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Intelligent Agents. Franco GUIDI POLANCO Politecnico di Torino / CIM Group http://www.cim.polito.it franco.guidi@polito.it 09-APR-2003. Agenda. Introduction Abstract Architectures for Autonomous Agents Concrete Architectures for Intelligent Agents Multi -Agent Systems Summary.

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intelligent agents

Intelligent Agents

Franco GUIDI POLANCO

Politecnico di Torino / CIM Group

http://www.cim.polito.it

franco.guidi@polito.it

09-APR-2003

Franco Guidi P.

agenda
Agenda
  • Introduction
  • Abstract Architectures for Autonomous Agents
  • Concrete Architectures for Intelligent Agents
  • Multi-Agent Systems
  • Summary

Franco Guidi P.

introduction

Introduction

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what agents are
What agents are
  • “One who is authorised to act for or in place of anotheras 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 are1
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 are2
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 are3
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 are4
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 are5
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 are6
What agents are

Franco Guidi P.

agents environments

sensor

input

action

output

Agent

Environment

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

Franco Guidi P.

agents environments cont
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.

Franco Guidi P.

agents environments cont1
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.

Franco Guidi P.

examples of agents

N

Examples of agents
  • Control systems
    • e.g. Thermostat
  • Software daemons
    • e.g. Mail client

But… are they known as IntelligentAgents?

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what intelligent agents are
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 itin 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

Franco Guidi P.

agent characteristics
Agent characteristics

Weak notion of agent

  • Autonomy
  • Proactiveness (Goal oriented)
  • Reactivity
  • Socially able (a.k.a. communicative)

Strong notion of agent

  • Weak notion +
  • Mobility
  • Veracity
  • Benevolence
  • Rationality

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

Franco Guidi P.

objects agents

sayHelloToThePeople()

say Hello to the people

“Hello People!”

Objects & Agents

Object

  • Classes control its states
  • Agents control its states and behaviours

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

Franco Guidi P.

objects agents cont
Objects & Agents (cont.)
  • Distinctions:
    • Agents embody stronger notion of autonomy than objects
    • Agents are capable of flexible (reactive, pro-active, social) behaviour
    • A multi-agent system is inherently multi-threaded

Franco Guidi P.

formalization
Formalization
  • Agents
    • Standard agents
    • Purely reactive agents
    • Agents with state
  • Environments
  • History
  • Perception

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agents environments1

Agent

action

output

sensor

input

Environment

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,…}

Franco Guidi P.

standard agents
Standard agents
  • A Standardagent 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.

Franco Guidi P.

environments
Environments
  • Environments can be modeled as function

env:S x A  P(S)

whereP(S) is the powerset of S;

This function takes the current state of the environment sS and an action aA (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.

Franco Guidi P.

history

a0

a1

a2

au-1

au

h:s0s1s2 … su

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

Franco Guidi P.

purely reactive agents
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: thermostat

Environment states: temperature OK; too cold

heater off if s = temperature OK

action(s) =

heater on otherwise

Franco Guidi P.

perception
Perception
  • see and action functions:

Agent

see

action

Environment

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perception cont
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

Franco Guidi P.

perception ability
Perception ability

Non-existent

perceptual ability

Omniscient

MAX

MIN

| 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 )

Franco Guidi P.

perception ability cont
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=s2

see(s) =

p2 if s=s3 or s=s4

Franco Guidi P.

agents with state
Agents with state
  • see,nextandactionfunctions

Agent

see

action

state

next

Environment

Franco Guidi P.

agents with state cont
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

Franco Guidi P.

agents with state cont1
Agents with state (cont.)
  • Behaviour:
    • The agent starts in some internal initialstatei0
    • Then observes its environment state s
    • The internalstate 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

Franco Guidi P.

classes of agents
Classes of agents
  • Logic-based agents
  • Reactive agents
  • Belief-desire-intention agents
  • Layered architectures

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logic based architectures
Logic-based architectures
  • “Traditional” approach to build artificial intelligent systems:
    • Logical formulas: symbolic

representation of its

environment and desired

behaviour.

    • Logical deduction or

theorem proving: syntactical

manipulation of this

representation.

and

grasp(x)

Kill(Marco, Caesar)

or

Pressure( tank1, 220)

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logic based architectures example
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

Franco Guidi P.

logic based architectures abstraction
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 D
  • The 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 

Franco Guidi P.

logic based architectures abstraction cont
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

Franco Guidi P.

logic based architectures abstraction cont1
Logic-based architectures: abstraction (cont.)
  • Pseudo-code of function action is:
      • begin function action
      • for each a  A do
      • if  | Do(a) then return a
      • for each a  A do
      • If  |   Do(a) then return a
      • return null
      • end function action

Franco Guidi P.

reactive architectures
Reactive architectures
  • Forces:
    • Rejection of symbolic representations
    • Rational behaviour is seen innately linked to the environment
    • Intelligent behaviour emerges from the interaction of various simpler behaviours

situation  action

Franco Guidi P.

reactive architectures example
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.

Franco Guidi P.

belief desire intention bdi architectures
Belief-Desire-Intention (BDI) architectures
  • They have their Roots in understandingpractical 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.

Franco Guidi P.

bdi architectures cont
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.

Franco Guidi P.

bdi architectures cont1
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

Franco Guidi P.

bdi architectures reconsideration of intentions
BDI architectures: reconsideration of intentions
  • Example (taken from Cisneros et al.)

P

Time t=0

Desire: Kill the alien

Intention: Reach point P

Belief: The alien is at P

Franco Guidi P.

bdi architectures reconsideration of intentions1
BDI architectures: reconsideration of intentions

Q

P

Time t=1

Desire: Kill the alien

Intention: Reach point P

Belief: The alien is at P

Wrong!

Franco Guidi P.

bdi architectures reconsideration of intentions2
BDI architectures: reconsideration of intentions
  • Dilemma:
    • 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.

Franco Guidi P.

layered architectures
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.

Franco Guidi P.

layered architectures horizontal layering
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

action

output

perceptual

input

Layer 2

Layer 1

Franco Guidi P.

layered architectures vertical layering

Layer n

Layer n

Layer 2

Layer 2

Layer 1

Layer 1

Layered architectures: vertical layering
  • Subdivided into:

action output

Two pass architecture

perceptual input

perceptual

input

action

output

One pass architecture

Franco Guidi P.

layered architectures touringmachines
Layered architectures: TOURINGMACHINES
  • Proposed by Innes Ferguson

Modelling layer

sensor input

Perception subsystem

Planning layer

Action subsystem

action output

Reactive layer

Control system

Franco Guidi P.

layered architectures interrap
Layered architectures: INTERRAP
  • Proposed by Jörg Müller

Cooperation layer

Social knowledge

Plan layer

Planning knowledge

World model

Behaviour layer

World interface

sensor input

action output

Franco Guidi P.

main idea
Main idea
  • Cooperative working environment comprising synergistic software components can cope with complex problems.

Franco Guidi P.

cooperation
Cooperation
  • Three main approaches:
    • Cooperative interaction
    • Contract-based co-operation
    • Negotiated cooperation

Franco Guidi P.

rationality
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 a

IU(a): individual utility

SU(a): social utility

Franco Guidi P.

communication
Communication
  • Agent Communication Languages (ACL)
  • Different ACLs:
    • FIPA (Foundation for Intelligent Physical Agents) ACL
    • etc.
  • 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

MAS Tools and Techniques
  • Some products identified by AgentLink:
  • IDOL
  • IMPACT
  • JACK
  • JADE
  • JADE / LEAP
  • JAFMAS /JIVE
  • JATLiteBean
  • JESS
  • Kaarlboga
  • LEE
  • Living Markets
  • MAML
  • MAP /CSM

Franco Guidi P.

summary

Summary

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summary1
Summary
  • Agents 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

Franco Guidi P.

summary cont
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

Franco Guidi P.

references
References
  • Cisneros 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, 1996
  • Maes P. “Software Agents”. Available http://www.media.mit.edu
  • Mangina E. “Review of software products for multi-agent systems”. Available http://www.agentlink.com
  • Wooldridge M. “An introduction to multiagent systems”. John Wiley & Sons, Chichester, February 2002

Franco Guidi P.