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MAGE: A Multi-Agent Environment for Humanized Systems

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Title: MAGE: A Multi-Agent Environment for Humanized Systems


1
MAGE A Multi-Agent Environment for Humanized
Systems
  • Zhongzhi Shi
  • Fen Lin
  • 30 October 2005

2
Outline
  • Introduction
  • Agent Model
  • Overview of MAGE
  • Agent Supporting Environment
  • Agent Development Environment
  • Examples
  • Conclusions

3
Introduction
  • Humanized systems are man-made systems
  • Closer To Humans,
  • Exhibit behaviors characteristic of natural
    living system

4
Artificial Fish
5
HUMANOID
6
Robocup
7
Introduction
  • Agent plays an important role in humanized
    systems
  • Agent exhibit human intelligence
  • Multi-agent systems facilitate the realization of
    humanized systems
  • adaptive,open,distributed systems
  • based on negotiation or cooperation
  • Multi-agent systems gains more and more interest
    in both the research area and the industry
  • migrate from the research laboratories to the
    software industry

8
Outline
  • Introduction
  • Agent Model
  • Overview of MAGE
  • Agent Supporting Environment
  • Agent Development Environment
  • Examples
  • Conclusions

?
9
Agent Model
  • Focuses on mental state description of agent.
  • Here we use dynamic description logic (DDL) for
    agent representation with a clear and formally
    defined semantics.
  • By combining the features of knowledge
    representation and reasoning both in static and
    dynamic domains, DDL is effective and significant
    for agent reasoning and programming

10
Dynamic Description Logic
11
Belief
  • Belief knowledge base K ltT, S, B gt where
  • T set of axioms about concepts and their
    definitions
  • S set of causality constraint axioms that are
    used to keep the consistence and complement of
    belief knowledge base
  • B current state

12
Belief Revision
  • AddBelief(F, B)
  • F' ? Extend(F)
  • Foreach ??F' do
  • If ? ? ?B Then B ? B ? ? ?
  • B' ? Extend(B?F)
  • If Consistent(B') Then Return B' Else
  • Let ?,?? ConflictSet(B')
  • If ??B Then Return B' ? ?
  • Else If ???B Then Return B'? ??
  • Else Return error

13
Goal
  • Goal Set of goal G
  • (1) A?G,A basic action
  • (2) If ? ?L,then achieve(? )?G
  • (3) If ? ?L,then ???G
  • (4)? ?1, ?2?G,??1?2?G,?1??2?G,?1?G

14
Goal
  • Target Set T?
  • (1) If ? is a basic action a,then T? E? ?
    P?,where
  • E? is the result of a,
  • P? is the preconditions a
  • (2) If ? has the form achieve(?),then T? ?
  • (3) If ? is a test action ,then T? ??

15
Goal Generation Rule
  • ?1, , ?n ? ?
  • where
  • ?1, , ?n are assertion formulas,
  • ? is a goal
  • When the assertions ?1, , ?n hold,
    agent will generates a new goal ?.

16
Planning
  • Static Planning, Planning rule
  • ?1 ? ? ?2
  • Where ?1?G, ?2?G, ? ?L.
  • ?1 rule header
  • ?2 rule body,
  • ? rule guard

17
Dynamic planning
  • Possible state sets of ?
  • Let ? be a basic goal, T? be the target set of
    ? and a be an action. For the result E? of, ?, if
    there exists ? ?T? such that ? ?E?, i.e. a
    sub-goal of ? can be realized by the execution of
    a, then is E? called a possible state set of ?.
    If the target formulas T? of ? appear in the
    result E?1,,E?n of actions a1,,an at the same
    time, then all E?1,,E?n of a1,,an are called
    possible state sets of ?.

18
Dynamic planning
  • Order of sub-goals
  • Assuming that ? is a goal and can be divided
    into two sub-goals ?a and ?b, then ?a has
    precedence over ?b if ?a must be achieved before
    ?b in order to achieve ?, which is denotes as
    ?a??b. Otherwise there is no need to achieve ?b
    before ?b and this can be denoted by ?a??b.
  • Planning algorithm

19
Planning Algorithm
  • Plan(?,B) //Plan for goal ?, where B is current
    beliefs
  • Begin
  • If ? is executable in B and can realize ? Then
  • Return B
  • Else
  • Search for all sub-goals of ?
  • Compute the priorities of these
    sub-goals
  • Order sub-goals and record them as ?1,,?
    n
  • For ? ?1 to ? n do
  • B Plan(?,B)
  • Enqueue(?, P)
  • B (B ? P?) ? E?
  • Return B
  • EndIF
  • End

20
Outline
  • Introduction
  • Agent Model
  • Overview of MAGE
  • Agent Supporting Environment
  • Agent Development Environment
  • Examples
  • Conclusions

?
21
Overview of MAGE
  • MAGE (Multi-Agent Environment) is an integrated
    tool suite for constructing multi-agent systems
  • MAGE is designed to be compliant with FIPA, its
    agents is comply with FIPA Agent Management
    Specification.
  • MAGE provide friendly and easy-to-use
    human-computer interface through visual
    programming paradigm and pick-and-choose
    principle.

22
Overview of MAGE
  • MAGE supports the entire process of AOSE
  • Analysis, Design , Development and Deployment
  • MAGE consists of agent supporting environment and
    agent development environment.
  • AUMP, VAStudio and Agent Supporting Environment

23
Agent Architecture
Function Component
Function interface
sensor
Engine
Plug-INs
Plug-in Manager
Communicator
Reasoning
Scheduling
Negotiation
Resource Database
Task Database
Cooperation
Agent Kernel
Others
Agent Architecture
24
Agent Architecture
  • Agent Kernel.
  • Sensor perceives the outside world.
  • Function Module Interface makes an effect to the
    outside.
  • Communicator handles communications between the
    agent and other agents.
  • Coordinator makes decisions concerning the
    agents goals, and it is also responsible for
    coordinating the agent interactions with other
    agents using given co-ordination protocols and
    strategies.
  • Scheduler plans the agents tasks based on
    decisions taken by the Co-ordination
  • Resource Database maintains a list of resources
    that are owned by and available to the agent.
  • Task Database provides logical descriptions of
    tasks known to the agent.
  • Plug-In Manager manages the components provided
    by MAGE or by users that can be plugged into
    agent kernel.

25
Work Flow of MAGE
Requirement Analysis
System Development
System Deployment
System Design
Behaviour Library
Agent Society
Agent Library
Work Flow of MAGE
26
Work Flow of MAGE
  • Analysis developing an understanding of the
    system and its structure.
  • Design transform the analysis models into a
    sufficiently low level of abstraction that
    traditional design techniques
  • Development constructing a functional solution
    to the problem. Here we divide development phase
    into three steps building behaviours, building
    agents and building system.
  • Deployment actualizing the solution to the real
    problem in the given domain and managing the
    runtime environment.

27
MAGE Framework
Agent development environment
Design and Programming Tool VAStudio
Modeling Tool AUMP
Agent supporting environment
MAGE Framework
28
MAGE Framework
  • AUMP is designed for system analysis and design
    stages
  • VAStudio is for system design, development and
    deployment stages
  • Agent Supporting Environment provides agent
    running environment

29
Outline
  • Introduction
  • Agent Model
  • Overview of MAGE
  • Agent Supporting Environment
  • Agent Development Environment
  • Conclusions
  • Examples

?
30
Agent Supporting Platform
Software
MAGE
Agent Library
Agent
Agent Management System
Directory Facilitator
??
??
Function component
Message Transport System (MTS)
Other Agent Platforms
Message Transport System (MTS)
31
Agent Supporting Platform
  • AMS offers white pages services to other agents.
  • DF provides yellow pages services to other
    agents.
  • MTS is the default communication method between
    agents on different agent platforms.
  • Agent is the fundamental actor in MAGE which
    combines one or more service capabilities into a
    unified and integrated execution model that may
    include access to external software, human users
    and communications facilities.
  • Software describes all non-agent, executable
    collections of instructions accessible through an
    agent.
  • Moreover, two auxiliary modules are provided to
    support designing agent systems Agent Library
    and Function Component.

32
Outline
  • Introduction
  • Agent Model
  • Overview of MAGE
  • Agent Supporting Environment
  • Agent Development Environment
  • Examples
  • Conclusions

?
33
MAS hierarchical Model
MAS hierarchical Model
34
MAS hierarchical Model
  • A component can be almost any reusable unit
  • A behaviour is the capability unit of agent
  • An agent can be seen as a software entity
  • An agent society is composed of agents, including
    the interaction between them, the protocol they
    use, the ontology they adopt.
  • two ways to develop a multi-agent system
    top-down approach, bottom-up approach.

35
VAStudio Architecture
VAStudio Architecture
36
VAStudio Architecture
  • VAStudio design module supports behaviour and
    agent design
  • Flow Chart mode, FSM mode, clone mode and ADL
    mode
  • VAStudio programming module supports code
    editing, compiling and debugging.
  • MAS running environment interface supports the
    important API using in VAStudio by MAGE agent
    supporting environment
  • Toolkits behaviour editor, protocol editor,
    ontology editor and strategy editor. behaviour
    library and agent library makes it possible to
    reuse resources.

37
Emotion Agent
Planning
Sensor
Belief
Emotion Inf.
Environment
Emotion KB
Rational Inf.
Effector
Intention
Desire
38
MAGE Comparison

???
???
???
???
39
Outline
  • Introduction
  • Agent Model
  • Overview of MAGE
  • Agent Supporting Environment
  • Agent Development Environment
  • Application Examples
  • Conclusions

?
40
Applications spider

DF
41
Examples
42
Data Mining Workflow of Execution Engine
  • A typical data mining workflow for classification

Normalization Phase
AttributeReduct Phase
Discretization Phase
Training Set and Testing Set
Training Set and Testing Set
Models and Predicting Results
Testing Indexes
step1
step1
step1
step1
stepN1
stepN2
stepN3
stepN4
Preprocessing
Evaluating
Training Testing
43
Execution Process of Data Mining Workflow
  • Coordination Process of Agents

44
Grid-Based Emergency Interactive System
GEIS
????
????
????
45
Conclusions
  • MAGE, a multi-agent environment for humanized
    systems, with a suit of tools to support
    agent-oriented requirement analysis, design,
    development and deployment
  • DDL for Agent Model
  • MAGE provides learning behavior library with rich
    learning algorithms for autonomous mental
    development of humanized systems
  • Future work is going to development of mind agent
    with emotion and evolution which will improve the
    performance of humanized systems dramatically.

46
Welcome
  Intelligence Science http//www.int
sci.ac.cn/
47
Thank You
Question!
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