Agent-Grid Intelligence Platform for Collaborative Working … · 2006. 6. 3. · 2006-6-3...

2006-6-3 AGrIP-SELMAS Zhongzhi Shi 1

Software Engineering for Large-scale Multi-Agent Systems (SELMAS06)

Zhongzhi [email protected]

Institute of Computing TechnologyChinese Academy of Sciences

Agent-Grid Intelligence Platform for Collaborative Working Environment


Acknowledgement

China Important Basic Research Programme 973 China High-Tech Programme 863 National Natural Science Foundation of ChinaMinistry of Information Industry, ChinaKnowledge Innovative Programme of CAS


Self-Introduction

Zhongzhi Shi is Professor at the Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, IEEE Senior member.

He is Chair of WG 12.2 of IFIP. He also serves as Vice President of Chinese Association for Artificial Intelligence.

He received the 2nd Grade National Award of Science and Technology Progress in 2002. In 1998 and 2001 he received the 2nd Grade Award of Science and Technology Progress from the Chinese Academy of Sciences.

His research interests include intelligence science, multiagentsystems, Semantic Web, machine learning and data mining.

He published 10 books, edited 11 books and more than 350 technical papers.


Outline

IntroductionAgent ModelMultiagent Environment— MAGEAgent CollaborationAgent Grid Intelligence PlatformApplicationsConclusions


Agent Computing

Agent computing is particularly well suited to the collaborative work. The agent-based computing paradigm has following features:

• Autonomy - agent operate without intervention;

• Social ability – agents interact each other using an agent communication language;

• Goal driven – agent exhibit goal-directed behavior;

• Reactivity – agents perceive and respond to their environment.


Semantic Web

Semantic Web will provide well-defined meaningwhich better enabling computers and people to work in collaboration


Grid Computing

Flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resource

From “The Anatomy of the Grid: Enabling Scalable Virtual Organizations”

Grid Forumwww.gridforum.org


Brain Meet Brawn

Ian Foster, Nicolas R. Jennings, Carl Kasselman. Brain Meet Brawn: Why Grid and Agents need each others. AAMAS’04

The Grid community has historically focused on "brawn": infrastructure, tools, and applications for reliable and secure resource sharing within dynamic and geographically distributed virtual organizations.

The agents community has focused on "brain": autonomous problem solvers that can act flexibly in uncertain and dynamic environments.


Research on MAS

1994：Multiagent Processing Environment MAPE

1996：Agent-based CSCW

1998：Common Agent Request Broker Architecture CARBA,

MAPE2

2000：Multiagent Environment MAGE

2002： Agent Grid Intelligence Platform AGrIP

2003： Dynamic Description Logic DDL

2004： Visual Agent Developing Tool VAStudio

2005： Ontology-based Knowledge Management KMSphere


Outline



BDI Model

Beliefs

Environment

Beliefs

PlansPlans

FIPA

GoalsGoals


Description Logic

Concepts and RoleTbox——AssertionsAbox——InstanceReasoning mechanism in terms of Tboxand Abox


Reasoning in DL

1) Subsumption2) consistency3) satisfiability4) instance checking


KB

TBox(Scheme)Man = Human ⊓ Male

Happy-father = Human ⊓ ∃ Has-child.Female⊓ …

Abox(Data)John: Happy-father

<John,Mary> : Has-child

Reasoning

Interface


Dynamic Description Logic

The primitive symbolsConcept name：C1, C2, …；

Role name：R1, R2, …；

Individual constant：a, b, c, …；

Individual variable：x, y, z, …；

Concept operation：¬, ⊓, ⊔, ∃, ∀；Axiom operation：¬, ∧, →∀；Action：A1, A2, …；

Action constraction：；(composition)，⋃ (alternation)，* (repeat)，？(test)；Action variable：α,β, …；

Axiom variable：ϕ, ψ, π, …；

State variable：u, v, w, …；



Concepts in DDL are defined as the following:

(1) Primitive concept P, top ⊤ and bottom ⊥are concepts.

(2) ¬C, C⊓D, C⊔D are concepts.

(3) ∃R.C, ∀R.C are concepts.


An action description is the form of),(),...,( 1 AAn EPxxA =

where(1) A is the action name.(2) x1, …, xn are individual variables, which denote the objects the action operate on.(3) PA is the set of preconditions, which must be satisfied before the action is executed.(4) EA is the set of results, which denote the effects of the action.



DDL Semantics

Actions in DDL are defined as the following: Atom action A(a1, …, an) is action.If α and β are actions, then α;β, α⋃β, α* are actions;If ϕ is an assertion formula, then ϕ ? is action.


Metal State ModelMental State: <K, A, G, P, I >，

Where

K beliefA actionG goalP planI intention


Belief

K = <T, S, B >

T: OntologyS: ConstrainsB: Current belief


Belief Revision

AddBelief(F, B) {F′ ← Extend(F); Foreach ϕ∈F′ do

If ¬ ϕ ∈B Then B ← B − {¬ ϕ}；B′ ← Extend(B∪F);If Consistent(B′) Then Return B′;

Else{Let {ψ,¬ψ} = ConflictSet(B′);If ψ∈B Then Return B′ − {ψ};

Else If ¬ψ∈B Then Return B′− {¬ψ};Else Return error;

}}


GoalLet A be a set of actions, L be a set of assertions, G can be defined

recursively ：

(1) A⊆G，A is basic action；

(2) If ϕ ∈L，then achieve(ϕ )∈G；

(3) If ϕ ∈L，then ϕ?∈G；

(4) If δ1, δ2∈G，

then δ1;δ2∈G，δ1⋃δ2∈G，δ1*∈G；


Static Plan

δ1 ⇐ ϕ | δ2

δ1∈G, δ2∈G, ϕ ∈L。

δ1: Rule headerδ2: Rule body ϕ: Rule guard condition

go_floor(x)⇐@At(elevator, y) | [y<x?;up(x)∪y>x?;down(x)]


Dynamic PlanPlan(δ, B) //Goal δ， B Current belief set

{If perform action α B⊢Tδ Then

{ Return B；Enqueue(α, P)；}Else {

look for subgoals to reach goal ；computing Eα and δ for each subgoal;computing the order of subgoalsselect optimal subgoal plan；sorting subgoals and form sequence δ1,…,δ n

For π = δ1 to δ n doB = Plan(π,B)

}}


Petri Net

OWL-S DDL

OWL-S

Interpreter

Petri NetAnalysor

Services

Petri Net

Generator

Incidences matrix+DDL


Emotion Agent

SensorSensor

EffectorEffector

BeliefBelief

EnvironmentEnvironment

Rational Inf.Rational Inf.

IntentionIntention

Emotion KB Emotion KB

Emotion Inf.Emotion Inf.

DesireDesire

PlanningPlanning


Agent Architecture

Agent kernel

SensorFunctionModuleInterface

Resource Database

Engine

Communicator

Scheduling

Function Component

Plug-INs

Reasoning

Negotiation

coOperation

others

Plug-inManager

TaskDatabase


Agent Life Cycle

Waiting Suspended

Transit Initiated

Active

Wake Up

Move

ExecuteInvoke

ResumeSuspend

Destroy

Quit

Create

Dead

Wait


Outline



Multiagent Environment MAGE

Req

uirem

ent A

nalysis

System

Design

System Development

Beh

aviour

Lib

rary

Agen

t Lib

rary

Agen

t Society

System

Dep

loymen

t

VAStudio MAGE Running Support

AUMP


Multiagent System Design Procedure

Use Cases Model

FunctionDescription

Activity Model

Reaction Rule Model

Inference Model

BehaviorDescription State model Interactive

ModelPlan Model

Organization Model

Agent ModelAgent Description

Ontology Model

OntologyDescription

Platform ModelPlatformDescription

一

二

三

四


AUML—Use Cases

actor

use case:goal

system

<<include>>

association

generalization

include system boundary

<<extend>>

actor use case

extend

Actor1

Use Case 5:Goal5

<<Extend>>

<<Include>>

System1

Actor2

Use Case1:Goal1

Use Case 4:Goal4

Use Case 3:Goal3

Use Case 2:Goal2


AUML—Activity Diagram

activity(:goal)

activity

start end decision

inputoutput

output event input event

fork or jointransition role

role

Symbols in Activity Diagram

(condition)

Activity1

Condition1Output1 Input1

Activity2:Goal1

Activity3

Condition2

Activity4

Role1 Role2


AUML—Interactive Diagram

role

role lifeline and split and combination or split or combination xor split xor combination

× ×

timelimit split

T

thread asynchronous message synchronous message

com-act: content com-act: content

and split and combination or split or combination decision split decision combination

× ×

terminate of thread

Role1 Role2 Role3

TX

InformIf4 Cancel

QueryRef2QueryRef1

InformRef2

InformRef1


AUML—State Diagram

initial state finial state transition

state

state

event/action

State1Event1/action1

State1Event4/action4

Event2/action2 Event3/action3


AUML—Reaction Diagram


AUML—Inference Model


AUML—Plan Model


AUML—Agent Class Diagram

agent classgoal

role: modelontology-classreaction-rule-libaction-librational-rule-lib

activity:Goal1

agent class

activity

association

role1,role2

Agent-class1Goal1

Role1: Activity1Ontology-class1

Agent-class2Goal2

Activity1: Role1,Role2Activity2: Role1

Ontology-class1Action-lib1

Activity1:Goal1

Role1 Role1,Role2

Activity2:Goal2

Role1


AUML—Organization Diagram

organization

agent

control relationship

control

agent

organization

peer relationship

peer

benevolence relationship

benevolence

benevolence

dependency relationship

dependency

dependency

Organization1

Agent1

Agent2

Agent3

Agent4

Agent5

peer

control

control control

control

benevolence

dependency


AUML—Ontology Diagram

association generalization

name

attr1: datatypeattr2: datatype

ontology class aggregate

role1 association-name role2

multiplicity1 multiplicity2

multiplicity1 multiplicity2

Class1


Class3


Class2


Class6


Class4


Class5


role1 association1 role2

0..1 *

1

1 *


AUML—Platform Diagram

communicationagent platform

agent platform

agent agent movement

moveagent:agent class

goal1,goal2

agent clone

clone

move

Agent platform1

Agent platform2

TCP/IP

Agent1:Agentclass1

Goal1

Agent1:Agentclass1

Goal1

Agent2..5:Agentclass1

Goal2

Agent6:Agentclass2

Goal3

Agent7:Agentclass2

Goal3clone

move


AUMP —Source Code

MAGE的行为类结构


Compare AUML with UML

ModelElement

Classifier

Feature

StructuralFeature

BehavioralFeature

Attribute

Operation Method

Class

Relationship

Generalization

Association

AssociationEnd

AgentClass

Organizational

Relationship

Control Peer

Benevolence

Dependency

Goal


Compare AUML with UML

Use Cases Model

Function

Activity Model

Reaction Model Inference Model

BehaviorState Model Interactive

ModelPlan Model

Organization Model

Agent ModelAgent

Ontology Model

Ontology

Platform ModelPlatform

一

二

三

四


Agent Unified Modeling Platform —AUMP


Computation Model for Agent

AgentClient/Service

AgentClient/Service

Soft Bus

Agent Service


Common Object Request Broker Architecture


Common Agent Request Broker Architecture

ApplicationPattern

ApplicationPattern

ApplicationFacilities

ApplicationFacilities

Agent Request BrokerAgent Request Broker

AgentServices

AgentServices


VAStudio ArchitectureAgent Society

Agents

Behaviours

Editor

Agent Library

Behaviour Library


VAStudio Architecture

VAStudio Design PlatformDesign GUI

TemplateLibrary

Composition

AgentClone FSM

Process Design

VAStudio Develop PlatformDevelop GUI

Edit Compile Debug Test

Process Design

VAStudio Run PlatformRun Monitor

AgentWorkList

ADL/BDLLoader

ReasonerLoader

ProcessEntity

MAGE

LibraryInterface

WSInterface

OntologyInterface


Behavior Library

• Data Package

package intsci.ace.data

• Neural Network Package

package intsci.ace.neural

• Learning Packagepackage intsci.ace.learning

• Data Mining Package

package intsci.ace.mining

• Language Processing Package

package intsci.ace.language


Behavior Library• Graphic Package

package intsci.ace.graphics

• Image Package

package intsci. ace .image

• Search Package

package intsci.ace.search

• Expert System Package

package intsci.ace.expert

• Model Package

package intsci.ace.model

• Decision Making Packagepackage intsci.ace.decision


Screenshot of VAStudio


VAStudio：Visual Agent Studio

Select agent society


VAStudio：Visual Agent Studio

Select behavior from behavior lib.


Create Agent by VAStudio

Add behavior



Fill out behavior parameters



Build agent (code generation)


Build Multiagent System by VAStudio

Auction protocol using FSM



Seller and buyer using clone



Compile to generate the auction system



Run the auction

system in MAGE



Display the agent running

procedure



Seller agent running


Run-Time Platform

MAGE

主体

Agent Management

System

DirectoryFacilitator

主体Agent

Message Transport System (MTS)

Software

AgentLibrary

FunctionComponent

Other Agent Platforms

Message Transport System (MTS)


AgentCities

Beijing!


MAGE ComparisonPhase

Agent Platform AgentBuilder Jack Zeus MAGE

Analysis

Completeness: ★★★ ★★★★ ★★★★★

Applicability: ★★★ ★★★ ★★★★

Complexity: ★★★★★ ★★★★★ ★★★★

Reusability ★★★ ★★★ ★★★★★

Design

Completeness: ★★★ ★ ★★★★ ★★★★★

Applicability: ★★★ ★ ★★★ ★★★★

Complexity: ★★★★ ★ ★★★ ★★

Reusability ★★★★

★★★ ★★★★

Development

Completeness: ★★★★★ ★★★★ ★★★★★ ★★★★★

Applicability: ★★★ ★★★★★ ★★★ ★★★★

Complexity: ★★★★ ★★ ★★★★ ★★★

Reusability ★★ ★★★★★★ ★★ ★★★

Deployment

Completeness: ★★★ ★★★ ★★★★ ★★★★★

Applicability: ★★★ ★★★★ ★★★ ★★★

Complexity: ★★★★ ★★★ ★★★★ ★★★★★

Reusability ★

★★★

★★★

★★★

★★★


Outline



Agent Collaboration

ACLWorking Flow Ontology-based Knowledge ManagementPolicy DrivenPlanning


Ontology

In philosophy, an ontology is a theory about the nature of existence.

An ontology is a document or file that formally defines the relations among terms.

An ontology is a formal, explicit specification of a shared conceptualization.

The most typical kind of ontology for the Web has a taxonomy and a set of inference rules.


Semantic Web Layer Cake

10 Feb 2004

W3C

Recommendation

OWL

by Tim Berners-Lee


Collaborative Architecture

Agent B

Goal Instance

Agent A

Goal Instance

has(1,n)

compatible goals = collaboration partners

automated collaborationexecution

KMSphere

uses(1,n)

has(1,n)

uses(1,n)

Collaboration Service

Web Service

Goal Template Repository

ServiceRepository

Collaboration Service

Web Service

OwnerOwnergoal assignment

service discovery

owns


KMSphere


KMSphere

Email Document File Image Video Web

Ontology Acquisition

Knowledge organization

Knowledge Distribution

KnowledgeApplication


KMSphere


KMSphere Demo

Create ontology by hand


KMSphere Demo

Ontology acquisition

from databases


KMSphere Demo

Ontology acquisition from text


KMSphere DemoEdit

ontology


KMSphere Demo

Ontology consistency

check


KMSphere Demo

RDQL (RDF Data

Query Language)


Semantic Web ServicesDefine exhaustive description frameworks for describing Web Services and related aspects (Web Service Description Ontologies)

Support ontologies as underlying data model to allow machine supported data interpretation (Semantic Web aspect)

Define semantically driven technologies for automation of the Web Service usage process (Web Service aspect)


OWL-S

Service

ServiceModel

ServiceGrounding

ServiceProfile

Presents(What it does)

Support

How to access it

Described by

How it works


OWL-S Context

Service Resource User Provides

Described by

Service Model

U-Context

Show U-Context

Service Grounding

SupportsPresents

Uses

Service Profile

S-Context

R-Context

Show R-Context

Show S-Context


Service Description Language SDLSIN

<asdl-descr>::=(ctype:service-name name:context context-name+:types (type-name = <modifier> type)+:isa name+:inputs (variable: <modifier> put-type-name)+:outputs (variable: <modifier> put-type-name)+:input-constraints (constraint)+:output-constrains (constraint)+:io-constrains (constraint)+:concept-description (ontology-name = ontology-body)+:state-language name:concept-language name:attributes (attributes-name : attributes-value)+:text-description name

)


Policy Driven

p S A S U=< >trigger goal，，，

Strigger: set of states for policy executionA: set of actionsSgoal : set of goalsU: set of utility functions

Goal stateoptimizationAction

policyGoal policy Utility

policy

Modelingplanning

Modelingoptimization


Action Policy

<ActionPolicy> :: =<Name><Parent><Performative><Type><Subject><Object><Action><Precondition><ConstraintLanauge>

<Duration><Priority>

Access Control PolicyObligation Policy


Action Policy

Gold-Silver Action Policies

G: IF (RTG > 100 msec) THEN (Increase CPUG by 5%)

S: IF (RTS > 200 msec)THEN (Increase CPUS by 5%)

Overlapping Action Policies

Conflict if CPU (almost) fully utilized!


Goal Policy

<GoalPolicy>::= <Name><Performative><Subject><Object><Precondition><Postcondition><ConstraintLanauge><Duration><Priority>

( :name goalpolicy1:performative “Achieve”:subject ftpagent:precondition between(clienthost, 192.168.0.0,

192.168.0.255):postcondition greater(bandwidth(clienthost), 1M)

)


Optimization

<UtilityPolicy>::= <Name><Performative><Subject><Object><Precondition><UtilityFunction><ConstraintLanauge><Duration><Priority>

( :name utilitypolicy1:performative “Optimize”:subject httpagent:utilityfunction

)∑∑ +=

ii

ii clientmeresponsetiwclientbandwidthwf )()( 21


Plan Representation

A plan Π is a triple <SO, OO, CS>

SO: a set of action-steps, OO: a set of ordering constraints on the actions in SO.CS: a set of variable-binding constraints between the

variables of the action-steps in SO and other variables or constants.


Distributed Multiagent Plan Algorithm

Input: a set of action-templates OPS, a set of capabilities of agents CaS , initial state I , and goal G

Output: Plan Π

),,,( GICaSOPSPlanMultiAgentdDistribute −−


Distributed Multiagent Plan AlgorithmProcedure:1. Inform all agents that I will start planning, and wait for them

finishing current actions and entering plan state.2. Initialize: Build an empty plan where

，，， . Decompose goalinto a set of sub-goals through adding to

according to every literal .3. Invoke recursive function .4. Taking effect: For each action executed by other agent, request

it to add the action. For each constraint related to the actionsexecuted by other agents, request it them to add the constraints.

5. Inform all agents the planning finishes, thus they can continue their plans.

6. If the invoking succeeds, then return success, otherwise return fault.

⟩⟨=Ψ CLSCSOOSO ,,,},{ GoalInitSO = φ=OO φ=CS φ=CLS G

GS Goalg, GS

Gg∈),( GSStepNextGenerate Ψ−−=ϕ


Outline



Agent Grid

Resource-specific implementations of basic servicesE.g., Transport protocols, name servers, differentiated services, CPU schedulers, public keyinfrastructure, site accounting, directory service, OS bypass

Resource-independent and application-independent servicesauthentication, authorization, resource location, resource allocation, events, accounting,

remote data access, information, policy, fault detection

DistributedComputing

Toolkit

CommonResources

AgentEnvironment

DevelopingToolkits

Data-Intensive

ApplicationsToolkit

CollaborativeApplications

Toolkit

RemoteVisualizationApplications

Toolkit

ProblemSolving

ApplicationsToolkit

RemoteInstrumentation

ApplicationsToolkit

ApplicationsService

IMS

Biology

Power Supply

E-Business

Environment


Agent Grid Intelligence Platform AGrIP

Information Information SoursesSourses

WebWeb

DatabasesDatabases

Stream Stream MediaMedia

MiddelwareMiddelwareMiddelware

DSSDSSDSS

MSMinerMSMinerMSMiner MIRESMIRESMIRES

MAGEMAGE

KMSphereKMSphereKMSphere

GHuntGHuntGHunt OKPSOKPSOKPS

ApplicationsApplications

E-BEE--BB E-GEE--GG IEIEIE IBIBIB SimulSimulSimul CorlCorlCorlDiag.DiagDiag..

CADCADCAD

GISGISCBRCBR


Data Mining Platform MSMiner

MSMiner Architecture

Data resources

数据仓库

Mete D

ata Managem

ent

Extract Transform Load

Topic2Topic1 Topicn

OLAP

Data Mining

...

MSDM

MSMetadata

MSETL

Data Warehouse

MSOlap

2006-6-3 AGrIP-SELMAS Zhongzhi Shi 99用户接口

信息定制文本信息检索基于内容的图像检索

数据表格检索

U R L 数据库

信息收集S p i d e r

文本数据库图像数据库

倒排文件索引

表格数据库库

全文检索

目录检索

概念语义

检索

文本目录索引

语义索引

专题检索

专题索引

网页内容解析

切词倒排

文本聚类

文本分类

信息抽取

概念空间

共现分析

图像分类

专题索引

文本摘要数据

库

文本摘要

图像标注

图像索引

目录检索

图像语义

检索

网址解析

URL

管理器

表格分类

表格索引

图像语义

分析

图像语义索引

I n t e r n e t

词库语料库

用户导航

网络日志O L A P信息检索管理

检索请求

检索结果

Search Enginer GHunt


Expert System OKPS

KnowledgeAcquisition

Knowledge Base

Inference EngineData Mining

InterfaceData Warehouse Visualization

OLAP


Outline



Emergency Interactive Systsem GEIS

Emergency

Signal

Emergency Center

Emergency Case Base

Retriv

al Case Base

Solutions

Decision Making in Terms of Reasoning

EmergencyExecution


GEIS Interface


Receive Crime Interface


Process Crime Interface


Geographical Information System


CBR-Based Reserve Plan Against Emergency


CBR-Based Reserve Plan Against Emergency


Fish Field Forecast in Eastern Sea of China


DSS for Oil Pipeline Design


Conclusions

Proposed Agent Models

MAGE Satisfies the Software Engineering for Large Multiagent Systems

Collaborative Working Agents on Semantic Grid

AGrIP is a Powerful Platform for Constructing Large Complex Systems


Published Paper List• Zhongzhi Shi, He Huang, Jiewen Luo, Fen Lin, Haijun Zhang. Agent-based Grid Computing, JAMM, 2006•Jiewen Luo, Zhongzhi Shi, Maoguang Wang, Jun Hu: AGrIP: An Agent Grid Intelligent Platform for Distributed System Integration. APWeb Workshops 2006: 590-594•Zhongzhi Shi. Autonomic Semantic Grid. Keynote speaker. IFIP AIAI2005, Sept., 7-9, 2005•Zhongzhi Shi, Mingkai Dong, Yuncheng Jiang, Haijun Zhang. A Logic Foundation for the Semantic Web. Science in China, Series F Information Sciences, 48(2):161-178, 2005•Zhongzhi Shi. Agent-based Semantic Grid. China Agent System, IFMAS, 2004, 12, 13-17•Zhongzhi Shi, Haijun Zhang, Mingkai Dong, MAGE: Multi-Agent Environment, ICCNMC-03, IEEE CS Press, pp.181-188, 2003•Zhongzhi Shi, Mingkai Dong, Haijun Zhang, Qiujian Sheng. Agent-based Grid Computing.Keynote Speech, International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Wuxi, Dec. 16-20, 2002•Zhongzhi Shi, Wenpin Jiao, Qiujian Sheng. Agent-Oriented Software Methodology.CEEMAS2001, Cracow, Poland,2001 •Zhongzhi Shi. Agent-based E-commerce. Invited Speaker, DS-9, Hong Kong, 2001


Published Paper List• Zhongzhi Shi, Ju Wang, and Hu Cao, The Security Mechanism In Multi-Agent System AOSDE, in: LNAI, Vol. 1733, pp.62-71 PRIMA’99, Dec. 1999.• Zhongzhi Shi, Qijia Tian, Yunfeng Li. RAO Logic for Multiagent Framework. Journal of Comouter Science and Technology, July, 1999• Zhongzhi Shi, Hu Cao, Yunfeng Li, Wenjie Wang, Tao Jiang, A Building Tool for Multiagent Systems: AOSDE. IT \& Knows, IFIP WCC '98, 1998• Zhongzhi Shi, Qijia Tian, Yunfeng Li, RAO Logic for Multiagent Framework, DAIMAS'97, St. Petersburg, Russia, 1997. • Zhongzhi Shi, Tao Wang, An Agent based CSCW System Architecture, Chinese Journal of Electronics, 6(1), 1996. • Wenjie Wang Zhongzhi Shi Qijia Tian Tao Wang. An Agent Belief System for Multi-agent System. IEEE SMC'96, 2065-2069, 1996. • Zhongzhi Shi, etc., MAPE: Multi-Agent Processing Environment, PRICAI-94, 1994.


THANK YOU!

Intelligence Sciencehttp://www.intsci.ac.cn/

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