Parallel and Distributed Intelligent
Systems: Multi-Agent Systems and eCommerce
Virendrakumar C. Bhavsar
Professor and
Director, Advanced Computational Research
Laboratory
Faculty of Computer Science, University of New
Brunswick Fredericton, NB, Canada
bhavsar@unb.ca
Outline
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Past Research Work
Current Research Work
Multi-Agent Systems
ACORN and Extensions
Multi-Agent Systems and E-Commerce
Applications
Areas for Collaboration
Conclusion
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
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
Past Research Work
 Computer Graphics (with Prof. Gujar)
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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
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
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)
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
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.
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)
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)
ACORN (Agent-based Community Oriented
Retrieval Network) Architecture
Steve 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
ACORN
Agent-Based Community-Oriented {Retrieval |
Routing} Network
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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.
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Intended to allow human users to collaborate
closely
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:
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– if you want to know something, you ask someone.
– If they don’t know, they may know someone else to ask...
– and so on
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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
Relation to Other Work
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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
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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
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Matchmakers
Relation to Other Work (cont.)
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Web Page Watchers and Push Technologies
– Tierra, Marimba, Channels
– ACORN is a means of pushing new data, reducing the
need to watch for changes
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Filtering Systems
– The filtering in ACORN is implicit in what is recommended
by humans
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‘Knowbots’
– Softbots (Washington, Etzioni, Weld), Nobots (Stanford,
Shoham)
– mobile agents for internet search
– ACORN provides diffusion also
ACORN
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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
The ACORN Mobile Agent
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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’
The Dublin Core
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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
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The DC home page can be found at:
http://purl.org/metadata/dublin_core
Agent Lifecycle
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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
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The Café - Agent Recommendations
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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)
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)
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The café represents a unique method of
automating community based information
sharing
tom@ucsd.edu
ymasrour@ai.it.nrc.ca
ucsd.edu
ai.it.nrc.ca
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dick@ucsd.edu
steve@ai.it.nrc.ca
Café
anwhere.else
foo@anywhere.else
Café
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cs.stir.ac.uk
meto.gov.uk
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Café
Clients
wibble@cs.stir.ac.uk
graham@cs.stir.ac.uk
anne@cs.stir.ac.uk
joan@meto.gov.uk
jane@meto.gov.uk
Testing and Deployment
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A working implementation of ACORN in Sun’s
Java language
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Stress testing the architecture using large numbers
of real users - problems
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Multiple artificial users on a simulated network
Multiple Autonomous Virtual Users
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Test-bed: Several Autonomous Servers, each
serving autonomous virtual users
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Virtual User - capable of creating agents
- picks up a topic from a client
core’s interest
- migrates to other servers
- potential destinations
Adaptation of ACORN
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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
Adaptation of ACORN (cont.)
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SiteController Class
– handles all communication between servers on a single
machine
– resolves agent migration requests
– handles communication between different machines
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Streamer Class
– provides transport of agents across IP
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Benefits
– Removal of the need for continuous user interaction
– Batch mode runs
– Only ~30 Java classes
Experiments
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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
Future Research Work
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Bioinformatics
-Canadian Potato Genomics Project
Biological databases, multi-agent systems,
pattern recognition
 Multi-Agent Systems - ACORN and B2B – B2C
extensions
Multi-Agent Systems
B2B-B2C Extensions
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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,
Multi-Agent Systems
B2B-B2C Extensions
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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
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Perceived and actual user privacy
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Automated and manually-driven user profile
generation and update
Multi-Agent Systems
B2B-B2C Extensions
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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
Trust In Information Systems - eCommerce
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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 agentbased interfaces such as the trust reasoning adaptive
web sites
Trust In Information Systems - eCommerce
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Adaptive technologies in general for
eCommerce, education, entertainment
 Personality in the user interface and how it
can affect user trust and perceived
satisfaction
Multi-Agent Systems for Distributed
Databases
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Problem: Businesses are faced with continuous
updating of their large and distributed databases
connected on intranets and the Internet
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Multi-Agent Systems
- Very naturally satisfiy many requirements in such an
environment
- Provide a very flexible and open architecture
- Scalability analysis with multiprocessor servers
Conclusion
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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.)