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January 11, 2006 AI: Chapter 2: Intelligent Agents
1
Artificial IntelligenceChapter 2: Intelligent
AgentsMichael Scherger
Department of Computer Science
Kent State University
January 11, 2006 AI: Chapter 2: Intelligent Agents
2
Agents and Environments
• An Agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
Agent
Enviro
nm
ent
Percepts
Actions
?
Sensors
Actuators
January 11, 2006 AI: Chapter 2: Intelligent Agents
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Agents and Environments
• Percept – the agent’s perceptual inputs– percept sequence is a sequence of everything
the agent has ever perceived
• Agent Function – describes the agent’s behavior– Maps any given percept sequence to an action– f : P* -> A
• Agent Program – an implementation of an agent function for an artificial agent
January 11, 2006 AI: Chapter 2: Intelligent Agents
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Agents and Environments
• Example: Vacuum Cleaner World– Two locations: squares A
and B– Perceives what square it is
in– Perceives if there is dirt in
the current square– Actions
• move left• move right• suck up the dirt• do nothing
A B
January 11, 2006 AI: Chapter 2: Intelligent Agents
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Agents and Environments
• Agent Function: Vacuum Cleaner World– If the current
square is dirty, then suck, otherwise move to the other square
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
January 11, 2006 AI: Chapter 2: Intelligent Agents
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Agents and Environments
• But what is the right way to fill out the table?– is the agent
• good or bad• intelligent or stupid
– can it be implemented in a small program?
Function Reflex-Vacuum-Agent([location, status]) return an action
if status == Dirty then return Suck
else if location = A then return Right
else if location = B then return Left
January 11, 2006 AI: Chapter 2: Intelligent Agents
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Good Behavior and Rationality
• Rational Agent – an agent that does the “right” thing– Every entry in the table for the agent
function is filled out correctly– Doing the right thing is better than
doing the wrong thing• What does it mean to do the right thing?
January 11, 2006 AI: Chapter 2: Intelligent Agents
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Good Behavior and Rationality
• Performance Measure– A scoring function for evaluating the
environment space
• Rational Agent – for each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and what ever built-in knowledge the agent has.
January 11, 2006 AI: Chapter 2: Intelligent Agents
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Good Behavior and Rationality
• Rational != omniscient• Rational != clairvoyant• Rational != successful
• Rational -> exploration, learning, autonomy
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Nature of Environments
• Task environments– The “problems” to which a rational
agent is the “solution”
• PEAS– Performance– Environment– Actuators– Sensors
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Nature of Environments
• Properties of task environments– Fully Observable vs. Partially Observable– Deterministic vs. Stochastic– Episodic vs. Sequential– Static vs. Dynamic– Discrete vs. Continuous– Single agent vs. Multi-agent
• The real world is partially observable, stochastic, sequential, dynamic, continuous, multi-agent
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Nature of Environments
• Examples– Solitaire– Backgammon– Automated Taxi– Mars Rover
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Structure of Agents
• Agent = Architecture + Program
• Basic algorithm for a rational agent– While (true) do
• Get percept from sensors into memory• Determine best action based on memory• Record action in memory• Perform action
• Most AI programs are a variation of this theme
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Structure of Agents
• Table Driven Agent
function Table-Driven-Agent (percept) return action
static: percepts, a sequence, initially empty
table, a table of actions, indexed by percept sequences, initially fully specified
append percept to the end of the table
action <- LOOKUP( percept, table )
return action
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Structure of Agents
Simple Reflex Agent
Enviro
nm
ent
Percepts
Actions
What the world is like now
Sensors
ActuatorsWhat action I should do now
Condition-ActionRules
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Structure of Agents
• Simple Reflex Agent
function Simple-Reflex-Agent (percept) return action
static: rules, a set of condition-action rules
state <- INTERPRET-INPUT( percept )
rule <- RULE-MATCH( state, rules )
action <- RULE-ACTION[ rule ]
return action
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Structure of Agents
Reflex Agent With State
Enviro
nm
ent
Percepts
Actions
What the world is like now
Sensors
ActuatorsWhat action I should do now
Condition-ActionRules
State
How the world evolves
What my actions do
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Structure of Agents
• Reflex Agent With State
function Reflex-Agent-With-State (percept) return action
static: state, a description of the current world state
rules, a set of condition-action rules
action, the most recent action, initially none
state <- UPDATE-STATE( state, action, percept )
rule <- RULE-MATCH( state, rules )
action <- RULE-ACTION[ rule ]
return action
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Structure of Agents
Goal Based Agent
Enviro
nm
ent
Percepts
Actions
What the world is like now
Sensors
ActuatorsWhat action I should do now
Goals
State
How the world evolves
What my actions doWhat it will be like
if I do action A
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Structure of Agents
Utility Based Agent
Enviro
nm
ent
Percepts
Actions
What the world is like now
Sensors
ActuatorsWhat action I should do now
Utility
State
How the world evolves
What my actions doWhat it will be like
if I do action A
How happy I will be in such a state
January 11, 2006 AI: Chapter 2: Intelligent Agents
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The Structure of Agents
Learning Based Agent
Enviro
nm
ent
Percepts
Actions
Critic(external
performance standard)
Sensors
Actuators
PerformanceElement
LearningElement
ProblemGenerator
changes
knowledge
feedback
learning goals