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
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Zhongzhi Shi MAGE

3. Introduction Humanized systems are man-made systems Closer To Humans,
Exhibit behaviors characteristic of natural living system
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4. Artificial Fish
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5. HUMANOID
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6. Robocup
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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
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8.  Outline Introduction Agent Model Overview of MAGE
Agent Supporting Environment
Agent Development Environment
Examples
Conclusions 
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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
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10. Dynamic Description LogicK B
TBox(Scheme)
Man = Human ⊓ Male
Happy-father = Human ⊓ ∃ Has-child.Female⊓ …
Abox(Data)
John: Happy-father
<John,Mary> : Has-child
Reasoning
Interface
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11. Belief Belief knowledge base K = <T, S, B > 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
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12. Belief Revision AddBelief(F, B) { F′  Extend(F); Foreach ϕF′ do
If   B Then B  B  { }；
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; }}
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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)若 1, 2G，则1;2G，1⋃2G，1*G；
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14. Goal
Target Set T：
(1) If  is a basic action α，then T =E  P，where
E is the result of α，
P is the preconditions α；
(2) If  has the form achieve()，then T = { }；
(3) If  is a test action ，then T = 。
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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 .
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16. Planning Static Planning, Planning rule: 1   | 2
Where 1G, 2G,  L.
1: rule header
2: rule body，
: rule guard
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17. Dynamic planning Possible state sets of 
Let  be a basic goal, T be the target set of  and α 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 α, 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 α1,…,αn at the same time, then all E1,…,En of α1,…,αn are called possible state sets of .
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18. Dynamic planning Planning algorithm 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
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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
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20.  Outline Introduction Agent Model Overview of MAGE
Agent Supporting Environment
Agent Development Environment
Examples
Conclusions 
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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.
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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
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23. Agent Architecture Agent Kernel Agent Architecture Engine Plug-INs
Function Component
Function interface
sensor
Engine
Communicator
Plug-in Manager
Plug-INs
Reasoning
Scheduling
Negotiation
Resource Database
Task
Database
Cooperation
Agent Kernel
Others
Agent Architecture
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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 agent’s goals, and it is also responsible for coordinating the agent interactions with other agents using given co-ordination protocols and strategies.
Scheduler plans the agent’s 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.
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25. Work Flow of MAGE Work Flow of MAGE Requirement Analysis
System Development
System Deployment
System Design
Behaviour Library
Agent Society
Agent Library
Work Flow of MAGE
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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.
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27. Agent development environment Design and Programming Tool VAStudio
MAGE Framework
Agent development environment
Modeling Tool AUMP
Design and Programming Tool VAStudio
Agent supporting environment
MAGE Framework
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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
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29.  Outline Introduction Agent Model Overview of MAGE
Agent Supporting Environment
Agent Development Environment
Conclusions
Examples 
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30. Agent Supporting Platform
Software
MAGE
Agent
Library
Directory
Facilitator
Agent Management
System
Agent 主体 主体
Function
component
Message Transport System (MTS)
Other Agent Platforms
Message Transport System (MTS)
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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.
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32.  Outline Introduction Agent Model Overview of MAGE
Agent Supporting Environment
Agent Development Environment
Examples
Conclusions 
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33. MAS hierarchical Model
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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.
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35. VAStudio Architecture
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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.
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37. Emotion Agent Planning Sensor Belief Emotion Inf. Emotion KB
Environment
Emotion KB
Rational Inf.
Effector
Intention
Desire
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38. MAGE Comparison 11/28/2018 Zhongzhi Shi MAGE Phase Agent Platform
AgentBuilder
Jack
Zeus
MAGE
Analysis
Completeness:
★★★
★★★★
★★★★★
Applicability:
Complexity:
Reusability
Design ★ ★★
Development
★★★★★★
Deployment
★★★
★★★
★★★
★★★
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39.  Outline Introduction Agent Model Overview of MAGE
Agent Supporting Environment
Agent Development Environment
Application Examples
Conclusions 
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40. Applications: spider URL Database URL agent DF Spider agent
Web page
Index agent DF
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41. Examples
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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
MAGE Container-1
Schedule Agent
Monitor Agent
Mining Task Agent 1
Mining Task Agent 2
Mining Task Agent φ
…………
MAGE Container-2
Schedule Agent
Monitor Agent
Mining Task Agent 1
Mining Task Agent 2
Mining Task Agent ψ
…………
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44. Grid-Based Emergency Interactive System GEIS
计算机网络系统
城市应急联动
和社会综合服务系统
有无线通信系统
联动中心终端设备
RDBMS
ODBMS
IFR…
IVR
CTI
呼叫管理中间件
支撑平台
RMS…
业务汇聚
MIS
MAP
CAD
中心辅助系统
其它… 防汛 医疗 市政 公安
专业应用系统
数据库系统 调度 大屏 监控
GPS
外部系统
指挥决策系统
预案管理
情报管理
指挥控制…
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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.
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46. Welcome Intelligence Science http://www.intsci.ac.cn/ 11/28/2018
Intelligence Science
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Zhongzhi Shi MAGE

47. Thank You
Question!
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Zhongzhi Shi MAGE