CS:4420 Artificial Intelligence Spring 2017 Intelligent Agents Cesare Tinelli The University of Iowa Copyright 2004–17, Cesare Tinelli and Stuart Russell a a These notes were originally developed by Stuart Russell and are used with permission. They are copyrighted material and may not be used in other course settings outside of the University of Iowa in their current or modified form without the express written consent of the copyright holders. CS:4420 Spring 2017 – p.1/36
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CS:4420 Artificial Intelligence
Spring 2017
Intelligent Agents
Cesare Tinelli
The University of Iowa
Copyright 2004–17, Cesare Tinelli and Stuart Russell a
aThese notes were originally developed by Stuart Russell and are used with permission. They are
copyrighted material and may not be used in other course settings outside of the University of Iowa in their
current or modified form without the express written consent of the copyright holders.
CS:4420 Spring 2017 – p.1/36
Readings
• Chap. 2 of [Russell and Norvig, 2012]
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Intelligent Agents
• An agent is a system that perceives its environment throughsensors and acts upon that environment through effectors.
• A rational agent is an agent whose acts try to maximize someperformance measure.
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Agents and Environments
Agent Sensors
Actuators
Environm
ent
Percepts
Actions
?
Agents include humans, robots, softbots, thermostats, etc.
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Agents as Mappings
An agent can be seen as a mapping between percept sequences andactions.
f : Percept∗ −→ Action
The agent program runs on a physical architecture to produce f
The less an agents relies on its built-in knowledge, as opposed to thecurrent percept sequence, the more autonomous it is
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Vacuum-cleaner world
A B
Percepts: location and contents, e.g., [A,Dirty ]
Actions: Left , Right , Suck , NoOp
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A 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
.
.
....
function Reflex-Vacuum-Agent( [location,status]) returns action
if status = Dirty then return Suck
else if location = A then return Right
else if location = B then return Left
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More Examples of Artificial Agents
Agent Type Percepts Actions Goals Environment
Medical diagnosissystem
Symptoms,findings, patient’sanswers
Questions, tests,treatments
Healthy patient,minimize costs
Patient, hospital
Satellite imageanalysis system
Pixels of varyingintensity, color
Print acategorization ofscene
Correctcategorization
Images fromorbiting satellite
Part-picking robot Pixels of varyingintensity
Pick up parts andsort into bins
Place parts incorrect bins
Conveyor beltwith parts
Refinery controller Temperature,pressure readings
Open, closevalves; adjusttemperature
Maximize purity,yield, safety
Refinery
Interactive Englishtutor
Typed words Print exercises,suggestions,corrections
Maximizestudent’s score ontest
Set of students
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Rational Agents
The rationality of an agent depends on
• the performance measure defining the agent’s degree of success
• the percept sequence, the sequence of all the things perceived bythe agent
• the agent’s knowledge of the environment
• the actions that the agent can perform
For each possible percept sequence, an ideal rational agent doeswhatever possible to maximize its performance, based on the perceptsequence and its built-in knowledge.
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Rationality
• What is the right function?
• Can it be implemented in a small agent program?
• Fixed performance measure evaluates the environment sequence• one point per square cleaned up in time T?• one point per clean square per time step, minus one per
move?• penalize for > k dirty squares?
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Rationality
• What is the right function?
• Can it be implemented in a small agent program?
• Fixed performance measure evaluates the environment sequence• one point per square cleaned up in time T?• one point per clean square per time step, minus one per
move?• penalize for > k dirty squares?
• Rational 6= omniscient
• Rational 6= clairvoyant
• Rational 6= successful
• Rational =⇒ exploration, learning, autonomy
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PEAS
To design a rational agent, we must specify the task environment
Consider, e.g., the task of designing an automated taxi:
• Performance measure?
• Environment?
• Actuators?
• Sensors?
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PEAS
To design a rational agent, we must specify the task environment
Consider, e.g., the task of designing an automated taxi: