Multi Agent Systems

page - 1 / 88VŠB – Technical University of Ostrava

Multi Agent Systems

LabIS: http://labis.vsb.cz/


Content

• Introduction to the Theory of Multi-Agent Systems

• A Brief Summary of the Project “Logic and Artificial Intelligence for Multi-Agent Systems” (Logic & MAS)

• The Project in more details:

• Logic and Communication (TL Language, Ontology)

• Geographical Data and Infrastructure

• Process Management (has been presented by Prof. Ivo Vondrák)

• Further research

• Discussion


Multi Agent System

Distributed application• runs on many computers over the network

(Internet). Composed of agents

• there can be hundreds or thousands of agents which cooperate to achieve their goals.

No centralized control• agents are (less or more) intelligent and

autonomous• generally, there is no central dispatcher


Agent Characterisation

• (Jacques Ferber)Agent is a physical or virtual entitya) which is capable of acting in an environmentb) which can communicate directly with other agentsc) which is capable of perceiving it's environment

(to a limited extent)d) which can offer services and possesses skills e) may be able to reproduce itselff) tends towards satisfying it's objectivesg) may be able to learn by experience

h) etc., more...


Object vs. AgentObject Agent

Encapsulates methods and attributes

Encapsulates behaviour

Communicates by method calling

Communicates by asynchronous messages in a higher language

Performs a predefined task when called

Performs a task if it wants to

Synchronization of threads is necessary

Agents are not dependent on each other

Less autonomous More autonomous

Predefined behaviour Dynamic, intelligent and autonomous behaviour

without intelligence: n = 0. Intelligence of a degree n > 0.


Agents can perceive an environment

• Agents can carry out actions which modify agent's environment.

• Agents can perceive the environment and make decisions based on it.


Agents communicate with each other

• Outer languages • define an envelope

format for languages and are not concerned with the content

(KQML, FIPA-ACL)

• Inner languages• are concerned only

with the content. The content of a message can be specified in any language that agents can understand

(Prolog, JESS, XML, ...)

How are you?

Hello

What's the time?

fine, thanks

10:30


Agents learn by experience

• Artificial intelligence• Expert systems• Logic and intelligent decisions in difficult

situations• Autonomy

???


Knowledge representation

• Ontology• is widely used for describing some parts of the real

world. Can be represented in different logical languages (e.g. W3C OWL) which can be easily interpreted by computers and humans. Today, there are many ontology databases and libraries which are focused on different topics (mobile devices, cars, vines, weather and many others).

What is the weather?

It is raining,rather take an umbrella

OK, I willconsider it ...


Agents’ features

• Agents can clone themselves

• They are able to move from one computer to another one.

Clone

Change PC


Multi Agent World

Agents

EnvironmentPerception

Actions

Communication

Internal representation of

environment

Internal representation of

environment

KnowledgeDesiresPlans


Some Application Areas

• Robotics• Traffic simulation and navigation• Ubiquitous computing • Air traffic control • Network management • Distributed data mining • Manufacturing systems• And many others...

Emergency and critical situations


Standardization

• FIPA (Foundation for Intelligent Physical Agents) • Standards for Intelligent Multi-agent systems.• Cooperation with universities and companies.• Conferences, seminars, workshops on multi-agent

systems• Standards for communication languages and

protocols.


Content

Introduction to the Theory of Multi-Agent Systems

Logic and Artificial Intelligence for Multi-Agent Systems (Logic & MAS)

The Project in more details:

Logic and Communication (TL Language, Ontology)

Geographical Data and Infrastructure



• Discussion

Multi-Agent Systems 15

LOGIC & MAS The pilot project conducted at the Research Laboratory of Intelligent Systems (LabIS – http://labis.vsb.cz/ )

The main goal:Research of information technologies needed

for coordination of autonomous intelligent agents in extraordinary or emergency situations

Multi-Agent Systems 16

Logic & Multi-Agent World

See the next presentantion VSE-MAS-B


Communication Language based on TIL

• FIPA SL• TL Language

• TL vs. TIL• Ontologies• Examples• Implementation

• Example


Communication Language based on TIL

A crucial point in a multi-agent environment: communication

Message• a unit of communication• can be of an arbitrary form, but• has to be structured in terms of attributes:

sender, receiver,. . . performative (communicative act) – inform, query,

request,. . . content – semantics of the message, encoded in

a content language ontology – vocabulary of concepts used in the content


FIPA SL

• FIPA (The Foundation for Intelligent Physical Agents)• creates widely accepted standards for MAS• one of them is the content language FIPA SL (Semantic

Language)

• FIPA SL is based on FOL (First-order logic)=>

+ widely known, well elaborated logic, but- mathematical logic – “stenography of mathematics”- weak expressive power, over – inferring / under –

inferring - an extension is needed in order to make it possible to

communicate in a standardised natural language:• the type of a message (query, request,. . . )• the distinction between empirical and analytical

concepts• to properly analyse modalities – possible, necessary• to properly analyse attitudes – believing, knowing,

thinking, wanting, seeking, etc.


FIPA SL

• SL Syntax – well defined (grammar)• SL Semantics (meaning) – poor:

• the standard work just contains Section “Notes on Semantics” (really just notes)

• derived from FOL semantics? – not applicable because of numerous extensions beyond FOL

• intuitive – is it?

• The poor semantics may lead to serious misunderstandings between agents.


TL – Why?

• We propose a new content language: TL• TL is a FIPA compliant content language• TL is based on Transparent Intensional Logic (TIL)=> + rich bi-dimensional hierarchy of types

+ great expressive power+ fine-grained rigorous semantics+ Makes it possible to analyse natural language

expressions in a fine-grained way (and formal languages too, e.q. SL)

+ a universal semantic tool (specification of semantics, combining different languages together,. . . )

• Appropriate for• (complex) knowledge representation• human-level communication


TL vs. TIL

• The TIL language of constructions is not apt for computerised processing, because• the TIL language

• is not standardised• uses non-ASCII characters (not found on the

keyboard)

• We need a standardised notation for types as well as for encoding constructions

• The epistemic base needs extending: a type of sequences is useful (and also a construction of it)

• There is no way of fixing ontologies in TIL (any entity of any order can be trivialised); we need to specify the chosen conceptual systems to work with


TL vs. TIL

• the TL type base is an extension of TIL epistemic base

TL name TIL equivalent Description

Bool Truth values

Indi Individuals

Date Times

PSW Possible worlds

String

Integer

Float Action Type of action


TL vs. TIL

Compound (molecular) types:• collections of partial functions (as in TIL)(Float,Indi Bool) ~ ()

• finite sequences of objects of particular types: Indi[]

Constructions:• variable – x or x:[Type]• trivialization – ’A ~ oA• application – (F A B C) ~ [F A B C]• abstraction – \x:[Type]A ~ x A• sequence – (Sequence:Type A B C)

• shortcut application – C@w,t ~ Cwt


Grammar of TL (fragment)

TLExpression = Construction| Macro.

Macro = SequenceMacro.Construction = Trivialization

| Variable| Application| Abstraction.

Trivialization = "’" Concept| "’" PrimitiveValue| "’" Construction.

Concept = UpperCaseAtom| "#" "(" UpperCaseAtom ArgList

")"....


TL and Ontologies

• An ontology is a vocabulary of domain-specific concepts (FIPA)

• All concepts used by a content language must be defined in an ontology

• Any concept in an ontology can be used by a content language

• TL types must be specified for concepts to be used in TL

• Ontologies are usually frame-like structures (frames with slots)

• The latest trend is Semantic Web & OWL (Description Logics).• It is not neglected by FIPA• but not supported (as yet)


Example

Every prime number is odd. (not true)• Ontology (part)

• TIL analysis:[o x[o [oPrime x] [oOdd x]]]

• TL notation:(’All \x:[Integer](’Impl (’Prime x) (’Odd x)))

Name TIL type TL type

Prime () (Integer->Bool)

Odd () (Integer->Bool)

All, (()) ((Integer->Bool)->Bool)

Impl, () (Bool,Bool->Bool)


Example

The highest mountain is in Asia.

• TIL analysis:wt[oInwt wt[oHighestwt oMountainwt]wt oAsia]

• TL notation:\w\t(’In@w,t ((\w\t(’Highest@w,t ’Mountain@w,t) w) t)

’Asia)

Name TIL type TL type

Highest (())((Indi->Bool)->Indi)@

Mountain ()(Indi->Bool)@

In ()(Indi,Indi->Bool)@

Asia Indi


Implementation of TL

• TL is implemented in the MAS framework JADE (work in progress)

• JADE• open-source, extensible development framework in

Java• strictly FIPA compliant

• Ontologies are frame-like, edited by Protege.• Working on integration of OWL ontologies into TL.• Any agent can simply choose TL as the content

language for his messages.


Example

The scenario contains:• large car parking, railway station

Agents:• Driver – would like to park close to the railway station• Dispatcher of the car parking managing available

pull-ins

Sketch of their dialogue:• Driver: I want you to park me somewhere not far

from the railway station.• Dispatcher: OK, I can park you at this pull-in

(concrete position).• Driver: Good.


Ontology for the Example

Concept TL type

TheDriver Indi

TheDispatcher Indi

TheTrainStation Indi

Pull-in Indi

Near (Indi,Indi->Bool)@

Arrange (Indi,Bool@->Action)

Park (Indi,Indi->Bool)@


Communication

Driver:call for proposal\x:[Indi](’Arrange ’TheDispatcher

\w\t(’And(’Park@w,t ’TheDriver x)(’Near@w,t ’TheTrainStation x)))

• A call for proposal is a communicative act which• takes one argument• returns the action to be proposed• such that the responding agent will fill in the

parameter x = the parking place


Communication

Dispatcher:propose (\x:[Indi](’Arrange ’TheDispatcher

\w\t(’And (’Park@w,t ’TheDriver x) (’Near@w,t ’TheTrainStation x)))#(Pull-in (Position "<gml:Polygon>

<gml:outerBoundaryIs><gml:LinearRing> <gml:coordinates> 0,0 100,0 100,100 0,100

0,0; </gml:coordinates></gml:LinearRing>

</gml:outerBoundaryIs></gml:Polygon>")))


Communication

Driver:Accept proposal

(same content as the last one)

Car Agent

1: Call for proposal Dispatcher

2: Propose

3: Accept proposal


Future work on TL communication

• Integration of TL with Semantic Web• namely the Ontology Web Language (OWL)• specification of ontologies for TL based

communication

• Methodology of agents’ communication using the TL language

• Future Research: • Specification of an accurate inference machine

based on the expressive language such as TL• Implementation of the inference machine


Content


“Logic and Artificial Intelligence for Multi-Agent Systems” (Logic & MAS)






• Discussion


Connecting GIS and Multi-agent systems

Storing

Geoinformaticsystem(GIS)

Gatheringdata

Maintaining

AnalyzingPresenting


Relations between GIS and MAS

• Identity – situated agent corresponds to a feature stored in the geo-database

• Causal – agents can affect other features or their attributes (behaviour of agents is affected by state of GIS)

• Temporal – synchronization between MAS and GIS

• Topological – provides information about topological relations between features


Environment perceiving


Environment built in a vector layer

Creation ofinfrastructureagents

Vector geodata export togeodatabase

Geo Database


Agent Based Environment


Agent types

• Infrastructure agents• streets, crossroads, other infrastructure objects

• Mobile agents• vehicles, humans

• Database agent• providing access to geographic databases

• Visualization agent• performing visualization of static and dynamic

agents


Implementation environment

• JADE• Java Agent DEvelomement framework – framework that

simplifies agent creation and deployment• PostGIS

• Open source spatial database, used for data storage and perception of the environment

• GRASS, JUMP, 3D visualization module• Used for visualization purposes only


Implementation environmentUser Interface

(JUMP, GRASS, 3D)Database Agent

ACL

ACL

ACL

Other Agents

Geo Database


Application screenshots


Application video


Content






Process Management

Discussion


Process management in MAS

• MAS software process is similar to the standard information system development process

• There are some differences, however:• functions, properties and skills of MAS are very close

to the real world situations• emphasis is placed on social behavior of system

components and the whole system• emphasis on the autonomy of components• emphasis on communication level between

components• components of such systems are Agents

• Methodology used during the development process - UML


MAS Development Process

Processes Agents

Business Model

Real World

“Tools” that support this development process• standard software process and methodology• UML tools with some extensions – Behavior Activity Diagram• etc.


Basic definitions

• Agent term describes a software abstraction, an idea, or a concept, similar to OOP terms such as methods, functions, and objects. The concept of an agent provides a convenient and powerful way to describe a complex software entity that is capable of acting with a certain degree of autonomy in order to accomplish tasks on behalf of its user. But unlike objects, which are defined in terms of methods and attributes, an agent is defined in terms of its behavior.

• Behavior refers to the actions or reactions of an object or organism, usually in relation to the environment. The complexity of the behavior of an organism is related to the complexity of its nervous system (internal structure, knowledge and skills).

• Realization is the element that provides an description for implementation of an element that specifies behavior, by other words, the Realization is one of possible implementation of Process; this implementation is specified by one “Behavior Activity Diagram”


Basic definitions

• Process is a naturally occurring or designed sequence of operations or events, possibly taking up time, space, expertise or other resource, which produces some outcome. A process may be identified by the changes it creates in the properties of one or more objects under its influence. The Processes form the Behavior of an Agent.

• Activity is something done as an action. It is atomic action within the Process.

• Process or Activity Nodes are the graphical representation of afore mentioned terms.


Agents’ Behaviors

• What is the Behavior of an Agent?• We have to specify:

• everything that an Agent can do,• including Agent’s abilities and potency of

• solving the problems he/she/it can meet, and of• responding to changes in the environment

• There are several types of behavior templates corresponding to Agent’s classification:• simple reactive behavior• simple proactive behavior• hybrid behavior (combined reactive and proactive

behavior)

• The Behavior is defined by Processes within the Agent’s life


Agents’ Processes

• Process is a tool for describing Agent’s elementary Behaviors

• Each description of a Process consists of the following:• input objects• output objects (in agreement with the goals of the

process)• a set of possible realizations (executing the function,

mapping: Inputs Outputs)

• Its execution depending on a current situation • Each Process can be realized in several ways –

possible realizations of the process


Internal Structure of Agents

• Processes can be defined as• internal: within an Agent• external: in the global repository

• (Internal) Processes within a given Agent• executable only by the given Agent• they can extend or restrict globally defined

processes too

• (External) Processes saved in the global repository• executable by all the Agents within the MAS• possibility of learning and changing the Behavior


Internal Structure of Agents

Agent_A primary process activity 1 activity 2 process 1 activity 1 activity 4 process 2 activity 3 activity 5

Agent_B primary process activity 6 process 3 activity 3 activity 4 process 1 activity 1 activity 4

Process defined in the global repository

Process may have different control flows defined by

Realizations

Only one possibility to connect the Agents –

message passing


Activity Diagram Extension

getAgentName

Agent_B

Identification of the Agent (type of agents) acts in the receiver role during this communication. The receiver can be just one instance of Agent type (Agent_B) or all instances of a given Agent type ([Agent_B]).

Envelope icon for send/receive activity indication.Main activity element shape

with the name of the sended message. This name

represents the message that is unique in the whole MAS.

getAgentName

Agent_A

The illustration of branching based on incoming message

and standard decision element.

Main activity element shape with the name of the

received message. The name of this activity can be

real name of received message (in the case of

one-to-one communication) or it can be replaced by

“<<unknown>>” string in the case of multiple or unknown

messages receiving.

<<unknown>>

<<undefined>>

<<unknown>>

Agent_A

[msg: getAgentName from: Agent_A] [msg: getColor

from: Agent_A]

Identification of the Agent acts in the sender role. This name of agent can be replaced by the “<<undefined>>” string in the case of receiving message from unknown or multiple sources.


Activity Diagram Extension


Realizations of Process

• Realization describes an algorithm of transforming Inputs into Outputs depending on a current situation (Agent’s reactions)

• Each Realization has to be in accordance with the parent process definition (objectives, input and output objects)

• Each Realization is one “Behavior Activity Diagram” – based on the UML Activity Diagram (from the visual point of view)

• For these purposes, the Multi-Agent Technology calls for an extension of the standard UML approach


Metamodel of MAS Model

ModelCore

Process

0..*

Agent

0..*

0..*

Realization

1..*

Message

0..*

Activity

0..*

Object

0..*

DiagramNode

0..*

InitialNode

FinalNode

SplitNode

JoinNode

DecisionNode

ActivityNode

SendReceiveActivityNode

ProcessNode

DiagramConnection

0..*

2

0..1

0..1

Object Activity

Process

Realization

0..*

0..*

0..*

0..*

0..* 0..*

Each Activity, Behavior and Processhas one set of Input Objectsand one set of Output Objects

ScoreType

0..*

ScoreValue

0..*

0..*


Intelligence & Behavior

• Intelligence contained in the activities• weather forecast, etc.

• Intelligence of the control flows routing• suitable car to a given cargo assignment, etc.

• Intelligent selection of Process Realizations• reconfiguration approach


Behavior Reconfiguration

• Agent’s Behavior can evolve during its life• as a reaction to a current situation in the

moment of emergency (when executing a process).

• This reconfiguration is based on the current state (objects, environment, etc.) of an Agent and/or the whole MAS, and it works with sets of particular Realizations of Processes.

Control Flow of the executed processin the moment of reconfiguration

Process = Reconfiguration Point

Current position in the behavior

Possible realizations of a given process

The most suitable realization

Control Flow of the executed processafter the reconfiguration





• Each Process node within the “Behavior Activity Diagram” can be a reconfiguration point – the position of Behavior that can be changed

• Basic reconfiguration algorithm1. selection of applicable Realizations from a given

Process set2. selection of the most suitable Realization3. execution of the selected Realization

• Tools for support of the reconfiguration• simple branching based on conditions• logical tools• Artificial Intelligence, Expert Systems


Selection of an applicable Realization

• Process approach- Realizations are combinedwith a given Process withinthe modeling phase- easy selection- low intelligence and flexibility

• Logical approach- real-time selection of Realizations

- errors can occur- describing from the logical point of view may be difficult- high intelligence and flexibility

process 1

Agent_A global repositorylocal repository of Agent_A

process 1

Agent_A global repositorylocal repository of Agent_A

Selection of suitable process realizations is

provided by the logic tools during the real-time

running of a given agent. The set of

selected realizations can be different from the set defined by connections.

A)

B)

Assignment of particular process realizations to a

given process is ensured by the links

between the process and all possible specifications of

realizations. This is done during the modeling

phase.


Selection of a Realization

• Modified reconfiguration algorithm1. specification of the set of process Realizations –

based on the process approach2. defining a set of applicable Realizations for a given

Process – based on the process approach3. selection of the most suitable Realization from a

defined set – based on the logical approach4. execution of the selected realization

• Terms usable in this algorithm• Input and Output Objects (step 1 and 2)• values of Objects and Scores (step 3)• FCA, TIL, etc.


Selection of a Realization

• Process and Logical approach together



• Which are the assets of this approach?• possibility of an independent life of each Agent• intelligence built in an Agent Behavior • easy distribution of knowledge on process

management among the Agents• easy way to distribute knowledge on system

adaptability and evolution among the Agents


AgentStudio


Future Architecture of AgentStudio

AgentStudio

Rea

l Wor

ld

MAS ModelModel Documentation

Diagrams of Realizations in PDF, JPEG, etc.

MAS Modelin BPEL

Structure of MASin XML

MAS Model File

Processes

Agents

Objects

Scores

Messages

Realizations

...

Analytic ToolsFCA, etc

Visualization Tools

Verification Tools

MAS Source Code Generation Tool

MAS Framework(Jade in Java, in C#, etc.)

MASMAS Source Code


Future Work

• development of the tool for Agents behavior modeling (.NET)

• behavior-model extensions (message specifications, interfaces, etc.)

• automatic generation of Agents based on their internal Behavior

• involvement of Logic in Agents behavior management (reconfiguration, branching, controlling, etc.)

• distribution of the Behavior specifications and learning process

• development of application methods of the whole MAS management• knowledge management and distribution• system states monitoring







Process Management

Discussion

Documents

Multi Agent Systems