AI - Lecture 2 .pdf

Intelligent Agent CHAPTER 2  Outline  Agents and environments  Rationality  PEAS (Performance measure, Environment, Actuators, Sensors)  Environment types  Agent types  Agents  An agent is anything that can be viewed as perceiving i...

Intelligent Agent CHAPTER 2  Outline  Agents and environments  Rationality  PEAS (Performance measure, Environment, Actuators, Sensors)  Environment types  Agent types  Agents  An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.  Human agent: eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for effectors.  Robotic agent: cameras and infrared range finders for sensors; various motors for effectors.  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 + program  Intelligent Agent  An intelligent agent is a software entity which senses its environment and then carries out some set of operations on behalf of a user with some amount of autonomy and to do so, it employs some knowledge or representation of the end-user’s goal.  An agent is different from a program; an agent need not to be a program at all. It may be a robot.  Also Software agents are by definition programs…but a program must measure up to several marks to be an agent.  Vacuum-cleaner world Function: if the current square is dirty, then suck the dirt, otherwise, move to the other square.  consider the case of an agent that is supposed to vacuum a dirty floor.  Percepts: location and contents, e.g., [A, Dirty]  Actions: Left, Right, Suck, NoOp  A vacuum-cleaner agent The table is an Percept sequence Action external characterization of [A;Clean] Right the agent. [A;Dirty] Suck [B;Clean] Left [B;Dirty] Suck [A;Clean], [A;Clean] Right [A;Clean], [A;Dirty] Suck … … Internally, the agent function function Reflex-Vacuum-Agent( [location,status]) returns for an an action artificial agent will be if status = Dirty then return Suck implemented by an else if location = A then return Right agent program else if location = B then return Left  Good Behavior: Rational agents  An agent should strive to "do the right thing", based on what it can perceive and the actions it can perform.  The right action is the one that will cause the agent to be most successful.  When an agent is plunked down in an environment, it generates a sequence of actions according to the percepts it receives. This sequence of actions causes the environment to go through a sequence of states. If the sequence is desirable, then the agent has performed well.  Rational agents  Performance measure: An objective criterion for success of an agent's behavior. Obviously, there is not one fixed measure suitable for all agents. Example: Vacuum-cleaner.  performance measure of a vacuum-cleaner agent could be mount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.  As general rule: it is better to design performance measures according to what one actually wants in the environment, rather than according to how one thinks the agent should behave.  Rational agents  Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has. what is rational at any given time depends on four things:  The performance measure that defines degree of success.  Everything that the agent has perceived so far. We will call this complete perceptual history the percept sequence.  What the agent knows about the environment..  The actions that the agent can perform  Rationality  percepts may not supply all relevant information.  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 experience (with ability to learn and adapt)  Characteristics of agents  They are autonomous … can work on their own.  They are persistent over a prolonged time period.  They are adaptive….adjust to change.  They are mobile ….can be transported over network.  They have ability to learn.  Applications of Intelligent Agents (IA)  IA’s are used to access and navigate information using different search engines.  IA ‘s help in decision-making by the knowledge workers.  IA like voice-activated inference agent reduces the user’s task of explicitly commanding the computer.  IAs perform the time consuming and cumbersome tasks of searching database, doing retrievals and filtering of information and sending the result-sets back to the user, in distributed environments.  IA can be used to assist mangers to do their job. Some management-oriented tasks that an agent can do are- advising, alter, browse, distribute, explain, filter, guide, match, monitor, navigate, organize, query, report, search…….  Specifying the task environment To design a rational agent, we must specify the task environment Consider.  PEAS: Performance measure, Environment, Actuators, Sensors  Must first specify the setting for intelligent agent design  Consider, e.g., the task of designing an automated taxi driver:  Performance measure  Environment  Actuators  Sensors  PEAS The task of designing an automated taxi: Performance measure Safe, fast, legal, comfortable trip, maximize profits. Environment Roads (streets/freeways) , traffic, pedestrians, weather, customers. Actuators speaker/display, steering wheel, accelerator, brake, signal, horn. Sensors Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard.  Internet shopping agent Performance measure price, quality, appropriateness, efficiency Environment current and future WWW sites, vendors, shippers Actuators display to user, follow URL, Sensors HTML pages (text, graphics, scripts)  PEAS 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)