Parallel and Distributed Intelligent Systems: Multi-Agent Systems and e-Commerce

1. Parallel and Distributed Intelligent Systems: Multi-Agent Systems and e-Commerce Virendrakumar C. Bhavsar Professor and Director, Advanced Computational Research Laboratory Faculty of Computer Science, University of New Brunswick Fredericton, NB, Canada bhavsar@unb.ca

2. Outline • Past Research Work • Current Research Work • Multi-Agent Systems • ACORN and Extensions • Multi-Agent Systems and E-Commerce Applications • Areas for Collaboration • Conclusion

3. Past Research Work • B. Eng. (Electronics and Telecommunications) • University of Poona, India • Project: 4-Bit Calculator • M.Tech. (Electrical Eng. - specialization: Instrumentation, Control, and Computers) Indian Institute of Technology, Bombay, India • Thesis: Special Purpose Computers for Military Applications with Emphasis on Digital Differential Analysers (DDAs) •  Ph. D. (Electrical Eng.) • Indian Institute of Technology, Bombay, India • Parallel Algorithms for Monte Carlo Solutions of Linear Operator Problems

4. Past Research Work •  Parallel/Distributed Processing • - Parallel Computer Architecture • Design and Analysis of Parallel Algorithms for • Monte Carlo Methods, Pattern Recognition, • Computer Graphics, Artificial Neural Networks, • Computational Physics, and other applications • Real-time and Fault-Tolerant Systems • for Process Control and On-Board Applications •  Artificial Neural Networks • - with Dr. Ghorbani •  Learning Machines and Evolutionary • Computation • - with Dr. Ghorbani and Dr. Goldfarb

5. Past Research Work • Computer Graphics (with Prof. Gujar) • Modeling of 3-D Solids • Generation and Rendering of Interpolated Objects • Algebraic and Geometric Fractals • Parallelization of Computer Graphics Algorithms • Visualization (with Dr. Ware) PVMtrace: Visualization of Parallel and Distributed Programs

8. Past Research Work •  Multimedia for Education • Intelligent Tutoring Systems for Discrete Mathematics • ( a NCE TeleLearning Project) • with Dr. Jane Fritz and Prof. Uday Gujar • - Animated Computer Organization • Multi-Lingual Systems and Transliteration • Web Portal for an NB company • Clustifier and Extractor • Intelligent User Profile Generator •  Supervision/co-supervision • - 50 master's theses; - 4 doctoral theses • - 5 post-doctoral fellows/research associates

9. Current Research Work •  Bioinformatics • -Canadian Potato Genomics Project • - databases, multi-agent systems, pattern recognition •  Parallel/Distributed Processing • - C3-Grid development • Design and analysis of parallel/distributed • applications • Dr. Aubanel (Research Associate)

10. Current Research Work •  Multi-Agent Systems • - with Dr. Ghorbani and Dr. Marsh (NRC, • Ottawa) • - Intelligent agents • - Keyphrase-based Information sharing • between agents • - Scalability and Performance Evaluation • - Applications to e-commerce and • bioinformatics • - with Dr. Mironov • Specification and verification of multi-agent systems

11. Advanced Computational Research Laboratory (ACRL) • Dr. Virendra Bhavsar (Director) • Dr. Eric Aubanel (Research Associate) • Mr. Sean Seeley (Technical Support) • ACRL Management Committee • AC3 – Atlantic Canada High Performance • Computing Consortium • C3.ca Association Inc.

12. ARCL • Advanced Computational Research Laboratory •  High Performance Computational Problem-Solving Environment and Visualization Environment •  Computational Experiments in multiple disciplines: Computer Science, • Science and Engineering •  Located in the Information Technology Center (ITC)

14. ACRL Facilities •  High Performance Multiprocessor • (16-processor) System • - 24 GFLOPS (peak) performance • - 72 GB internal disk storage • - 109.2 GB external disk storage •  Software for Computational Studies and Visualization •  Parallel Programming Tools •  E-Commerce Software, including datamining software •  Memorandum of Understanding between IBM and UNB (in process)

15. ACORN (Agent-based Community Oriented Retrieval Network) ArchitectureSteve Marsh, Institute for Information Technology, NRC Virendra C. Bhavsar, Ali A. Ghorbani, UNB- Keyphrase-based Information Sharing between Agents Hui Yu – MCS Thesis (UNB) MATA’2000 Paper- Performance Evaluation using Multiple Autonomous Virtual Users HPCS’2000 paper

16. ACORNAgent-Based Community-Oriented {Retrieval | Routing} Network • ACORN is a multi-agent based system for information diffusion and (limited) search in networks • In ACORN, all pieces of information are represented by semi-autonomous agents...- searches; documents; images, etc. • Intended to allow human users to collaborate closely

17. Degrees of Separation • In the 1960’s, Stanley Milgram showed that everyone in the US was personally removed from everyone else by at most six degrees of separation • In communities, such as a research community, this is clear to all members: • if you want to know something, you ask someone. • If they don’t know, they may know someone else to ask... • and so on • This also works when you have something to tell people... • if you want someone relevant to know, you tell people you know will be interested... • and they forward the information to people they know will be interested.. • and so on

18. Relation to Other Work • Search Engines • Alta Vista, Excite, Yahoo, InfoSeek, Lycos, etc... • We don’t aim to search the Web • If the user has to search, it’s because the information diffusion is • not fast enough • not accurate enough • Recommender Systems • Firefly (Maes), Fab (Balabanovic) • Content-based or Collaborative • ACORN’s agents are a radical new approach, and a mixture of both... • ACORN is distributed • ACORN levers direct human-human contact knowledge • Matchmakers • Yenta (Foner) • Very close to the ACORN spirit, lacking in flexibility of ACORN

19. Relation to Other Work (cont.) • Web Page Watchers and Push Technologies • Tierra, Marimba, Channels • ACORN is a means of pushing new data, reducing the need to watch for changes • Filtering Systems • The filtering in ACORN is implicit in what is recommended by humans • ‘Knowbots’ • Softbots (Washington, Etzioni, Weld), Nobots (Stanford, Shoham) • mobile agents for internet search • ACORN provides diffusion also

20. ACORN • Uses communication between agents representing pieces of information, ACORN automates some of the processes • Anyone can create agents, and direct them to parties they know will be interested • An Agent carries user profile • Agents can share information

21. The ACORN Mobile Agent • represents a unit of information • structure Mobile Agent Name: (Unique ID, timestamp) Owner Address Dublin Core Metadata Visited Recommended Known Lists of users (humans) and/or cafés the agent has visited, is due to visit, or ‘knows of’

22. The Dublin Core • The Dublin Core is a Metadata element set, first developed at a workshop in Dublin, Ohio • Includes author, title, date • Also includes • Keywords; Publisher; type (e.g. home page, novel, poem) • format (of data) The Dublin Core presents a powerful structured medium for distributing human (and machine) readable metadata • It also presents an interesting query formulation tool • The DC home page can be found at:http://purl.org/metadata/dublin_core

23. Agent Lifecycle • A mobile agent in ACORN (one which represents information) undergoes several stages in its lifecycle • Creation • Distribution • Visiting a user • Mingling with other agents • Going to next site • Return

24. The Café - Agent Recommendations • User recommendations are not the only way an agent can expand its list of people to visit • Each site can have (between zero and many) cafés • A café is simply a meeting place for agents • Cafés can be generic or have specific topics (agents can be filtered before entering)

25. Café • At set intervals, agents present are compared, and relevant information exchanged • Keyphrase-based Information Sharing • Agents reside at cafés for set lengths of time (currently we have a default, but intend to make the length of time owner selectable) • The café represents a unique method of automating community based information sharing

26. S e r v e r Café S e r v e r S e r v e r S e r v e r Café Café tom@ucsd.edu ucsd.edu ymasrour@ai.it.nrc.ca ai.it.nrc.ca S e r v e r bob@ai.it.nrc.ca dick@ucsd.edu steve@ai.it.nrc.ca anwhere.else foo@anywhere.else cs.stir.ac.uk meto.gov.uk joan@meto.gov.uk Clients jane@meto.gov.uk wibble@cs.stir.ac.uk graham@cs.stir.ac.uk anne@cs.stir.ac.uk

27. Testing and Deployment • A working implementation of ACORN in Sun’s Java language • Stress testing the architecture using large numbers of real users - problems • Multiple artificial users on a simulated network

28. Multiple Autonomous Virtual Users • Test-bed: Several Autonomous Servers, each serving autonomous virtual users • Virtual User - capable of creating agents - picks up a topic from a client core’s interest - migrates to other servers - potential destinations

29. Adaptation of ACORN • ACORN: ~ >100 Java classes • Adaptation • Removal of user interaction classes • Removal of client behavior clases • Removal of other extraneous classes • Simulation of multiple client-server architecture: run more than one server on a single machine • Possibility of using multiple processor machines • Addition of a SiteController Class

30. Adaptation of ACORN (cont.) • SiteController Class • handles all communication between servers on a single machine • resolves agent migration requests • handles communication between different machines • Streamer Class • provides transport of agents across IP • Benefits • Removal of the need for continuous user interaction • Batch mode runs • Only ~30 Java classes

31. Experiments • Virtual Users • Porting of ACORN to many machine architectures SGI Onyx. PowerPC, and PC • O(n2) agent interactions in a Café, n - number of agents

32. Future Research Work Bioinformatics -Canadian Potato Genomics Project Biological databases, multi-agent systems, pattern recognition • Multi-Agent Systems - ACORN and B2B – B2C extensions

33. Multi-Agent SystemsB2B-B2C Extensions • ACORN and B2B – B2C extensions - User-driven personalisation • personalised and personalisable automatic delivery and search for information • directed advertisements based on user profiles and preferences • directed programming (both these examples based on interactive TV facilities such as those offered by iMagicTV and Microsoft interactive TV). • agent learning • data mining over large distributed networks and databases,

34. Multi-Agent SystemsB2B-B2C Extensions • ACORN and B2B – B2C extensions - the management of firms and user reputation (as in eBay's reputation manager, amongst others)  finally leading into proposed standards and legal bases necessary for eCommerce • Perceived and actual user privacy • Automated and manually-driven user profile generation and update

35. Multi-Agent SystemsB2B-B2C Extensions • Adaptation to Multi-processor machines at a single as well as multiple sites to exploit CA*NETIII • Usability Studies • XML objects instead of Java objects

36. Trust In Information Systems - eCommerce • Formalization of Trust: Steve Marsh (early 1990s) • Prototype version of an adaptable web site for eCommerce transactions • Trust in information systems: - creation and sustainability - user interface technologies • - user perceptions, behaviors, etc. and how to • influence and use such user behaviors. • - automatic user profile generation, its use in agent-based interfaces such as the trust reasoning adaptive web sites

37. Trust In Information Systems - eCommerce • Adaptive technologies in general for eCommerce, education, entertainment • Personality in the user interface and how it can affect user trust and perceived satisfaction

38. Multi-Agent Systems for Distributed Databases • Problem:Businesses are faced with continuous updating of their large and distributed databases connected on intranets and the Internet • Multi-Agent Systems - Very naturally satisfiy many requirements in such an environment - Provide a very flexible and open architecture - Scalability analysis with multiprocessor servers

39. Conclusion • Parallel and Distributed Intelligent Systems • Multi-Agent Systems and ACORN • Applications in e-Commerce • B2B and B2C Extensions • Trust in Information Systems • Multi-Agent Systems for Distributed Databases • NRC Collaborations in the above and other areas (Software Engineering, Intelligent Systems, etc.)