agents are active a multi-agent system is inherently multi-threaded, in that each agent is assu med to have at least one thread of active control The Concept of Rationality What is rational at any given time depends on four things: The performance measure that defines the criterion of success. The agent’s prior knowledge of the environment. The actions the agent can perform. The agent’s percept sequence to date. Rational Agents Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure. Performance measure: An objective criterion for success of an agent's behavior, given the evidence provided by the percept sequence. Nature of Task Environment To design a rational agent we need to specify a task environment a problem specification for which the agent is a PEAS: to specify a task environment Performance measure Environment Actuators Sensors solution PEAS Specifying an Automated Taxi Driver Performance measure: safe, fast, legal, comfortable, maximize profits Environment: roads, other traffic, pedestrians, customers Actuators: steering, accelerator, brake, signal, horn Sensors: cameras, sonar, speedometer, GPS PEAS: Another Example Agent: Medical diagnosis system Performance measure: Healthy patient, minimize costs. Environment: Patient, hospital, staff Actuators: Screen display (questions, tests, diagnoses, treatments, referrals) Sensors: Keyboard (entry of symptoms, findings, patient's answers) Recommended Textbooks [Negnevitsky, 2001] M. Negnevitsky “ Artificial Intelligence: A guide to Intelligent Systems”, Pearson Education Limited, England, 2002. [Russel, 2003] S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition [Patterson, 1990] D. W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Prentice-Hall Inc., Englewood Cliffs, N.J, USA, 1990. [Minsky, 1974] M. Minsky “A Framework for Representing Knowledge”, MIT-AI Laboratory Memo 306, 1974. [Hubel, 1995] David H. Hubel, “Eye, Brain, and Vision” [Ballard, 1982] D. H. Ballard and C. M. Brown, “Computer Vision”, Prentice Hall, 1982.