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 Zhongzhi Shi MAGE

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

4. Artificial Fish Zhongzhi Shi MAGE

5. HUMANOID Zhongzhi Shi MAGE

6. Robocup Zhongzhi Shi MAGE

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 Zhongzhi Shi MAGE

8. Outline • Introduction • Agent Model • Overview of MAGE • Agent Supporting Environment • Agent Development Environment • Examples • Conclusions  Zhongzhi Shi MAGE

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 Zhongzhi Shi MAGE

10. Dynamic Description Logic K B TBox(Scheme) Man = Human ⊓ Male Happy-father = Human ⊓ ∃ Has-child.Female⊓ … Abox(Data) John: Happy-father <John,Mary> : Has-child Reasoning Interface Zhongzhi Shi MAGE

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 Zhongzhi Shi MAGE

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; }} Zhongzhi Shi MAGE

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； Zhongzhi Shi MAGE

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= 。 Zhongzhi Shi MAGE

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 . Zhongzhi Shi MAGE

16. Planning • Static Planning, Planning rule: 1 | 2 Where 1G, 2G, L. 1: rule header 2: rule body， : rule guard Zhongzhi Shi MAGE

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 . Zhongzhi Shi MAGE

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 Zhongzhi Shi MAGE

19. Planning Algorithm Plan(,B) //Plan for goal , where B is current beliefs Begin If is executable in B and can realize Then ReturnB; Else Search for all sub-goals of ; Compute the priorities of these sub-goals; Order sub-goals and record them as1,…,n; For = 1tondo B = Plan(,B); Enqueue(, P)； B = (BP) E； ReturnB； EndIF End Zhongzhi Shi MAGE

20. Outline • Introduction • Agent Model • Overview of MAGE • Agent Supporting Environment • Agent Development Environment • Examples • Conclusions  Zhongzhi Shi MAGE

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. Zhongzhi Shi MAGE

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 Zhongzhi Shi MAGE

23. Agent Architecture Function Component Function interface sensor Engine Plug-in Manager Plug-INs Communicator Reasoning Scheduling Negotiation Resource Database Task Database Cooperation Agent Kernel Others Agent Architecture Zhongzhi Shi MAGE

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 • ResourceDatabase maintains a list of resources that are owned by and available to the agent. • TaskDatabase provides logical descriptions of tasks known to the agent. • Plug-InManager manages the components provided by MAGE or by users that can be plugged into agent kernel. Zhongzhi Shi MAGE

25. Work Flow of MAGE Requirement Analysis System Development System Deployment System Design Behaviour Library Agent Society Agent Library Work Flow of MAGE Zhongzhi Shi 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. Zhongzhi Shi MAGE

27. MAGE Framework Agent development environment Modeling Tool AUMP Design and Programming Tool VAStudio Agent supporting environment MAGE Framework Zhongzhi Shi MAGE

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 Zhongzhi Shi MAGE

29. Outline • Introduction • Agent Model • Overview of MAGE • Agent Supporting Environment • Agent Development Environment • Conclusions • Examples  Zhongzhi Shi MAGE

30. Agent Supporting Platform Software MAGE Agent Library Agent Directory Facilitator Agent Management System 主体 主体 Function component Message Transport System (MTS) Other Agent Platforms Message Transport System (MTS) Zhongzhi Shi MAGE

31. Agent SupportingPlatform • 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. Zhongzhi Shi MAGE

32. Outline • Introduction • Agent Model • Overview of MAGE • Agent Supporting Environment • Agent Development Environment • Examples • Conclusions  Zhongzhi Shi MAGE

33. MAS hierarchical Model MAS hierarchical Model Zhongzhi Shi MAGE

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. Zhongzhi Shi MAGE

35. VAStudio Architecture VAStudio Architecture Zhongzhi Shi MAGE

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. Zhongzhi Shi MAGE

37. Emotion Agent Planning Sensor Belief Emotion Inf. Emotion KB Environment Rational Inf. Effector Intention Desire Zhongzhi Shi MAGE

38. AgentBuilder Jack Zeus MAGE Analysis Completeness: ★★★ ★★★★ ★★★★★ Applicability: ★★★ ★★★ ★★★★ Complexity: ★★★★★ ★★★★★ ★★★★ Reusability ★★★ ★★★ ★★★★★ Design Completeness: ★★★ ★ ★★★★ ★★★★★ Applicability: ★★★ ★ ★★★ ★★★★ Complexity: ★★★★ ★ ★★★ ★★ Reusability ★★ ★★ ★★★ ★★★★ Development Completeness: ★★★★★ ★★★★ ★★★★★ ★★★★★ Applicability: ★★★ ★★★★★ ★★★ ★★★★ Complexity: ★★★★ ★★ ★★★★ ★★★ Reusability ★★ ★★★★★★ ★★ ★★★ Deployment Completeness: ★★★ ★★★ ★★★★ ★★★★★ Applicability: ★★★ ★★★★ ★★★ ★★★ Complexity: ★★★★ ★★★ ★★★★ ★★★★★ Agent Platform Reusability ★ Phase MAGE Comparison ★★★ ★★★ ★★★ ★★★ Zhongzhi Shi MAGE

39. Outline • Introduction • Agent Model • Overview of MAGE • Agent Supporting Environment • Agent Development Environment • Application Examples • Conclusions  Zhongzhi Shi MAGE

40. URL Database URL agent Spider agent Spider agent Spider agent Index agent Web page Database Applications: spider DF Zhongzhi Shi MAGE

41. Examples Zhongzhi Shi MAGE

42. A typical data mining workflow for classification Data Mining Workflow of Execution Engine NormalizationPhase AttributeReductPhase DiscretizationPhase 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. MAGE Container-1 ………… ScheduleAgent MonitorAgent Mining TaskAgent 1 Mining TaskAgent 2 Mining TaskAgent φ MAGE Container-2 ………… ScheduleAgent MonitorAgent Mining TaskAgent 1 Mining TaskAgent 2 Mining TaskAgent ψ Execution Process of Data Mining Workflow • Coordination Process of Agents Zhongzhi Shi MAGE

44. 支撑平台 呼叫管理中间件 指挥决策系统 CTI IVR IFR… 专业应用系统 外部系统 CAD MAP 城市应急联动 和社会综合服务系统 MIS 业务汇聚 RMS… 数据库系统 计算机网络系统 RDBMS ODBMS RDBMS ODBMS 公安 GPS 有无线通信系统 市政 监控 医疗 大屏 防汛 调度 联动中心终端设备 其它… 其它… 中心辅助系统 Grid-Based Emergency Interactive SystemGEIS 预案管理 情报管理 指挥控制… Zhongzhi Shi MAGE

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. Zhongzhi Shi MAGE

46. Welcome Intelligence Science http://www.intsci.ac.cn/ Zhongzhi Shi MAGE

47. Thank You Question! Zhongzhi Shi MAGE