02-IntelligentAgents

8/6/2019 02-IntelligentAgents

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Intelligent Agents Christian Jacob

TOC 1 Back

1 Defining Agents 2

2 How Agents Should Act 3

2.1 Mapping from Percept Sequences to Actions 5

2.2 Autonomy 6

3 Designs of Intelligent Agents 7

3.1 Architecture and Program 7

3.2 Agent Programs 9

3.3 Simple Lookup? 11

3.4 Example An Automated Taxi Driver 13

4 Types of Agents 15

4.1 Simple Reflex Agents 16

4.2 Agents that Keep Track of the World 19

4.3 Goal-Based Agents 224.4 Utility-Based Agents 24

5 Environments 26

5.1 Properties of Environments 26

5.2 Environment Programs 29
http://../CPSC-231%20Outline.pdfhttp://../CPSC-231%20Outline.pdf


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Defining Agents Christian Jacob

First Back TOC 2 Prev Next Last

1 Defining Agents

An agent is anything that can be viewed as perceiving its environment throughsensors and acting upon that environment through effectors.

environmentagent

sensors

effectors

actions

percepts

?
http://goback/http://goback/


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How Agents Should Act Christian Jacob


2 How Agents Should Act

Rational Agent

A rational agent performs actions that cause the agent to be most successful,depending on a performance measure, which decides howand whento evaluatean agent.

What is rational at any given time depends on four things:

The performance measure that defines degree of success

The percept sequence, a complete history of what the agent has perceived sofar

The agents knowledge about the environment

The actions that the agent can perform

This leads to a definition of an ideal rational agent ...


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Ideal Rational Agent

For each possible percept sequence, an ideal rational agent should do

whatever action is expected to maximize its performance measure,

on the basis of the evidence provided by the percept sequence and

whatever built-in knowledge the agent has.

Example: Crossing a busy road

Doing actions in order to obtain useful informationis an important part of rationality.

The notion of an agent is meant to be a tool for analyzing systems.


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2.1 Mapping from Percept Sequences to Actions

Any particular agent can be described by making a table of the action it takes inresponse to each possible percept sequence.

Such a list is called a mapping from percept sequences to actions.

However, this does not mean that we have to create an explicit table with an entry forevery possible percept sequence (compare the square root example).

Percept x Action z

1.0 1.0000001.1 1.0488081.2 1.0954451.3 1.1401751.4 1.1832151.5 1.2247441.6 1.2649111.7 1.3038401.8 1.3416401.9 1.378404...

function SQRT(x)

z := 1.0 /* initial guess */

repeat until | z2 - x | < 10-15

z := z - (z2 - x) / ( 2 z )

end

returnz


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2.2 Autonomy

Assume an agents actions are based completely on built-in knowledge.

Then it need not pay attention to its percepts.

This agent clearly lacks autonomy.

An agents behavior can be based on both its

own experience and

built-in knowledge

A system is autonomous to the extent that its behavior is determined by its ownexperience.

--> Agents should be provided with

initial knowledge (compare animal reflexes) and

the ability to learn.


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Designs of Intelligent Agents Christian Jacob


3 Designs of Intelligent Agents

AI intends to design agent programs, functions that implement the agent mappingfrom percepts to actions.

The computing device, we assume this program to run on, is referred to as thearchitecture.

3.1 Architecture and Program

The architecture might include special-purpose hardware (camera,microphone,etc.).

The software might provide a degree of insulation between the raw computerhardware and the agent program, enabling programming at a higher level.

agent = architecture + program

What matters is not the distinction between real and artificial environments, butthe complexityof the relationship among the behavior of the agent, the perceptsequence generated by the environment, and the goals that the agent is supposedto achieve.


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Percepts and Actions for a Selection of Agent Types

Agent Type Percepts Actions Goals Environment

Medical diagno-

sis system

Symptoms, find-

ings,

patients answers

Questions,

tests,

treatments

Healthy patient,

minimize cost

Patient,

hospital

Satellite image

analysis system

Pixels of varying

intensity,

color

Print a categoriza-

tion of scene

Correct catego-

rization

Image from

orbiting satellite

Part-picking

robot

Pixels of varying

intensity

Pick up parts and sort

into bins

Place parts in

correct bins

Conveyor belt

with parts

Refinery control-

ler

Temperature,

pressure readings

Open, close valves;

adjust temperature

Maximizing

purity, yield,

safety

Refinery

InteractiveEnglish tutor

Typed words Print exercises,suggestions,

corrections

Maximize stu-dents score on

test

Set of students


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3.2 Agent Programs

All agents and agent programs accept percepts from an environment and generateactions.

function SKELETON-AGENT( percept) returnsaction

static: memory the agents memory of the world

memory


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Remarks on agent programs:

Percept sequence

The agent program receives only a singlepercept as its input.

It is up to the agent to build up the percept sequence in memory.

Performance measure

The goal or performance measure is notpart of the agent program.

The performance evaluation is applied externally.


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3.3 Simple Lookup?

A lookup table is the simplest possible way of writing an agent program.

It operates by keeping in memory its entire percept sequence, and using it to indexinto table, which contains the appropriate action for all possible percept sequences.

function TABLE-DRIVEN-AGENT( percept) returnsaction

static: percepts, a sequence, initially empty

table, a table, indexed by percept sequences, initially fully specified

append perceptto the end of percepts

action


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Why is this TABLE-DRIVEN AGENT proposal doomed to failure?

Table size:The table needed for something as simple as an agent that can only play chesswould be about 35100 entries.

Time to build:It would take an enormous amount of time to build complete tables.

Lack of autonomy:The agent has no autonomy at all, because the calculation of best actions isentirely built-in.

If the environment changed in some unexpected way, the agent would be lost.

Lack or infeasibility of learning:Even if the agent were given a learning mechanism, so that it could have adegree of autonomy, it would take forever to learn the right value for all the table

entries.


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3.4 Example An Automated Taxi Driver

The full driving task is extremely open-ended. There is no limit to the novel

combination of circumstances that can arise.

First, we have to think about the percepts, actions, goals and environment for thetaxi.

Agent Type Percepts Actions Goals Environment

Taxi driver Cameras,

speedometer,

GPS,

sonar,

microphone

Steer,

accelerate,

brake,

talk to passenger,

communicate with

other vehicles

Safe,

fast,

legal,

comfortable trip,

maximize profits

Roads,

other traffic,

pedestrians,

customers


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Performance measures for the taxi driver agent:

Getting to the correct destination

Minimizing fuel consumption and wear and tear

Minimizing the trip time and/or cost

Minimizing violations of traffic laws

Minimizing disturbances to other drivers

Maximizing safety and passenger comfort

Maximizing profits

Obviously, some of these goals conflict, so there will be trade-offs involved.

Driving environments:

local roads or highways weather conditions

left or right lane driving


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Types of Agents Christian Jacob


4 Types of Agents

We have to decide how to build a real program to implement the mapping frompercepts to action for the taxi driver agent.

Different aspects of driving suggest different types of agent programs.

We will consider four types of agent programs:

Simple reflex agents

Agents that keep track of the world

Goal-based agents

Utility-based agents


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4.1 Simple Reflex Agents

Instead of constructing a lookup table for the percept-action mapping, we can

summarize portions of the table by noting certain commonly occurring input/output associations.

This can be accomplished by condition-action rules.

Example:

ifcar-in-front-is-breakingtheninitiate-braking

Humans (and animals in general) have many such connections,

some of which are learnedresponses (e.g., driving) and

some of which are innate reflexes.


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Schematic diagram of a simple reflex agent

Environment

Agent

Sensors

Effectors

What the worldis like now

What action Ishould do now

Condition-action rules


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A simple reflex agent

function SIMPLE-REFLEX-AGENT( percept) returnsaction

static: rules, a set of condition-action rules

state


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4.2 Agents that Keep Track of the World

For determining whether a vehicle is braking one has to keep the previous frame

from the camera to detect when two red lights at the edge of the vehicle go on or offsimultaneously.

Hence, the driving agent will have to maintain some sort of internal state.

Two kinds of knowledgehave to be encoded in the agent program:

Information about how the world evolves independently of the agent.

Information about how the agents own actions will affect the world.


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Schematic diagram of a reflex agent with internal state

Environment

Agent

Sensors

Effectors



Condition-action rules

State

How the world evolves

What my actions do


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A reflex agent with internal state

function REFLEX-AGENT-WITH-STATE( percept) returnsaction

static: state, a description of the current world state

rules, a set of condition-action rules

state


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4.3 Goal-Based Agents

Besides a current state description the agent needs some sort of goal information,

which describes situations that are desirable.

The agent program can combine this with information about the results of possibleactions in order to choose actions that achieve the goal.

Achieving the goal may involve

a single action or

(long) sequences of actions.

The subfields of Ai devoted to finding action sequences that do achieve agentsgoals are

searching and

planning.

Goal-based agents involve consideration of the futureand is more flexibly reacting tochanges in the environment.


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Schematic diagram of an agent with explicit goals

Environment

Agent

Sensors

Effectors



Goals

State


What my actions do

What it will be likeif I do action A


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4.4 Utility-Based Agents

Goals alone are not really enough to generate high-quality behavior.

There might be different ways (action sequences) of achieving a specific goal.

If one world state is preferred to another, then it has higher utility for the agent.

Utility is therefore a function that maps a state onto a real number, which describesthe associated degree of happiness.

A complete specification of the utility function allows rational decisions in two kinds ofcases:

When there are conflicting goals, only some of which can be achieved, theutility function specifies the appropriate trade-off.

When there are several goals that the agent can aim for, none of which can be

achieved with certainty, utility provides a way in which the likelihood ofsuccess can be weighed up against the importance of the goals.


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Schematic diagram of a complete utilty-based agent

Environment

Agent

Sensors

Effectors



Utility

State


What my actions do

What it will be likeif I do action A

How happy I willbe in such a state

E i t Ch i ti J b


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Environments Christian Jacob


5 Environments

5.1 Properties of Environments

Accessible vs. inaccessible.

An agents sensory apparatus gives it access to the complete state of theenvironment.

An environment is effectively accessible if the sensors detect all aspects that

are relevant to the choice of action.

In an accessible environment an agent need not maintain any internal state tokeep track of the world.

Deterministic vs. nondeterministic

In a deterministic environment, the next state of the environment is completely

determined by the current state and the actions selected by the agents.An agent need not worry about uncertaintyin an accessible, deterministicenvironment.

If the environment is inaccessible, however, it may appear to the agent to benondeterministic.



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Episodic vs. nonepisodic

In an episodic environment, the agents experience is divided into episodes.Each episode consists of the agent perceiving and the acting.

Subsequent episodes do not depend on what actions occur in previousepisodes.

In episodic environments the agent does not have to think ahead.

Static vs. dynamic

A dynamic environment can change while an agent is deliberating.

In static environments, an agent need not keep looking at the world while it isdeciding on an action, nor need it worry about the passage of time.

An environment is called semidynamic if it does not change with the passageof time but the agents performance score does.

Discrete vs. continuous

If there are a limited number of distinct, clearly defined percepts and actions wesay that the environment is discrete. Chess is discrete. Taxi driving iscontinuous.



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Examples of Environments and their Characteristics

Environment Accessible Deterministic Episodic Static Discrete

Chess with a clock

Chess without a clock

Poker

Backgammon

Taxi driving

Medical diagnosis systemImage-analysis system

Part-picking robot

Refinery controller

Interactive English tutor

Yes

Yes

No

Yes

No

NoYes

No

No

No

Yes

Yes

No

No

No

NoYes

No

No

No

No

No

No

No

No

NoYes

Yes

No

No

Semi

Yes

Yes

Yes

No

NoSemi

No

No

No

Yes

Yes

Yes

Yes

No

NoNo

No

No

Yes



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5.2 Environment Programs

A generic environment program illustrates the basic relationship between agents and

environments.

procedure RUN-ENVIRONMENT( state, UPDATE-FN, agents, termination)

inputs: state the initial state of the environmentUPDATE-FN function to modify the environmentagents a set of agents

termination a predicate to test when we are done

repeatfor eachagentinagentsdo

PERCEPT[ agent]


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Environment Simulator Keeping Track of Agent Performances

procedure RUN-EVAL-ENVIRONMENT( state, UPDATE-FN, agents, termination,

PERFORMANCE-FN) returnsscores

local variables: scores a vector the same size as agents, all 0

repeatfor eachagentinagentsdo

PERCEPT[ agent]

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