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Algorithms of Artificial Intelligence

Lecture 9: Intelligent agents E. Tyugu

Spring 2003

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Intelligent agents

Intelligent agents are software components. They possess some features of intelligent behavior:

• proactiveness• understanding of agent communication language (ACL)

• reactivity (ability to make some decisions and to act) and they may have properties like • planning ablity• mobility• reflection• perception of environment etc.In the software engineering community they are also called software agents

and are considered to be objects with proactiveness and ACL.(Compare agents and objects.)

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Proactiveness

Proactiveness is the ability to act without external stimuli. It can be realized by means of a separate thread performing the following simple algorithm, and a knowledge base of proactive actions.

A9.1: while true do

wait(givenInterval);

lookAtClock;

consultKB;

act;

od

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How to act in an open world?

• perceiving the world

• affecting the world

• having a (rational) model of behavior

• having motivations to be fulfilled by implementing corresponding goals

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Basic concepts: Agents

• act on behalf of another entities

• autonomy

• proactiveness

• reactivity

• decentralized data and control

• negotiation, communication, coordination

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More definitions

• Distributed Problem Solving (DPS) -considers how the task of solving a particular problem can be divided among a number of cooperating modules

• Multi-Agent Systems (MAS) - concerned with behavior of a collection of autonomous agents aiming in solving a given problem

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Simple reactive agentreactiveAgent() {

while (true) dostate := perceive();rule :=selectRule(KB,state);rule.action();

}}

perceive

actselect

KBEnvironment

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Agent with internal statereactiveAgent() {

while (true) doinput := perceive();rule := selectRule(KB,input,state);state := update(state,input);rule.action();

}}

perceive

actselectKB

Environmentstate

update

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Goal-driven agent

perceive

actselectKB1

Environmentstate

updategoal

planner KB2

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BDIBDI = Beliefs, Desires, Intentions

perceive

actselectKB1

Environmentstate

updategoal

planner KB2

BDI ACL communication

?intentions

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Knowledge processing within an agent

Goal activation

Scheduling

Execution

Planning

GoalsSkills

Worldstate

Plans

Tasks

Resources

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Generic Agent Interpreter - an agent shell(Y. Shoham)

Initialize mental

state and capabilities

Define rules for making new commitments

Update mental state

Execute commitments for current time

clock Representation of mental state and capability

Outgoing messages

Incoming messages

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Agent finite state machine

ACTIVE

RECEIVING

WAITING

INACTIVE

SENDING

Reques

t tran

smit

Transm

ission

done

Time slice elapsed

Agent rescheduled

Decides to receivereceived

suspend

Wait condition satisfied

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Agent machinery

EventType1 Handler1

EventType2 Handler2

… …

EventTypeN HandlerN

Event Event … EventEvent list

Event description list(including Event typesand references toEvent handlers)

Handler1 …. HandlerN

Event handlers

Agent engine

Agent interface

A CL port

Sensors/ affectors

Models

Data

Agent memory

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Agent Shell Functional Structure

ACL Communication block

Low-level communication

Affection

Sensing

Goals analyser/synthesiser

Planner Declarative reflection manager

Procedural reflection manager

Models

Executor

Reactive block

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Agent communication languages (ACL)

There are several ACL-s, the best known are:

– KQML

– FIPA.

The KQML language is based on the speech act theory:

– it gives a message format and message handling protocol

– it has primitives with fixed meaning, called performatives

– it is assumed that the environment for using KQML provides facilitators - special agents supporting the communication process, e.g. supporting a name service etc.

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KQML statement (message)

A KQML message consists of • a performative• its arguments, if any• message content (not understood in KQML)• communication (transport and context( information.Example:(Ask-one - performative

:sender customer1 - communication :content (PRICE VCR ?price) - content

:reply-with VCR-price:langugae LPROLOG - parameters:ontology USD)

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KQML performatives

KQML performatives have the role of commands.

Some KQML performatives are:– tell, deny, cancel, untell

– evaluate, reply, ask-if, ask-one, ask-about, ask-all

– achieve, advertise, subscribe, monitor, register, unregister

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Reflection in agents

• Procedural • Parametric• Declarative

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Declarative reflection schema 1

Oracle

(planner)self

Model of the self and of the environment

daemon

daemon

daemon

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Declarative reflection schema 2

Oracle

(planner)self

Model of the self and of the environment

Reflection manager

Modeling languagedaemon

daemon

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Concept specification language

• Superclass declarationsuper C; - C is a superclass

• Component declarationvar A : C; - A is a new component of class C

• Method declaration rel R : <axiom> <program>; - R is a new method (the name R may be

omitted.)• Equivalence declaration

rel A = B; - A and B are components with the same value

• Equation declarationrel e1 = e2; - e1, e2 are arithmetic expressions which

define a constraint on the variables occurring in them

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Translation of specifications

We use a function Sem´ in an expression Sem´(D,w) denoting the set of formulas (axioms) representing the semantics of a declaration D with respect to the object w.

Sem(class C : D) = ( w. C(w) Sem´(D,w)) <structural axioms>

- semantics of the class is a set of axioms of the form C(w) F, where each F is included into axioms of one of its declarations, plus axioms representing the structure of the class.

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Translation of specifications continued

• Sem´(super C, w) = C(w) – axioms of a superclass are taken without changes.

• Sem´(var A : C, w) = C(w.A) – a component A of an object w obtains the name w.A and keeps the properties of the class C given in its declaration.

• Sem´(rel <axiom> <program> , w) = <axiom instantiated by w> - semantics of a method is given by its axiom.

• Sem´(rel X = Y, w) = w.X = w.Y – equality of components is encoded in the proper way.

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Exhibition/shopping center example

• class Visitor:super Person;

var affiliation : text;

• class Guide:super Agent;

var id : text;param : text;

• class Team:var visitor : Visitor;

guide : Guide;planOfVisit, parameters,

state : text; level, priority : num;

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Exhibition/shopping center example

• class FirstMeet: varguide : any;visitor : any;team : Team; rel

intro: guide, visitor -> team.planOfVisit, team.level, team.param {…};team.guide = guide;team.visitor = visitor;

• class Guide:super Agent;var id, param : text;

• class Team:var visitor : Visitor;

guide : Guide;level, priority : num;planOfVisit , parameters ,state ,nextstate : text;

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Exhibition/shopping center example

• class FirstMeet: varguide : any;visitor : any;team : Team; relintro: guide, visitor ->

team.planOfVisit, team.level, team.param {…};team.guide = guide;team.visitor = visitor;

• class Move: var team : Team;

exhibitionInfo : any;nextPlace : any;

rel next : exhibitionInfo, team.planOfVisit -> nextPlace {…};init exhibitionInfo := Expo2XXX();

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Exhibition/shopping center example

• class AtBooth:super Agora;

var booth : any;time : num;

team : Team;ready,

bye : bool; aliasstate = (time, all.state);

nextstate = (time, all.nextstate); rel intro : team, booth -> state {…};

discourse : (state -> nextstate) -> ready {…}; farewell : ready -> bye {…};

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Declarations for the PC example

Class DiscrepancyExist :var ListOfPCmemebers: any;

resolveConflict: bool;paper: Paper;rel

Conflict: ListOfPCmemebers, paper ->

resolveConflict {…};

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Managing reflection (Exhibition/Shopping center)

Tommy9 : Guide type = Tommy;

MrSmith :Visitor name = ´John´;

act1 : FirstMeet guide = Tommy9, visitor = MrSmith;

team1024 = act1.team;

act2 : Move team = team1024 ;

act3 : AtBooth booth = act2.nextPlace;

act4 : Move team = team1024;

act6 : AtBooth booth = Nokia;

…

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References• G. M. O’Hare, N. R. Jennings, eds. (1996) Foundations of Distributed

Artificial Intelligence. John Wiley & Sons.

• Norvig, Russell (2000) Artificial Intelligence: Modern Approach

• P. Maes. (1987). Concepts and Experiments in Computational Reflection. Proc. ACM Conf. On OOP, Systems, Languages and Applications, Orlando, p. 147 – 156.

• Y. Shoham. (1997). An Overview of Agent-Oriented Programming. In: J. M. Bradshaw, ed. Software Agents. AAAI Press/The MIT Press, p. 271 - 290.

• M. Addibpour, E. Tyugu. Declarative reflection tools for agent shells. Future Generation Computer Systems. July 1996, p. 1 - 12.