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
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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
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