2. 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

3. 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.

4. Agent Function & program  An agent is specified by an agent function f that maps sequences of percepts Y to actions 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

5. Agents and Environments  The agent function maps from percept histories (sequences of percepts) to actions: [f: P*  A]

6. Example: A Vacuum-Cleaner Agent AB  Percepts: location and contents, e.g., (A,dust) (Idealization: locations are discrete)  Actions: move, clean, do nothing: LEFT, RIGHT, SUCK, NOP

7. Example: A Vacuum-Cleaner Agent

8. 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

9. 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

10. 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.

11. 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.

12. 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 solution  PEAS: to specify a task environment Performance measure Environment Actuators Sensors

13. 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

14. 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)

15. 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.