AI - Lecture1402

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    Artificial Intelligence (AI)

    Lecture2Sikandar S. Toorhttps://sites.google.com/site/uolcsai

    Textbook: Artificial Intelligence: A Modern Approachby Peter Norvig & Stuart Russel

    https://sites.google.com/site/uolcsaihttps://sites.google.com/site/uolcsai
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    Intelligent Agent

    An agent is anything that can be viewed asperceiving its environment through sensorsand acting upon that environment through

    actuators.

    General assumption is that every agent canperceive its own actions but not always the

    effects.

    Also calledbot.

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    An Intelligent Agent

    percepts

    actions

    EnvironmentAgent

    ?

    Sensors

    Actuators

    The agents behavior is a mathematical functionthat maps given

    percept sequence to action:

    f : P* A

    P* = Percept History

    A = Actions

    The a ent ro ramruns on the h sical architecture to roduce f

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    Agents - Examples

    A human agent has eyes, ears, and other organs forsensors and hands, legs, mouth and other body parts foractuators

    A robotic agent substitutes cameras and infrared rangefinders for the sensors and various motors for theactuators.

    A software agent receives keystrokes, network packets,file contents as sensory input and acts upon the

    environment by displaying on screen, sending networkpackets and writing files.

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    Percept & Percept Sequence

    Percept refers to agents perceptual inputat any given instance

    Percept sequenceis the complete history ofeverything agent has ever perceived

    An agents choice of action at any giveninstance can depend on the entire percept

    sequence observed to-date

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    Agent Function

    Mathematically, an agents behavior is described by theagent functionthat maps any given percept sequence toan action

    We may tabulate agent function that describe any given agent

    For most agent this would become a very large table. May be ofinfinite size

    We want to put a bound on the length of the percept sequence wewant to consider

    This table is an external characterization of an agent Internally, agent function for an artificial agent will be

    implementation by an agent program

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    Vacuum-Cleaner Agent

    Locations: square A, square B

    Percepts: location and contents, e.g.,[A,Dirty]

    Actions: Left, Right, Suck, NoOp

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    Vacuum-Cleaner Agent

    Percept Sequence Action

    [A, Clean] Right

    [A, Dirty] Suck

    [B, Clean] Left

    [B, Dirty] Suck

    [A, Clean], [A, Clean] Right

    [A, Clean], [A, Dirty] Suck

    .

    .

    .

    .[A, Clean], [A, Clean], [A, Clean] Right

    [A, Clean], [A, Clean], [A, Dirty] Suck

    .

    ...

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    Good Behavior - Rationality

    Rational Agent is the one which does the rightthing

    The right action is the one that will cause theagent to be most successful

    The environment shall change with agentssequence of actions

    If the sequence is desirable, the agent hasperformed well

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    Performance Measure

    Performance measureis an objective criterion for successof an agent's behavior

    Performance measure can be defined by the designer whoconstructed the agent

    e.g., performance measure of a vacuum-cleaner agentcould be amount of dirt cleaned up, amount of time taken,amount of electricity consumed, amount of noisegenerated, etc

    No one universal criterion of performance measure forevery agent

    We could ask the agent for a subjective opinion of howhappy it is with its own performance

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    Performance Measure

    The selection of performance measure is notalways easy

    As a general rule, it is better to design a

    performance measure according to what oneactually wants in the environment rather thanaccording to how one thinks the agent shouldbehave

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    Rationality

    What is rational at given time depends uponfour things

    The performance measure that defines criterion

    for success

    The agents prior knowledge of the environment

    The actions that agent can perform

    The agents percept sequence to-date

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    Rational Agent

    For each possible percept sequence, arational agent should select an action that isexpected to maximize its performancemeasure, given the evidence provided bythe percept sequence and whatever built-inknowledge the agent has.

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    Omniscience

    An omniscience agent knows the actualoutcomes of its actions and can actaccordingly

    Omniscience is impossible in reality

    Omniscience is different from rationality

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    Perfection, Learning

    Rationality is NOT the same as Perfection

    Rationality maximizes expected performance

    Perfection maximizes actual performanceA rational agent not only gather information

    but also learnas much as possible from what

    it perceives

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    Exploration

    Doing actions in order to modify futurepercepts, sometimes called informationgathering, is an important part of rationality

    Information gathering is an important part ofrationality

    It performs such actions to increase its

    perception

    This is called Exploration

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    Learning

    We do not want our rational agent to gatherinformation only

    We also want it to learn as much as it can from

    what it perceives Successful agents split the task of computing agent

    function (learning) into 3 different periods

    When designing

    When agent is deliberating on its next action

    When it learns from experience

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    Agent Autonomy

    To what extent the agent is able to make decisionsand take actions on its own

    The capacity to compensate for partial or incorrect

    prior knowledge by learning An agent is called autonomous if its behavior is

    determined by its own experience (with ability tolearn and adopt)

    A truly autonomous agent should be able tooperate successfully in a wide variety ofenvironments

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    Task Environment

    The task environment are essentially the problemsfor which the agent is solution

    PEAS

    PPerformance Measure E - Environment

    AActuators

    SSensors

    First step in designing an agent must be todefine the task environment

    19

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    PEAS - Example

    Automated Taxi Driver Agent Performance measure: Safe, correct destination,

    minimum fuel consumption, min wear and tear, fast,legal, comfortable trip, maximize profit

    Environment: Roads, other traffic, pedestrians,customers, stray animals, police cars, signals, potholes

    Actuators: Steering wheel, accelerator, brake, signal,horn, display, voice synthesizer

    Sensors: Cameras, sonar, speedometer, accelerometer,GPS, odometer, engine sensors, keyboard, mic

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    Agent Type and PEAS

    Agent Type PerformanceMeasures

    Environment Actuators Sensors

    Medical Diagnostic Healthy patients,minimize costs,lawsuits

    Patients, hospital,staff

    Display questions,tests, diagnoses,treatments,referrals

    Keyboard entry ofsymptoms,findings, patientsanswers

    Satellite imageanalysis system

    Correct imagecharacterization

    Downlink fromorbiting satellite

    Displaycategorization ofscene

    Color pixel arrays

    Part picking robot Percentage of partsin correct bins

    Conveyor belt withparts, bins

    Valves, pumps,heaters, displays

    Cameras, jointangle sensors

    Refinery controller Maximize purity,yield, safety

    Refinery, operators Valves, pumps,heaters, displays

    Temperature,pressure, chemicalsensors