1. Information Agents RETSINA & WebMate CS525M Multi Agent Systems, WPI Presented by: Jian, Jinhui

2. 2 Name Matters zRETSINA: Reusable Environment for Task-Structured Intelligent Networked Agents zRetsina: the wine of Greek Gods

3. 3 Outline zRETSINA yThe Functional Architecture yThe Agent Architecture yThe MAS Architecture zWebMate zConclusion

4. 4 What is RETSINA zRETSINA is a domain-independent and reusable infrastructure on which MAS systems, services, and components live, communicate, and interact. zRETSINA is an architecture for developing distributed intelligent software agents that cooperate asynchronously to perform goal-directed information retrieval and information integration in support of a variety of decision making tasks. zRETSINA is project done in the Robotics Institute, CMU

5. 5 The Functional Architecture

6. 6 The Functional Architecture (cont.) zInterface agents -- interact with users, receive user input, and display results. zTask agents -- help users perform tasks, formulate problem- solving plans and carry out these plans by coordinating and exchanging information with other software agents. zInformation agents -- provide intelligent access to a heterogeneous collection of information sources. zMiddle agents -- help match agents that request services with agents that provide services.

7. 7 Another Functional Architecture to compare with Info. Site Descriptor Match-Maker Info. Req. Facilitator Use Agent Info Prov. Facilitator Extractor Source Agent Info Prov. Facilitator Extractor Source Agent Extractor Source Agent Extractor Source Agent Info. Site

8. 8 The Agent Architecture

9. 9 Reusable Modules Inside an Agent zThe Communication and Coordination module accepts and interprets messages and requests from other agents. zThe Planning module takes as input a set of goals and produces a plan that satisfies the goals. zThe Scheduling module uses the task structure created by the planning module to order the tasks. zThe Execution module monitors this process and ensures that actions are carried out in accordance with computational and other constraints.

10. 10 The MAS Architecture

11. 11 Operating Environment zPlatform independent: yany platform that runs Windows, Linux, or Sun OS yPalmPilots zMultiple language support: Java, C/C++, Python, LISP, and Perl zNetwork support: TCP/IP, wireless, infrared, and serial connections

12. 12 Communication Infrastructure zPeer to Peer: Message Transfer (A2A) ysynchronous or asynchronous ymultithreaded communication zMulticast: Discovery Process (finding the infrastructure components) yinfrastructure components announce the presence yagents register themselves

13. 13 Agent Communication Language zKQML based yThe envelop yThe content zShared Dictionary: Ontology ydomain-specific taxonomies of concepts from the WordNet yterm similarity measurement

14. 14 MAS Management Services zMonitor yLogger: records the activity of the agents (e.g. entering/exiting, agent states, transitions, etc) yLogger Module: voluntarily provided by the agent zDebug y Activity Visualizer zLaunch yLauncher: configures and starts infrastructure components and agents (enable single point control)

15. 15 Performance Services zNo performance services support yet (only failure monitoring) zbut agents can do it by themselves : yself-monitoring yclone: task sharing

16. 16 Security zFunctionality yAgent authentication yCommunication security yComponent integrity zMechanism ySSL (public/ private keys) yunique Agent Id as the private key

17. 17 Name to Location Mapping zRETSINA ANS (Agent Naming Services) yAgent Id --> Address mapping ymultiple and redundant ANS for robustness

18. 18 Middle agents (the matchmakers) zProvide a registry of services yadvertisement yrequest zService matching using the LARKS matching engine yboth syntactic and semantic analysis yboth exact and partial matches

19. 19 MAS Inter-operation zThere are more than one agent architectures ydifferent communication languages ydifferent MAS architectures zOOA-RETSINA inter-operation support only (RETSINA-OOA InterOperator) yhelp finding each other yhelp talking to each other

20. 20 The Applications Based on RETSINA zShow up!Show up!

21. 21 WebMate an information agent example zWebMate is a personal agent for World- Wide Web browsing that enhances searches and learns user interests.

22. 22 The Missions zProvides URL recommendations based on a continuously updated user profile zOffers ever more relevant web documents based on the "Trigger Pairs Model" approach to keyword refinement zResponds to user feedback by selecting features from documents the user finds relevant and incorporating these features into the context of new queries zCompiles a daily personal newspaper with links to documents of interest to the user (“pull”)

23. 23 The Architecture

24. 24 Learning User Preference zTF-IDF Value yTF (Term Frequency): measures how many times a word appears in a document. yIDF (Inverse Document Frequency): measures the number of documents containing a word zVector Space Model yrepresent each document as a vector in a vector space so that documents with similar content have similar vectors. yeach dimension of the vector space represents a word and its weight, which is a TF-IDF value. zMultiple TF-IDF Vector Model yuse multiple vectors to capture user’s multiple preferences

25. 25 Refining the User Query zTrigger Pairs Model yfind the word pairs that occur together yone word in the pair trigger the other (enlarging the user query) zUsing the user feedback yuser may give a “relevant” rating to a page ythe system will analyze the page using the context of keyword (i.e. the words near by) ythe system finds out the relevant keywords yenlarge the user query using the relevant keywords

26. 26 Pulling Relevant Contents zUse spider agents to grab data from different sites zUse Vector Space Model to measure relevance (using the user profile) zReturn only the relevant pages

27. 27 Conclusion zA good MAS infrastructure should support agents for different tasks, from different domains, and with different originalities. zThere are lots we can do beyond web search engines: yuser preference learning yuser query refinement

28. 28 Reference zKatia Sycara, Massimo Paolucci, Joseph Giampapa; “The RETSINA MAS Infrastructure”; TechReport CMU-RI-TR-01- 05; 2001 zKiren Chen, Katia Sycaca; “WebMate: A Personal Agent for Browsing and Searching”; The Robotics Institute, Carnegie Mellon University; 1998 zK. L. Clarc, V.S. Lazarou; “A Multiagent System for Distributed Information Retrieval on the World Wide Web”; 1997

29. 29 Questions to Ask for a MAS Infrastructure zWhat constitutes a MAS infrastructure zWhat functionality it supports zWhat characteristics it should have to enable value- added abilities zWhat its possible relation with and requirements it may impose on the design and structure of single agents

30. 30 The Principle in mind zThere are more than one MAS systems in the world (seems trivial, but…) yAgents of different kinds should be able to enter the system yAgents’ internal structure should be transparent to the system yAgents’ business should be left alone ( the ways to coordinate, negotiate, etc)