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Intelligent Agents

Franco GUIDI POLANCOPolitecnico di Torino / CIM Group

http://www.cim.polito.it

franco.guidi@polito.it09-APR-2003

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Agenda

Introduction

Abstract Architectures for Autonomous Agents

Concrete Architectures for Intelligent Agents

Multi-Agent Systems

Summary

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Introduction

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What agents are

“One who is authorised to act for or in place of another as a : a representative, emissary, or official of a government <crown agent> <federal agent> b : one engaged in undercover activities (as espionage) : SPY <secret agent> c : a business representative (as of an athlete or entertainer) <a theatrical agent>”

                                                

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What agents are

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

Russell & Norvig

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What agents are

"Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed."

Pattie Maes

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What agents are

“Intelligent agents continuously perform three functions: perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions.”

Barbara Hayes-Roth

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What agents are

"Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user's goals or desires."

IBM's Intelligent Agent Strategy white paper

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What agents are

Definition that refers to “agents” (and not “intelligent agents”):

“An agent is a computer system that is situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives.”

Wooldridgep & Jennings

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What agents are

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Agents & Environments

The agent takes sensory input from its environment, and produces as output actions that affect it.

Environment

sensor

inputaction

outputAgent

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Agents & Environments (cont.)In complex environments: An agent do not have complete control over

its environment, it just have partial control Partial control means that an agent can

influence the environment with its actions An action performed by an agent may fail to

have the desired effect.

Conclusion: environments are non-deterministic, and agents must be prepared for the possibility of failure.

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Agents & Environments (cont.)

Effectoric capability: agent’s ability to modify its environment.

Actions have pre-conditions

Key problem for an agent: deciding which of its actions it should perform in order to best satisfy its design objectives.

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Examples of agents

Control systemse.g. Thermostat

Software daemonse.g. Mail client

But… are they known as Intelligent Agents?

N

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What is “intelligence”?

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What intelligent agents are“An intelligent agent is one that is capable of flexible autonomous action in order to meet its design objectives, where flexible, I mean three things: reactivity: agents are able to perceive their environment, and

respond in a timely fashion to changes that occur in it in order to satisfy its design objectives;

pro-activeness: intelligent agents are able to exhibit goal-directed behaviour by taking the initiative in order to satisfy its design objectives;

social ability: intelligent agents are capable of interacting with other agents (and possibly humans) in order to satisfy its design objectives”;

Wooldridgep & Jennings

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Agent characteristics

Autonomy

Proactiveness (Goal oriented)

Reactivity

Socially able (a.k.a. communicative)

Weak notion +• Mobility• Veracity• Benevolence• Rationality

Weak notion of agent

Strong notion of agent

An Agent has the weak agent characteristics. It may have the strong agent characteristics. (Amund Tveit)

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Objects & Agents

Object

“Objects do it for free; agents do it for money”

sayHelloToThePeople() say Hello to the people

“Hello People!”

Agents control its states and behaviours

Classes control its states

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Objects & Agents (cont.)

Distinctions:Agents embody stronger notion of

autonomy than objectsAgents are capable of flexible (reactive,

pro-active, social) behaviourA multi-agent system is inherently multi-

threaded

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Abstract Architectures for Autonomous Agents

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Formalization

AgentsStandard agentsPurely reactive agentsAgents with state

Environments

History

Perception

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Agents & Environments

Agent’s environment states characterised by a set:

S={ s1,s2,…}

Effectoric capability of the Agent characterised by a set of actions:

A={ a1,a2,…}

Environment

sensor

input

action

output

Agent

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Standard agents

A Standard agent decides what action to perform on the basis of his history (experiences).

A Standard agent can be viewed as function

action: S* A

S* is the set of sequences of elements of S.

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Environments

Environments can be modeled as function

env: S x A P(S)

where P(S) is the powerset of S;

This function takes the current state of the environment sS and an action aA (performed by the agent), and maps them to a set of environment states env(s,a).

Deterministic environment: all the sets in the range of env are singletons.Non-deterministic environment: otherwise.

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History

History represents the interaction between an agent and its environment. A history is a sequence:

Where:

s0 is the initial state of the environment

au is the u’th action that the agent choose to perform

su is the u’th environment state

h:s0 s1 s2 … su

a0 a1 a2 au-1 au

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Purely reactive agents

A purely reactive agent decides what to do without reference to its history (no references to the past).It can be represented by a function

action: S A

Example: thermostatEnvironment states: temperature OK; too cold

heater off if s = temperature OKaction(s) =

heater on otherwise

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Perception

see and action functions:

Environment

Agent

see action

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Perception (cont.)

Perception is the result of the function

see: S P

where P is a (non-empty) set of percepts (perceptual inputs).

Then, the action becomes:

action: P* A

which maps sequences of percepts to actions

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Perception ability

MIN MAX

Omniscient

Non-existent

perceptual ability

| E | = 1 | E | = | S |

where

E: is the set of different perceived states

Two different states s1 S and s2 S (with s1 s2) are indistinguishable if see( s1 ) = see( s2 )

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Perception ability (cont.)

Example:x = “The room temperature is OK”y = “There is no war at this moment”

then:S={ (x,y),(x,y),(x,y),(x, y)} s1 s2 s3 s4

but for the thermostat: p1 if s=s1 or s=s2see(s) =

p2 if s=s3 or s=s4

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Agents with statesee, next and action functions

Environment

Agent

see action

next state

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Agents with state (cont.)

The same perception function:

see: S P

The action-selection function is now:

action: I A

where

I: set of all internal states of the agent

An aditional function is introduced:

next: I x P I

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Agents with state (cont.)

Behaviour: The agent starts in some internal initial state i0

Then observes its environment state s The internal state of the agent is updated with

next(i0,see(s)) The action selected by the agent becomes

action(next(i0,see(s))), and it is performed The agent repeats the cycle observing the

environment

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Concrete Architectures for Intelligent Agents

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Classes of agents

Logic-based agents

Reactive agents

Belief-desire-intention agents

Layered architectures

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Logic-based architectures

“Traditional” approach to build artificial intelligent systems: Logical formulas: symbolic

representation of its environment and desired behaviour.

Logical deduction ortheorem proving: syntactical manipulation of this representation.

and

or

grasp(x)

Pressure( tank1, 220)

Kill(Marco, Caesar)

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Logic-based architectures: example

A cleanning robot

•In(x,y) agent is at (x,y)•Dirt(x,y) there is a dirt at (x,y)•Facing(d) the agent is facing direction d

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Logic-based architectures: abstraction

Let L be the set of sentences of classical first-order logic

Let D=P(L) be the set of L databases (the internal state of the agent is element of D), and 1, 2,.. memebers of DThe agent decision making rules are modelled through a set of deduction rules, | means that formula can be proved from database using only the deduction rules

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Logic-based architectures: abstraction (cont.)

The perception function remains unchanged:

see: S P

The next function is now :

next: D x P D

The action function becomes:

action: D A

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Logic-based architectures: abstraction (cont.)

Pseudo-code of function action is:

1. begin function action

2. for each a do

3. if | Do(a) then return a

4. for each a do

5. If | Do(a) then return a

6. return null

7. end function action

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Reactive architectures

Forces:Rejection of symbolic representationsRational behaviour is seen innately linked

to the environment Intelligent behaviour emerges from the

interaction of various simpler behaviours

situation action

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Reactive architectures: example

A mobile robot that avoids obstacles

•ActionGoTo (x,y): moves to position (x,y)

•ActionAvoidFront(z): turn left or rigth if there is an obstacle in a distance less than z units.

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Belief-Desire-Intention (BDI) architectures

They have their Roots in understanding practical reasoning.

It involves two processes:Deliberation: deciding what goals we want

to achieve.Means-ends reasoning: deciding how we

are going to achieve these goals.

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BDI architectures (cont.)

First: try to understand what options are available.

Then: choose between them, and commit to some.

These choosen options become intentions, which then determine the agent’s actions.

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BDI architectures (cont.)

Intentions are important in practical reasoning: Intentions drive means-end

reasoning Intentions constrain future

deliberation Intentions persist Intentions influence beliefs

upon which future reasoning is based

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BDI architectures: reconsideration of intentionsExample (taken from Cisneros et al.)

Time t = 0Desire: Kill the alienIntention: Reach point PBelief: The alien is at P

P

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BDI architectures: reconsideration of intentions

P

Q

Time t = 1Desire: Kill the alienIntention: Reach point PBelief: The alien is at P Wrong!

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BDI architectures: reconsideration of intentionsDilemma: If intentions are not reconsidered sufficiently often, the

agent can continue to aim to an unreachable or no longer valid goal (bold agents)

If intentions are constantly reconsidered, the agent can fail to dedicate sufficient work to achieve any goal (cautious agents)

Some experiments: Environments with low rate of change: better bold

agents than cautious ones. Environments with high rate of change: the opposite.

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Layered architectures

To satisfy the requirement of integrating a reactive and a proactive behaviour.

Two types of control flow: Horizontal layering: software layers are each

directly connected to the sensory input and action output.

Vertical layering: sensory input and action output are each dealt with by at most one layer each.

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Layered architectures: horizontal layering

Advantage: conceptual simplicity (to implement n behaviours we implement n layers)

Problem: a mediator function is required to ensure the coherence of tje overall behaviour

Layer n

…

Layer 2

Layer 1

perceptual

input

action

output

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Layered architectures: vertical layering

Subdivided into:

Layer n

…

Layer 2

Layer 1

Layer n

…

Layer 2

Layer 1

perceptual input

action output

perceptual

input

action

outputOne pass architecture

Two pass architecture

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Layered architectures: TOURINGMACHINES

Proposed by Innes Ferguson

Perception subsystem Action subsystem

Reactive layer

Planning layer

Modelling layer

Control system

sensor input

action output

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Layered architectures: INTERRAP

Proposed by Jörg Müller

World interface

Behaviour layer

Plan layer

Cooperation layer

World model

Planning knowledge

Social knowledge

sensor input action output

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Multi-Agent Systems (MAS)

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Main idea

Cooperative working environment comprising synergistic software components can cope with complex problems.

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Cooperation

Three main approaches:Cooperative interactionContract-based co-operationNegotiated cooperation

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Rationality

Priciple of social rationality by Hogg et al.:“Whithin an agent-based society, if a socially rational agent can perform an action so that agents’ join benefit is greather than their joint loss then it may select that action.”

EU(a) = f( IU(a), SU(a) )

where:EU(a): expected utility

of action aIU(a): individual utilitySU(a): social utility

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Communication

Agent Communication Languages (ACL)

Different ACLs:FIPA (Foundation for Intelligent Physical

Agents) ACLetc.

Ontology

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MAS Tools and Techniques

ADK

AgentSheets

AgentTool

Bee-gent

CABLE

Cornet Way JAK

CORMAS

Cougaar

DECAF

Excalibur Agent

FIPA-OS

Grasshopper

Massyve Kit

NARVAL

RePast

RESTINA

SEMOA

SIM_AGENT

StarLogo

TuCSon

VOYAGER

Xraptor

ZEUZ

IDOLIMPACTJACKJADEJADE / LEAPJAFMAS /JIVEJATLiteBeanJESSKaarlbogaLEELiving MarketsMAMLMAP /CSM

Some products identified by AgentLink:

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Summary

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SummaryAgents exhibit autonomy, responsiveness, proactiveness and social ability. They may also exhibit mobility, veracity, benevolence, rationality and cooperation

Frameworks for agent development see agents as intentional systems. Some invoke semantics of possible worlds, other distinguish between explicit and implicit belief

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Summary (cont.)

Agents’ architectures may be fundamentally deliberative or reactive, or may combine both approaches in a hybrid architecture

Rationality in MAS involves considering the social and the individual utility of an action

For an effective communication between agents is required a common language and ontology

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ReferencesCisneros J., Huerta D. and Mandujano S. “Arquitectura BDI - Sistemas multiagente” Franklin S. et al. “Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents” in Proceedings of the Third International Workshop an Agent Theories, Architectures, and Languages. Springer-Verlag, 1996Maes P. “Software Agents”. Available http://www.media.mit.eduMangina E. “Review of software products for multi-agent systems”. Available http://www.agentlink.comWooldridge M. “An introduction to multiagent systems”. John Wiley & Sons, Chichester, February 2002