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Intelligent Agent
Chapter 2
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Outline
Agent and Environment
Rationality
Performance Measure
Environment Type
Agent Type
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Agent interacting with Environment
Agents include Human, robots, softbots, thermostats, etc.
The agent function maps from percept histories to actions
The agent program runs on the physical architecture to produce f
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Fig 2.3
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Rational AgentCharacteristics of Rational Agent
tries to maximize expected value of performance measure
performance measure = degree of success
on the basis of evidence obtained by percept sequence
using its built-in prior world knowledge
Rational Agent : agent that does the RIGHT things
Rational not= Omniscient, clairvoyant, successfulRight Decision vs. Lucky decision
example : Playing lotto
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RationalityRationality Information-Gathering, learning,autonomy
Information-Gathering
Modify future perceptsExploration unknown environment
LearningModify prior knowledge
Autonomy
Learn to compensate for partial and incorrect prior knowledgeBecome independent of prior knowledge
Successful in variety of environment
importance of learning
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Performance Measure, Environment,
Actuators, Sensors
To design a rational agent, we must specify task
environment which consists of PEAS (Performance
Measure, Environment, Actuators, Sensors)
Taxi Driver
Performance measure : safety, fast, legal, confortable trip,
maximize profits
Environment : Roads, other traffic, pedestrains, customers
Actuators : steering, accelerator, brake, signal, horn, display
Sensors : cameras, sonar, speedometer, GPS, odometer,
accelerometer, engine sensors, keyboard or microphone to accept
destination
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Internet Shopping Agent
Performance Measure :
Environment :
Actuators :
Sensors :
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Properties of Task Environments
Fully observable vs. Partially observable
Deterministic vs. Stochastic
Strategic : deterministic except for actions of other
agents
Episodic vs. Sequential
Static vs. DynamicDiscrete vs. Continuous
Single Agents vs. Multiagent
Competitive, cooperative
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Task Environment Types
8-puzzle BackgammonInternet
Shopping
Medical
diagnosis
Taxi
driving
Observable ?
Deterministic ?
Episodic ?
Static ?
Discrete ?
Single-agent ?
Real world is
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2-3. Structure of Intelligent Agents
perception action?
Agent = architecture + program
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Types of agentsFour basic types
Simple Reflex Agent
Reflex Agent with state that keeps track of the world
Also called model-based reflex agent
Goal-based agent
Utility-based agent
All these can be turned into Learning Agents
Generality
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(1) Simple Reflex Agentcharacteristics
no plan, no goal
do not know what they want to achieve
do not know what they are doing
condition-action rule
Ifcondition then action
architecture - [fig. 2.9]
program - [fig. 2.10]
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Fig 2.9 Simple reflex agent
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Fig 2.10 Simple Reflex Agent
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(2) Model-based Reflex AgentsCharacteristics
Reflex agent with internal state
Sensor does not provide the complete state of the world.
Updating the internal world
requires two kinds of knowledge which is called model
How world evolves
How agents action affect the world
architecture - [fig 2.11]
program - [fig 2.12]
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Fig 2.11 A model-based Agent
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(3) Goal-based agentsCharacteristics
Action depends on the GOAL . (consideration of future)
Goal is desirable situation
Choose action sequence to achieve goal
Needs decision making
fundamentally different from the condition-action rule.
Search and Planning
Appears less efficient, but more flexibleBecause knowledge can be provided explicitly and modified
Architecture - [fig 2.13]
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Fig 2.13 A model-based, Goal-based Agent
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(4) Utility-based agentsUtility function
Degree of happiness
Quality of usefulness
map the internal states to a real number (e.g., game playing)
Characteristics
to generate high-quality behavior
Rational decisions are made
Looking for higher Utility value
Expected Utility Maximizer
Explore several goals
Structure - [fig 2.14]
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Fig 2.14 A Model-based, Utility-based Agent
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Learning AgentsImprove performance based on the percepts
4 components
Learning elements Making improvement
Performance elements
Selecting external actions
Critic
Tells how well the agent doing based on fixed performancestandard
Problem generator
Suggest exploratory actions
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General Model of Learning Agents
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