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Artificial Intelligence
2. Intelligent Agents
Dr. M. Tounsi
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
Definition
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Is anything:
 perceiving its environment through sensors
 acting upon its environment through effectors
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Example: With Robotic agent
 Sensors: Cameras and infrared
 Effectors: various motors
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
Definitions and Concepts
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Rational Agent
One that does the right thing (most successful!)
 Issue: how and when to evaluate the agent’s success ?
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Performance Measure
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Criteria that determines how successful an agent is
Percept Sequence
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Everything that the agent has perceived so far
 Ideal Rational Agent
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Should do whatever action is expected to maximize its
performance measure based on percept sequence and whatever
build-in knowledge the agent has
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
Rational agents
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An agent should strive to "do the right thing", based on
what it can perceive and the actions it can perform. The
right action is the one that will cause the agent to be most
successful
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Performance measure: An objective criterion for
success of an agent's behavior
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E.g., performance measure of a vacuum-cleaner agent
could be amount of dirt cleaned up, amount of time taken,
amount of electricity consumed, amount of noise
generated, etc.
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
PEAS
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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:
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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
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
PEAS
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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)
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
PEAS
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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
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
PEAS
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Agent: Interactive English tutor
Performance measure: Maximize student's score
on test
Environment: Set of students
Actuators: Screen display (exercises,
suggestions, corrections)
Sensors: Keyboard
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
Agent functions and programs
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An agent is represented by the agent function
which maps percept sequences to actions
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Aim: find a way to implement the rational agent
function concisely
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
Table-lookup agent
Function Table-Agent(percept) returns an Action
Static: percepts // sequence, initially empty
Table // table of actions, indexed by
percept, initially fully specified
Begin
Append perept to the end of the percepts
Action := LOOKUP(percepts, Table)
return Action
End
Drawbacks:
 Huge table
 Take a long time to build the table
 No autonomy
 Even with learning, need a long time to learn
the table entries
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
Program Agent
The design of the agent depends on the percepts, actions, goals
and environment
Function SKELETON-AGENT (percept)
Static: memory;
memory <- Update-Mem (memory, percept)
action <- Choose-Best-Action (Memory)
Memory <- Update-Memory(memory, action)
Return action
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
Agent types
Four basic types in order of increasing generality:
1.
Simple reflex agents
respond immediately to percepts (i.e.: rules)
2.
3.
Model-based reflex agents
Goal-based agents
act so that they will achieve their goal(s)
4.
Utility-based agents
try to maximize their own happiness
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
Example: Simplex reflex agent
Function Reflex-Vacum_agent(location, status)
return actions
{
If status = dirty then action= Clean_ON;
else {
if location = A then action=right;
else action = left;
}
}
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU
Autonomy
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If the agent is based completely on the “built-in
knowledge” part, such that it pays no attention to its
percepts, 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
AI Agent should have initial knowledge as well as an
ability to learn
CS 370 – Artificial Intelligence
Dr. Mohamed Tounsi
PSU