AI Intelligent Agents

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PSUCS 370Artificial Intelligence Dr. Mohamed Tounsi

Artificial Intelligence

2. Intelligent Agents

Dr. M. Tounsi


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Definition

Is anything:

perceiving its environment through sensors

acting upon its environment through effectors

Example:With Robotic agent

Sensors: Cameras and infrared

Effectors: various motors


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Definitions and Concepts

Rational Agent One that does the right thing (most successful!)

Issue:how and when to evaluate the agents success ?

Performance Measure

Criteria that determines how successful an agent is

Percept Sequence

Everything that the agent has perceived so far

Ideal Rational Agent

Should do whatever action is expected to maximize itsperformance measure based on percept sequence and whateverbuild-in knowledge the agent has


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Rational agents

An agent should strive to "do the right thing", based onwhat it can perceive and the actions it can perform. Theright action is the one that will cause the agent to be mostsuccessful

Performance measure: An objective criterion forsuccess of an agent's behavior

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.


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PEAS

PEAS: Performance measure, Environment,Actuators, Sensors

Definition: Actuator = Effector

Must first specify the setting for intelligent agent design

Example: the task of 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 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)



PEAS

Agent: Part-picking robot

Performance measure: Percentage of parts incorrect bins

Environment: Conveyor belt with parts, bins Actuators: Jointed arm and hand

Sensors: Camera, joint angle sensors



PEAS

Agent: Interactive English tutor

Performance measure: Maximize student's scoreon test

Environment: Set of students Actuators: Screen display (exercises,

suggestions, corrections)

Sensors: Keyboard



Agent functions and programs

An agent is represented by the agent functionwhich maps percept sequencesto actions

Aim: find a way to implement the rational agentfunction concisely



Table-lookup agent

FunctionTable-Agent(percept) returnsan ActionStatic:percepts// sequence, initially empty

Table // table of actions, indexed bypercept, initially fully specified

Begin

Appendperept to the end of the perceptsAction := LOOKUP(percepts, Table)

returnAction

End

Drawbacks:

Huge table

Take a long time to build the table

No autonomy

Even with learning, need a long time to learnthe table entries



Program Agent

FunctionSKELETON-AGENT (percept)

Static: memory;

memory



Agent types

Four basic types in order of increasing generality:

1. Simple reflex agents

respond immediately to percepts (i.e.: rules)2. Model-based reflex agents

3. Goal-based agentsact so that they will achieve their goal(s)

4. Utility-based agentstry to maximize their own happiness



Example: Simplex reflex agent

FunctionReflex-Vacum_agent(location, status)return actions

{

If status = dirty then action= Clean_ON;else {

if location = A then action=right;

else action = left;

}

}


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If the agent is based completely on the built-inknowledge part, such that it pays no attention to itspercepts, then we say that the agent lacks autonomy

If the behavior of the agent is determined by only its own

experience the system is autonomous

Autonomy

AI Agent should have initial knowledge as well as anability to learn

AI Intelligent Agents

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