Chap2 Agent

7/28/2019 Chap2 Agent

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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

Documents

Chap2 Agent