Intelligence Agent - Artificial Intelligent (AI)

Intelligence Agent - Artificial Intelligent (AI)

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  Artificial Intelligence Mr. MMM.Mufassirin Dept. of Mathematical Sciences South Eastern University of Sri Lanka Lecture - 02 Intelligent Agents
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  Intelligent Agents
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  Outline • Agents andenvironments • Rationality • Intelligence Agent • PEAS (Performance measure, Environment, Actuators, Sensors) • Environment types • Agent types
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  Agents • An agentis anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators – Operates in an environment – Perceive its environment through sensors – Acts upon its environment through actuators/ effectors – Has Goals
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  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
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  Sensor and Effectors •An agent perceives its environment through sensors The complete set of inputs at a given time is called a percept The current percept or Sequence of percepts can influence the action of an agent • It can change the environment through actuators/ effectors  An operation involving an actuator is called an action  Action can be grouped into action sequences
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  Examples of Agents HumansPrograms Robots___ senses keyboard, mouse, dataset cameras, pads body parts monitor, speakers, files motors, limbs
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  Ex:  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, wheels, and speakers for actuators  A software agent: function (Input) as sensors — function as actuators (output-Screening) Examples of Agents (cont..)
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  Vacuum-Cleaner World • Percepts:location and contents, e.g., [A,Dirty] • Actions: Left, Right, Suck, NoOp
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  A vacuum-cleaner agent •input{tables/vacuum-agent-function-table}
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  Rationality What is rationalat 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 that the agent can perform. – The agent's percept sequence to date.
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  Rational agents • Arational agent is one that does the right thing. • 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. • An agent's percept sequence is the complete history of everything the agent has ever perceived
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  Rational agents (cont..) •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)
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  Autonomy in Agents Theautonomy of an agent is the extent to which its behaviour is determined by its own experience (with ability to learn and adapt) • Extremes – No autonomy – ignores environment/data – Complete autonomy – must act randomly/no program • Example: baby learning to crawl • Ideal: design agents to have some autonomy – Possibly good to become more autonomous in time
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  Intelligence Agent • Mustsense • Must act • Must autonomous (to some extend) • Must rational
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  PEAS • PEAS: Performancemeasure, 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
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  PEAS • The taskof designing an automated 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, keyboard
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  PEAS Agent: Medical diagnosissystem • 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)
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  PEAS 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 sensors
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  PEAS Agent: Interactive Englishtutor • Performance measure: Maximize student's score on test • Environment: Set of students • Actuators: Screen display (exercises, suggestions, corrections) • Sensors: Keyboard
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  Types of theEnvironment
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  23 Environments – Accessible vs.inaccessible • An accessible environment is one in which the agent can obtain complete, accurate, up-to-date information about the environment’s state • Most moderately complex environments (for example, the everyday physical world and the Internet) are inaccessible • The more accessible an environment is, the simpler it is to build agents to operate in it
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  24 Environments – Deterministic vs.non-deterministic • A deterministic environment is one in which the next state of the environment is completely determined by the current state and the action executed by the agent. • The physical world can to all intents and purposes be regarded as non-deterministic • Non-deterministic environments present greater problems for the agent designer
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  25 Environments – Episodic vs.non-episodic • In an episodic environment, the performance of an agent is dependent on a number of discrete episodes, with no link between the performance of an agent in different scenarios • Episodic environments are simpler from the agent developer’s perspective because the agent can decide what action to perform based only on the current episode — it need not reason about the interactions between this and future episodes
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  26 Environments – Static vs.dynamic • A static environment is unchanged while an agent is reflecting. • A dynamic environment is one that has other processes operating on it, and which hence changes in ways beyond the agent’s control • Other processes can interfere with the agent’s actions (as in concurrent systems theory) • The physical world is a highly dynamic environment
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  27 Environments – Discrete vs.continuous • An environment is discrete if there are a fixed, finite number of actions and percepts in it – Ex: chess game • Continuous environments have a certain level of mismatch with computer systems – Ex: taxi driving • Discrete environments could in principle be handled by a kind of “lookup table”
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  Discussion ?
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  Review Question • Definein your own words the following terms: – agent, agent function, agent program, rationality, autonomy, • For each of the following agents, develop a PEAS description of the task environment – Part-picking robot, Taxi Driver • Describe the following types of the environment – Static vs. Dynamic – Deterministic vs. non-deterministic • List 4 most complex environments in which constructing agent is very difficult. • What is Omniscience?