Intelligent Agent Technology Research and Development pot

The conference Web support team at the Knowledge Information Systems Laboratory, Maebashi Institute of Technology did a terrific job of putting together and maintaining the home page for

World Scientific

Proceedings; trf the

2nd Asia-Pacific

Intelligent Agent

2nd Asia-Pacific rri 1 1

Conference on W A eCnnOIOgy

Research and Development

Proceedings erf the

2nd Asia-Pacific

Conference on IAT

Intelligent Agent

Technology

Research and Development

Published by

World Scientific Publishing Co Pte Ltd

P O Box 128, Farrer Road, Singapore 912805

USA office: Suite IB, 1060 Main Street, River Edge, NJ 07661

UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library

INTELLIGENT AGENT TECHNOLOGY

Research and Development

Copyright © 2001 by World Scientific Publishing Co Pte Ltd

All rights reserved This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher

For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA In this case permission to photocopy is not required from the publisher

ISBN 981-02-4706-0

PREFACE

Intelligent Agent Technology is concerned with the development of autonomous computational or physical entities capable of perceiving, reasoning, adapting, learning, cooperating, and delegating in a dynamic environment It is one of the most promising areas of research and development in information technology, computer science, and engineering today

This book is an attempt to capture the essence of the current state of the art in intelligent agent technology and to identify the new challenges and opportunities that it is or will be facing It contains the papers accepted for presentation at The Second Asia-Pacific Conference on Intelligent Agent Technology (IAT '01), held in Maebashi, Japan, October 23-26, 2001 The second meeting in the IAT conference series follows the success of IAT '99 held in Hong Kong in 1999 IAT '01 brought together researchers and practitioners to share their original research results and practical development experiences in intelligent agent technology The most important feature of this conference was that it emphasized a multi-facet, holistic view of this emerging technology, from its computational foundations, in terms of models, methodologies, and tools for developing a variety of embodiments of agent-based systems, to its practical impact on tackling real-world problems

Much work has gone into the preparation of the IAT '01 technical program: Original, high-quality papers were solicited for various aspects of theories, applications, and case studies related to agent technologies 134 full papers were submitted from 32 countries and regions of all continents Each submitted paper was reviewed by at least three experts on the basis of technical soundness, relevance, originality, significance, and clarity Based on the review reports, 25 regular papers (19%) and 40 short papers were accepted for presentation and publication

This book is structured into six chapters according to the main conference sessions:

Chapter 1 Formal Agent Theories

Chapter 2 Computational Architecture and Infrastructure

Chapter 3 Learning and Adaptation

Chapter 4 Knowledge Discovery and Data Mining Agents

Chapter 5 Distributed Intelligence

Chapter 6 Agent-Based Applications

In addition to the above chapters, this book also includes the abstract or papers for the IAT '01 keynote/invited talks by Benjamin W Wah, Toyoaki Nishida, Zbigniew

W Ras, Andrzej Skowron, and Katia Sycara, which provide different perspectives

to Intelligent Agent Technology

v

vi

We wish to express our gratitude to all members of the Conference Committee and the International Advisory Board for their instrumental and unfailing support IAT '01 has a very exciting program with a number of features, ranging from technical sessions, invited talks, agent demos, and social programs All of this work would not have been possible without the generous dedication of the Program Committee members and the external reviewers in reviewing the papers submitted

to IAT '01, of our invited speakers, Benjamin W Wah, Toyoaki Nishida, Zbigniew

W Ras, Andrzej Skowron, and Katia Sycara, in preparing and presenting their very stimulating talks, and of Jianchang Mao (Demos & Exhibits Chair) in soliciting demo proposals and setting up the program We thank them for their strong support The conference Web support team at the Knowledge Information Systems Laboratory, Maebashi Institute of Technology did a terrific job of putting together and maintaining the home page for the conference as well as building a software,

namely, cyber-chair, which is an intelligent agent and interface among organizers,

program committee members, and authors/attendees We would like to thank Juzhen Dong, Muneaki Ohsima, Norichika Hayazaki of the conference Web support team for their dedication and hard work

IAT '01 could not have taken place without the great team effort of the Local Organizing Committee and the support of Maebashi Institute of Technology and Maebashi Convention Bureau Our special thanks go to Nobuo Otani (Local Organizing Chair), Sean M Reedy, Masaaki Sakurai, Kanehisa Sekine, and Yoshitsugu Kakemoto (the Local Organizing Committee members) for their enormous efforts in planning and arranging the logistics of the conference from registration/payment handling, venue preparation, accommodation booking, to banquet/social program organization We are very grateful to the IAT '01 sponsors: ACM SIGART, Maebashi Institute of Technology, Maebashi Convention Bureau, Maebashi City Government, Gunma Prefecture Government, The Japan Research Institute, Limited, United States Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development, and United States Army Research Office in Far East, and Web Intelligence Laboratory, Inc for their generous support

We thank ACM SIGWEB, SIGCHI, Japanese Society for Artificial Intelligence, JSAI SIGFAI, SIGKBS, and IEICE SIGKBSE for being in cooperation with IAT '01 Last but not the least, we thank Ms Lakshmi Narayanan of World Scientific for her help in coordinating the publication of this book

October 2001

Ning Zhong and Jiming Liu

Program Committee Chairs

Setsuo Ohsuga and Jeffrey Bradshaw

General Conference Chairs

CONFERENCE ORGANIZATION

General Chairs:

Program Chairs:

Demos and Exhibits Chair:

Local Organizing Chair:

Jeffrey M Bradshaw (Inst H&M Cognition, USA)

Michele L D Gaudreault (US AOARD)

Daniel T Ling (Microsoft Corp., USA)

Jiming Liu (Hong Kong Baptist U.)

Jianchang Mao (Verity Inc., USA)

Hiroshi Motoda (Osaka U., Japan)

Masahiko Satori (Maebashi Inst Tech., Japan)

Tadaomi Miyazaki (Maebashi Inst Tech., Japan)

Nobuo Otani (Mabashi Inst Technology, Japan)

Sean M Reedy (Mabashi Inst Technology, Japan)

Ning Zhong (Maebashi Inst Technology, Japan)

Setsuo Ohsuga (Waseda U., Japan) Jeffrey Bradshaw (Inst H&M Cognition, USA) Ning Zhong (Maebashi Inst Technology, Japan) Jiming Liu (Hong Kong Baptist U.)

Jianchang Mao (Verity Inc., USA) Nobuo Otani (Mabashi Inst Technology, Japan)

Setsuo Ohsuga (Waseda U., Japan) Patrick S P Wang (Northeastern U., USA) Yiyu Yao (U Regina, Cadada)

Jie Yang (U Science & Technology of China) Ning Zhong (Maebashi Inst Technology, Japan) Jan Zytkow (U North Carolina, USA)

Toshio Kawamura (Maebashi Convention B.) Masaaki Sakurai (Maebashi Convention Bureau) Kanehisa Sekine (Maebashi Convention Bureau) Midori Asaka (IPA, Japan)

Yoshitsugu Kakemoto (JRI, Limited, Japan)

International Advisory Board

Local Organizing Committee

Program Committee

K Suzanne Barber (U Texas-Austin, USA)

Guy Boy (EURISCO, France)

Cristiano Castelfranchi (CNR, Italy)

Kerstin Dautenhahn (U Hertfordshire, UK)

Edmund H Durfee (U Michigan, USA)

E A Edmonds (Loughborough U., UK)

Tim Finin (UMBC, USA)

Adam Maria Gadomski (ENEA, Italy)

Scott Goodwin (U Regina, Canada)

Vladimir Gorodetsky (Russian Academy of Sci.)

Mark Greaves (The Boeing Company, USA)

Barbara Hayes-Roth (Stanford U., USA)

Michael Huhns (U South Carolina, USA)

Keniti Ida (Maebashi Inst Technology, Japan)

Tom Ishida (Kyoka oto U., Japan)

Lakhmi Jain (U South Australia)

Stefan J Johansson (U Karlskrona, Sweden)

Qun Jin (U Aizu, Jaoan)

Juntae Kim (Dongguk U., Korea)

David Kinny (U Melbourne, Australia)

Matthias Klusch (German Research Center for AI)

Sarit Kraus (U Maryland, USA)

Danny B Lange (General Magic, INC., USA)

Jimmy Ho Man Lee (Chinese U Hong Kong)

Jiming Liu (Hong Kong Baptist U.)

Mike Luck (U Southampton, UK)

Helen Meng (Chinese U Hong Kong)

Joerg Mueller (Siemens, Germany)

Hideyuki Nakashima (ETL, Japan) Wee-Keong Ng (Nanyang Tech U., Singapore) Katsumi Nitta (Tokyo Inst Technology, Japan) Yoshikuni Onozato (Gunma U., Japan) Tuncer Oren (Marmara Research Center, Turkey) Ichiro Osawa (ETL, Japan)

Sun Park (Rutgers U., USA) Van Parunak (ERIM, USA) Zbigniew W Ras (U North Carolina, USA) Eugene Santos (U Connecticut, USA) Zhongzhi Shi (Chinese Academy of Sciences) Carles Sierra (Scientific Research Council, Spain) Kwang M Sim (Chinese U Hong Kong) Andrzej Skowron (Warsaw U., Poland) Ron Sun (U Misouri-Columbia, USA) Niranjan Suri (U West Florida, USA) Takao Terano (U Tsukuba, Japan) Demetri Terzopoulos (U Toronto, Canada) Huaglory Tianfield (Glasgow Caledonian U., UK) David Wolpert (NASA Ames Research Center) Jinglong Wu (Kagawa U., Japan)

Takahira Yamaguchi (Shizuoka U., Japan) Kazumasa Yokota (Okayama Prefectural U., Japan) Eric Yu (U Toronto, Canada)

P C Yuen (Hong Kong Baptist U.) Chengqi Zhang (Deakin U., Australia) Ning Zhong (Maebashi Inst Technology, Japan)

TABLE OF CONTENTS

Preface v Conference Organization vii

Chapter 1 Formal Agent Theories

SPY: A Multi-Agent Model Yielding Semantic Properties 44

F Buccafurri, D Rosaci, G M L Same, L Palopoli

ABT with Asynchronous Reordering 54

Marius-Calin Silaghi, Djamila Sam-Haroud, Boi Faltlngs

Social Rationality and Cooperation 64

Guido Boella

Belief Revision in Type Theory 69

Tijn Borghuis, Fairouz Kamareddine, Rob Nederpelt

Heterogeneous BDI Agents II: Circumspect Agents 74

Maria Fash

A Preference-Driven Approach to Designing Agent Systems 80

Stefan J Johansson, Johan Kummeneje

Agent Consumer Reports: of the Agents, by the Agents,

and for the Agents 86

Xiaocheng Luan, Yun Peng, Timothy Finin

Logical Formalizations Built on Game-Theoretic Argument

about Commitments 92

Lamber Royakkers, Vincent Buskens

Asynchronous Consistency Maintenance 98

Marius-Calin Silaghi, Djamila Sam-Haroud, Boi Faltings

Chapter 2 Computational Architecture and Infrastructure

Reasoning about Mutual-Belief among Multiple Cooperative Agents 104

Wenpin Jiao

Portable Resource Control for Mobile Multi-Agent Systems in JAVA 114

Walter Binder, Jarle G Hulaas, Alex Villazon, Rory G Vidal

An Agent-Based Mobile E-Commerce Service Platform for

Forestry and Agriculture 119

Matthias Klusch, Andreas Gerber

An Itinerary Scripting Language for Mobile Agents in Enterprise

Applications 124

Seng Wai Loke, Arkady Zaslavsky, Brian Yap, Joseph Fonseka

Intelligent Agents for Mobile Commerce Services 129

Proactiveness and Effective Observer Mechanisms in Intelligent Agents 144

Jon Plumley, Kuo-Ming Chao, Rachid Anane, Nick Godwin

Chapter 3 Learning and Adaptation

Parrondo Strategies for Artificial Traders 150

Magnus Boman, Stefan J Johansson, David Lyback

BDI Multi-Agent Learning Based on First-Order Induction of

Logical Decision Trees 160

Alejandro Guerra Hernandez, Amal El-Fallah Seghrouchni,

Henry Soldano

Evolutionary Behaviors of Competitive Agents in Dilemma Situation 170

Tin Tin Naing, Lifeng He, Atsuko Mutoh, Tsuyoshi Nakamura,

Hidenori Itoh

A Strategy for Creating Initial Data on Active Learning of Multi-Layer

Perceptron 180

Kazunori Iwata, Naohiro Ishii

Equilibrium Selection in a Sequential Multi-Issue Bargaining Model

with Evolutionary Agents 190

Norberto Eiji Nawa, Katsunori Shimohara, Osamu Katai

Affect and Agent Control: Experiments with Simple Affective States 200

Matthias Scheutz, Aaron Sloman

Meta-Learning Processes in Multi-Agent Systems 210

Ron Sun

Scalability and the Evolution of Normative Behavior 220

Jorg Wellner, Sigmar Papendick, Werner Dilger

Thinking-Learning by Argument 230

Aladdin Ayesh

Evolution of a Foraging Model with Many Individuals by Kin-selection 235

Kazue Kinoshita, Atsuko Mutoh, Tsuyoshi Nakamura,

Hidenori Itoh

The Use of Emergent Behaviour in a Multi-Agent System to Drive

Self-Adaptation at the Interface 240

Peter Marshall, Sue Greenwood

A Biologically Inspired Four Legged Robot That Exhibits Some Natural

Walking Behaviours 245

5 Peng, G R Cole, C P Lam

Chapter 4 Knowledge Discovery and Data Mining Agents

CM-RELVIEW: A Tool for Causal Reasoning in Multi-Agent

Environments 252

Brahim Chaib-Draa

User's Ontology-Based Autonomous Interface Agents 264

Tarek Helmy, Satoshi Amamiya, Makoto Amamiya

Integration and Reuse of Heterogeneous XML DTDs for

Information Agents 274

Euna Jeong, Chun-Nan Hsu

Virtual Museum's Assistant 284

Osvaldo Cairo, Ana Aldeco, M.E Algorri

Index Based Document Classification with CC4 Neural Networks 289

Enhong Chen, Zhengya Zhang, Xufa Wang, Jie Yang

Price Watcher Agent for E-Commerce 294

Simon Fong, Aixin Sun, Kin Keong Wong

Automated Information Extraction from Web Pages Using

an Interactive Learning Agent 300

Jugal K Kalita, Paritosh Rohilla

An Intelligent Agent with Structured Pattern Matching for

a Virtual Representative 305

Seung-ik Lee, Sung-Bae Cho

A Calendar Management Agent with Fuzzy Logic 310

Wayne Wobcke

XML Based Multi-Agent Collaboration for Active Digital Libraries 315

Yanyan Yang, Omer F Rana, David W Walker,

Roy Williams, Giovanni Aloisio

Chapter 5 Distributed Intelligence

An Intelligent Channel Allocation Scheme for Mobile Networks:

An Application of Agent Technology 322

Eliane L Bodanese, Laurie G Cuthbert

An Atomic Approach to Agent-Based Imagery and Geospatial

Problem Solving 334

James J Nolan, Robert Simon, Arun K, Sood

Model-Based Creation of Agents and Distribution of Problem Solving 344

Katsuaki Tanaka, Setsuo Ohsuga

A Distributed Algorithm for Coalition Formation Among

E-Commerce Agents 355

Guillaume Vauvert, Amal El Fallah-Seghrouchni

Optimal Reward Functions in Distributed Reinforcement Learning 365

David H Wolpert, Kagan Turner

Polygonal Approximation of Planar Digital Curves Using Ant System 375

Peng-Yeng Yin

A Biological View on Information Ecosystems 385

Bengt Carlsson, Paul Davidsson

The CoDAC Collaboration Framework 390

K W Ng, T O Lee

A Multi-Agent Approach to Modelling Interaction in Human

Mathematical Reasoning 395

Alison Pease, Simon Colton, Alan Smaill, John Lee

Secure Asynchronous Search 400

Marius-Calin Silaghi, Djamila Sam-Haroud, Boi Faltings

Foundations of Market-Driven Agents: An Adaptation of Zeuthen's

Bargaining Model 405

Kwang Mong Sim, Chung Yu Choi

Chapter 6 Agent Based Applications

Kavanah: An Active User Interface Information Retrieval Application 412

Eugene Santos JR., Hien Nguyen, Scott M Brown

iJADE WeatherMAN - A Multi-Agent Fuzzy-Neuro Network Based

Weather Prediction System 424

Raymond Lee, James Liu, Jane You

Acquaintance Models in Coalition Planning for Humanitarian

Relief Operation 434

Michal Pechoucek, Vladimir Marik, Jaroslav Barta

Agent Negotiation in a Virtual Marketplace 444

Walid S Saba, Pratap R Sathi

Modeling User Preferences to Facilitate More Automated and Accurate

Transaction Brokering within Heterogeneous Multi-Agent Electronic

Markets 454

G Tewari, P Maes, A Berkovich, V Gabovich

Attitude Based Agents in E-Commerce Applications 464

S Au, N Parameswaran

Organizing Internet Agents According to a Hierarchy of

Information Domains 469

Sylvie Cazalens, Philippe Lamarre

Introducing User Preference Modeling for Meeting Scheduling 474

Hon Wai Chun, Rebecca Y M Wong

Executive Attentional Control in Autonomous Robotic Agents 479

Jason Garforth, Anthony Meehan, Sue Mchale

Implementation and Analysis of Mobile Agents in a Simulation

Environment for Fieldbus Systems 484

R Hunstock, U Ruckert, T Hanna

Evaluating Believability in an Interactive Narrative 490

Jarmo Laaksolahti, Per Persson, Carolina Palo

iJADE Stock Predictor - An Intelligent Multi-Agent Based Time Series

Stock Prediction System 495

Raymond S T Lee, James N K Liu

Approximate Sensor Fusion in a Navigation Agent 500

J F Peters, S Ramanna, M Borkowski, A Skowron

Simulating Day-Ahead Trading in Electricity Markets with Agents 505

Max Scheldt, Hans-Jurgen Sebastian

Using Mobile Agents to Update and Maintain Course Materials on

Students' Computers in Internet-Based Distance Education 510

Hongxue Wang, Pete Holt

INVITED TALKS

I N T E L L I G E N T A G E N T S F O R M A R K E T - T R E N D

P R E D I C T I O N

BENJAMIN W WAH

Department of Electrical and Computer Engineering

and the Coordinated Science Laboratory University of Illinois at Urbana- Champaign

Urbana, IL 61801, USA

http://manip.crhc.uiuc.edu

(2001 IEEE Computer Society President)

In this presentation we discuss the role of intelligent agents in trend predictions Market-trend d a t a , such as stock-market d a t a , are charac- terized by non-stationary time series t h a t may depend on non-numeric and non-quantifiable measures T h e prediction of market trends, therefore, should consist of prediction of non-stationary time series and the abstraction and in- tegration of non-numeric information in prediction In this talk, we survey various prediction techniques for and mining of m a r k e t - t r e n d d a t a We pro- pose t o use intelligent agents in t h e abstraction of non-numeric information, the decomposition of non-stationary time series into multiple stationary time series, and the prediction of trends using artificial neural networks Finally,

market-we illustrate our techniques in predicting stock-market d a t a

2

SOCIAL INTELLIGENCE DESIGN FOR KNOWLEDGE CREATING COMMUNITIES

TOYOAKI NISHIDA

Department of Information and Communication Engineering

Graduate School of Information Science and Technology

The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

nishida@kc t u-tokyo ac.jp

Communities play an important role in knowledge creation by providing people with opportunities to continually learn from others, find partners to collaborate with, and demonstrate the significance of their disciplines In education or business, it is relatively easy

to find typical examples of knowledge creating communities for sharing and exchanging specialized knowledge among knowledge workers In other domains such as NPO or local communities, people are naturally practicing mutual learning and invaluable knowledge is built as a result, even if knowledge creation is not deemed a primary goal of the community

In this paper, 1 present an interdisciplinary approach to augmenting the community knowledge creating process by integrating insights from social psychology, cognitive psychology, and advanced information technology I emphasize the role of conversations and stories as a means of establishing a common background in a community

I describe several systems that primarily use the conversational modality to mediate community communication Among others, EgoChat allows the user to make conversation with virtualized egos responding on behalf of other users It allows the user to take an initiative by interrupting the conversation and changing its flow VoiceCafe allows artifacts

to make conversation with people or other artifacts It stimulates creative thinking by bringing about utterances from the physical object's point of view, which might be strikingly different from humans' view

These engineering approaches should be tightly coupled with sociological and cognitive approaches, to predict and assess the effects of community communication mediation systems on the human society 1 discuss issues on designing a constructive framework of interaction for achieving practical goals without being caught by known pathological pitfalls

3

We will not be able to innovate a totally new kingdom of artificial agents apart from the human society, but we have to carefully embed the agent system in the existing human society This means that we need to understand more about humans and the human society to better design an embedded system We need to pay much attention

on the effects the technology brings about the human society We need to make every effort to have the proposal accepted by the human community In contrast, we need not insist on the novelty of the technology or a pedagogical issue of whether the artifact can be called an agent

Let us call this field social intelligence design in general Research on social

intelligence design involves such issues as how new technologies induce the emergence of a new language and lifestyle For example, interactive multimedia websites are a new medium and maybe even a new language, with interesting new conventions, and increasing adaptation to the support of communities Japanese teenagers have developed a new language for use originally with beepers and now with mobile phones These are both new mainstream real world developments that should be studied further, and could probably give some valuable insights The theme of "social intelligence" is really an angle on the support of groups in pursuit

of their goals, whether that is medical knowledge, stock trading, or teenage gossip

I focus on community support systems to shed light on key aspects of social intelligence design The goal of a community support system is to facilitate formation and maintenance of human and knowledge networks to support activities

in a community Examples of community support systems include socially intelligent agents that mediate people in getting to know and communicate with each other, a collaborative virtual environment for large-scale discussions, personalized agents for helping cross-cultural communication, interactive community media for augmenting community awareness and memory, to name just a few

I emphasize the role of stories and conversations as a means of establishing a common background in a community Stories allow us to put pieces of information into an intelligible structure Conversations give us an opportunity to examine information from various angles and search for a good story structure In some community support systems, story-telling agents play a central role It should be noted that their significance depends more on the contents of stories rather than conversation mechanism

I also emphasize the empirical aspects of social intelligence design Engineering approaches should be tightly coupled with sociological and cognitive approaches, to predict and assess the effects of community communication

5

mediation systems on the human society I show how psychological approaches are applied to design and evaluation of community support systems

2 Communities and Social Intelligence

Social intelligence design is distinguished from most of other conventional engineering disciplines in that we have to be strongly aware of the human society as

a target For this reason, I first take a look at the nature of my target, i.e., communities, in this section

A community is a group of people loosely coupled by a shared interest or environment More formal discussion can be found in literature in sociology For example, Smith defines a community as follows:

Generically, a community can be understood as a set of on-going social relations bound together by a common interest or shared circumstance As a result, communities may be intentional or unintentional, a community's participants may purposely join together or be thrust into membership by circumstance Intentional communities are of particular interest because they raise more questions about the reasons and causes for their emergence than do unintentional ones [21]

Traditional communities were local communities that are characterized by locality and shared living environment The advent of a global information network has not only considerably relaxed spatial constraints for communities to be built, but also provided a new opportunities for existing communities Typical networked communities include:

• communities of interest, in which people are tied with a shared interest;

• communities of practice, in which a group of people work together and share

a common work practice; and

• enhanced local communities or smart communities, which result from

enhancing communication and information sharing facilities in existing local communities

Schlichter contrasts communities with groups and teams [23] He characterizes communities as sets of people who share something but who do not necessary know each other or interact on personal basis In contrast, groups are sets of people who know each other but who do not necessarily cooperate, while teams are sets of people who are cooperating to achieve a common goal In educational environments, the class of lecture may be regarded as a community, a discussion group a group, and a learning group a team

Recently, communities have become increasingly paid more attention in the context of knowledge management and distance learning A community provides its members with opportunities to continually learn from others, find partners to collaborate with, and demonstrate the significance of their disciplines In education

6

or business, it is relatively easy to find examples of communities oriented towards knowledge creation by sharing and exchanging specialized knowledge among knowledge workers In other domains such as NPO or local communities, people are naturally practicing mutual learning and invaluable knowledge is built as a result, even if knowledge creation is not deemed a primary goal of the community

We consider that community knowledge creation is essentially a co-evolution of human and knowledge networks [16, 17] By human network, I mean a collection of people connected by various social relations, such as acquaintance or partnership A human network is considered to embody tacit knowledge that may be shared in a community but may not be explicitly spoken In contrast, knowledge network is a collection of documents or files connected explicitly by hyperlinks or implicitly by references Knowledge network explicitly describes shared knowledge and interest

in a community

A knowledge network enables people with a common interest to know each other, resulting in extension of human network A human network, in turn, helps new ideas grow through intimate discussions It facilitates the extension of knowledge network through publication of new knowledge Thus, a synergetic cycle

of human and knowledge network will lead to a successful community

A more elaborate characterization of human and knowledge networks is proposed by Contractor [3] He pointed out that observed knowledge networks are different from cognitive networks that each individual possesses as a cognitive perception of the network He proposes to distinguish between: (i) knowledge networks that represent the extent to which the same or disparate knowledge is distributed among various members of the group, and (ii) cognitive knowledge networks that represent individuals' cognitive perceptions of "who knows what" within the group

In order to understand the dynamics of community knowledge, Contractor proposes to observe five types of network data: (i) a communication network of actors based on existing tasks and project links between them, (ii) a knowledge network based on actors providing an inventory of their skills and expertise, (iii) a knowledge network of actors based on links between their web sites, (iv) a knowledge network of actors based on common links from their web sites, and (v) a knowledge network based on similarity in content between different actors' web sites

It should be noted that all kinds of interaction in a community may not bring about fruitful results In social psychology, various pathological pitfalls are known about group interactions A notorious example is flaming, an endless slander battle

on the net, which is rare in face-to-face communication Flaming blocks discussions among community members, possibly resulting in a destructive damage to a community False consensus is another undesirable phenomenon It results from "a spiral of silence", or "bandwagon effect", for instance, in which false cognition is socially amplified

7

3 Community Support Systems

The role of community support systems is to support community activities by providing a communication channel for community members Community support

systems are built on top of the communication and expected to help community

members (i) exchange awareness with other members, (ii) explore human and knowledge networks, (iii) build community knowledge, (iv) organize public events, (vi) form a group/team for collaborative work, (v) negotiate with others, and (vii) discuss public issues and make decisions about the community Community support systems provide rather long-range, bottom-up communicative functions in the background of daily life This feature is contrastive with groupware that emphasizes more task-driven, short-range collaboration, although awareness is equally emphasized In the rest of this section, I will discuss the first three functions

3.1 Helping to Awareness with Other Members

Most of networked communities are based on intentional participation, based on a common interest for instance Compared with mission-oriented groups where participants are incorporated in a certain work structure, the degree of necessity to exchange awareness is relatively low in networked communities Participants tend

to become silent unless a mechanism is provided for lowering the cost for exchanging awareness with other members

In order to support awareness, Schlichter uses spatial metaphors such as rooms

or hallways in "The Lecture 2000", a computational environment for supporting a learning community FaintPop supports a light-weight, acknowledge-only mode of communications [19] The major design goal of FaintPop is to communicate the sense of connectedness, not to perform informative functions FaintPop is a communication device similar to a photo frame Small photos or icons of the user's colleagues are displayed in the frame, through which the user can communicate with other users using a simple touch actions Three types of touching are permitted: a tap to communicate a neutral feeling, a pet a positive feeling, and a hit a negative feeling The user can communicate her/his feeling towards her/his colleagues by using these three types of touching and other community members can observe it Sumi proposes to use interest-based information distribution system, which pushes information to interested users, rather than passively waits for requests from users [24]

Voice Cafe [8] allows artifacts to make conversation with people or other artifacts (Figure 1) It stimulates creative thinking by bringing about utterances from the physical object's point of view, which might be strikingly different from humans' view Each Voice Cafe artifact consists of a physical object and a conversational agent It can communicate with community members by exchanging gossips, or small talks about members' conditions, schedules, thoughts and opinions, and so on

8

(a) the conceptual framework of Voice Cafe

Figure 1 Virtualized egos as an interactive community medium

By listening to the gossips, members can gain awareness of other people at the small talk level

3.2 Helping to Explore Human and Knowledge Networks

This facility helps the user find human and human resources in a community Social matchmaking is frequently used to locate people on the Internet who share some similar interests and enable the automatic formation of interest group

Social matchmaking calculates the distance between users by referring to their user profiles A major motivation behind social matchmaking is to address situations such that finding an expert is difficult and time consuming; people are often working on similar projects without realizing it; or people feel socially isolated

9 Real World Inhabitant

Figure 2 Virtualized egos as an interactive community medium

because nobody around s/he seems to share the same Interest Yenta [4] is a agent matchmaking system that can automatically determine user interests and operate in a completely decentralized, peer-to-peer fashion Yenta is a persistent agent that uses referrals to find each other, build clusters of like-minded agents, and introduce users to each other Special care is paid to protect user privacy Silhouettell [20] combines awareness support and social matchmaking to bridge between informal and formal meetings It projects the location of participants on the screen as shadows, and facilitates conversation by presenting Web pages that are inferred to common to the participants

multi-Referral Web [11] integrates recommendations and search through the concept

of a social network It helps the user discover her/his relationship to the best human experts for a given topic It gathers all information from public sources, which removes the cost of information posting and registration It can also explain the user why each link in the referral-chain appeared

In order to provide an integrated method of exploring and building human and knowledge networks, we use a talking-virtualized-egos metaphor in CoMeMo-Community [14] and EgoChat [12] to enable an elaborate asynchronous communication among community members A virtualized ego mainly plays two functions (Figure 2) First, it stores and maintains the user's personal memory Second, it presents the content of the personal memory on behalf of the user at appropriate situations By personal memory, we mean an aggregation of relevant information represented in the context specific to a particular person Personal memory plays a crucial role not only in personal information management but also

in mutual understanding in a community

A virtualized ego serves as a portal to the memory and knowledge of a person

It accumulates information about a person and allows her/his colleague to access the information by following an ordinary spoken-language conversation mode, not by

10

going up and down a complex directory in search for possibly existent information,

or by deliberately issuing commands for information retrieval In addition, virtualized ego embodies tacit and non-verbal knowledge about the person so that more subtle messages such as attitude can be communicated

As is also the case with Voice Cafe, we take a conversation-centered approach

in designing intelligent systems and capturing intelligence itself Conversation plays varieties of roles in human societies It not only allows people to exchange information, but it also helps them create new ideas or manage human relations In our approach, more emphasis is placed on creating, exchanging, reorganizing, and utilizing conversational contents in knowledge creation, rather than implementing intelligent agents or yet-another human interface

3.3 Helping to Build Community Knowledge

The third function of a community support system is for helping community members build a shared knowledge Nonaka and Takeuchi pointed out that the community knowledge is built by a spiral of interactions between explicit and tacit knowledge [18] They suggest that the process of knowledge creation is more important than the body of knowledge, for people often find more value in communities that evolve as a result of learning This implies that more emphasis should be placed on supporting interactions or the emergent aspect of community knowledge [13] in community support systems

The Public Opinion Channel (POC) [15, 16, 7] is a community-wide interactive broadcasting system (Figure 3) A POC continuously collects messages from people

in a community and feeds edited messages back to them POC is not intended to be

a system that broadcasts public opinions per se Instead, it is intended to broadcast miscellaneous information that can serve as a basis of public opinion formation

A POC repeats a cycle consisting of call-for-opinion followed by one or more repetition of responding by the community members and summarization by the POC system In the initial call-for-opinion message, the POC system specifies a focus of discussion Alternatively, people may also initiate discussion by submitting a topic Then, interested community members may respond with messages In principle, messages are not limited to pure opinions Instead, they may include questions, stories, findings, jokes, proposals, and all other message types The POC system may combine these messages, generate a story, and broadcast it to the community The POC system may issue a progress report based on responses from community members The process proceeds with altering subjects

A POC brings about ecology of ever evolving stories People can access to the story pool at anytime by an on-demand-type access means Another thing I would like to emphasize here is that the POC broadcasting can be embedded in the ambient environment, just like a radio broadcasting, so that people need not pay much attention at all times

11

Community Broadcasting

Servers (POC Servers)

POC Clients We have to

discuss of the ethics of cyborg ^j

Advertising/or Opinions

' Community B

Broadcasting Opinions

cyborg nil be

Community C

Figure 3 The Conceptual framework of Public Opinion Channel (POC) The POC is an interactive

broadcasting system that continuously collects messages from community members and feeds edited message streams back to the community

Compared with existing mass media, a POC has various advantages Computational support and network connectivity enable a large amount of responses

to be analyzed on the fly, allowing real-time interactive stories to be generated In particular, a combination of statistical computation and semantic processing permits minority opinions to be reflected in the structure of public opinion

We believe that POC also contributes to community knowledge building and public discussion

4 Social Intelligence Design

Social Intelligence Design is a new discipline aimed at understanding and

supporting social intelligence, i.e., intelligence collectively exhibited by

(natural/artificial) agents to dynamically organize members' activities into a coherent one by exploiting or innovating the social structure Social intelligence models intelligence as a phenomenon emerging from the way agents, either natural or artificial, are interacting with each other Research into community support systems

is concerned with engineering aspects of Social Intelligence Design Meanwhile, investigation into the sociological and cognitive aspects are equally or sometimes more important Engineering approach should be tightly coupled with sociology and psychology and other disciplines closely related to the study of humans and human society Thus, Social Intelligence Design involves not only designing artifacts but

of new knowledge and relationship among participants An interdisciplinary study integrating insights from Artificial Intelligence, Human-Computer Interaction, Social and Cognitive Sciences, Media Studies, and other related disciplines is necessary to predict and assess the effects of social intelligence augmentation systems on the human society from sociological and cognitive viewpoints Promising application domains includes collaborative environment, e-learning, knowledge management, community support systems, symbiosis of humans and artifacts, crisis management, and digital democracy

The engineering side of Social Intelligence Design involves not only community support systems but also systems that range from group/team oriented collaboration support systems [5] to large-scale online-discussion support systems such as Bubble used in the IBM's WorldJam trial [26]

The humanity side of Social Intelligence Design involves design and assessment

of social intelligence In the rest of this section, I will overview a couple of research

in this side

4.1 Social Intelligence Design from Social Psychological View

Azechi points out that two factors hinder dynamic knowledge interaction in a networked community One is the essential disposition of a group that prevents

effective cooperation, particularly known as groupthink and the hostility to

out-groups Groupthink [9] means a phenomenon that collective creativity does not

exceed individual creativity The hostility to out-groups means that a group member has hostility to out-groups easily [23, 25] This phenomenon is closely related with stereotyping, which means some stigmata produce the wrong inference about an outsider's behavior pattern and personality

Another factor is a new concept called escape from information, which means

the tendency of the people living in a mass-consumption society to make themselves the same as others and avoid choosing and expressing information themselves Azechi classifies the content of a message into dry and wet information [1] Dry information primarily contains logical linguistic information and constitutes the core of a message It may be an assertion, a question, a view, an opinion, or any other statements that are logically constituted by fact In contrast, wet information is

These arguments are inspired by discussion at JSAI-Synsophy International Workshop on Social Intelligence Design, Matsue, Japan, May 21-22, 2001

by trash information, setting up a clear purpose for using the tool will encourage the user, and informing the user of the size of the user group will increase the motivation [2]

Matsumura addresses the consensus formation in networked communities and points out the importance of minority opinions in group decision making [10] Based on social psychological experiments on the minority opinions, he has found out that (i) minority members tend to overestimate the number of other members who share the same attitude, (ii) minority members tend to underestimate the attitude

of other members, (iii) minority members who underestimate the proportion of the minority's opinion tend to lose an intention to act Such inaccuracy in cognition of opinion distribution is called false consensus effect These observations should be taken into account in designing discussion support systems so that useful discussions can be expected by reflecting minority opinions He discusses the pros and cons of using anonymous messages, which will obscure the real distribution of opinions Good news is that it will not discourage minority members by the fact that they are

in the minority Bad news is that it may cause an incorrect cognition about the distribution of opinions

4.2 Evaluations of Social Intelligence

Social Intelligence Design is certainly an empirical study We have to repeat the design-implement-evaluation cycle until we reach better systems

Network Analysis is a powerful means of evaluating or comparing empirical data It provides us with a means for calculating various aspects of a given network

in terms of centrality, density or cohesion By comparing those features from one network against those from another, we can describe the similarity and difference in quantitative term Fujihara has applied Network Analysis to a log collected from experiments with a POC prototype for several months to see if POC actually facilitates community knowledge creation [6]

Fujihara points out the importance of setting up appropriate control condition for formalizing the result of experiments It will allow the effects of tools to be

14

measured and compared against a baseline condition He also suggests that multiple methods should be combined to gain a reliable result Methods of evaluation may fall into the following three types:

• Analysis of users' subjective estimations and introspection that can be

collected through questionnaire and interview,

• Experimental methods where experimental conditions are operated

or innovating the social structure The central issue here is designing and understanding a world where people and agents cohabit, rather than inventing a system of artifacts I have overviewed community support systems as example of the engineering aspects of Social Intelligence Design I have also shown some psychological approaches related to the design and evaluation stages of Social Intelligence Design The agent technology has a large potential of augmenting social intelligence, provided that special care is taken in order to embed artifacts into the human society

References

1 Azechi, S., Social psychological approach to knowledge-creating community,

in: Nishida, T (ed.), Dynamic Knowledge Interaction, pp 15-57, CRC Press

LLC, 2000

2 Azechi, S., Motivation for showing opinion on public opinion channel: a case study, to be presented at KES-2001, Osaka, Sept 6, 7 & 8 September 2001, Japan, 2001

3 Contractor, N., Bishop, A., and Zink, D., PrairieKNOW: a tool to assist the study, creation, and growth of community networks, in: Bullinger, H.-J and

Ziegler, J (eds.), Human-Computer Interaction, Volume 2, Erlbaum, Hillsdale,

pp 447-451, 1999

4 Foner, L N., Political Artifacts and Personal Privacy: The Yenta Multi-Agent

Distributed Matchmaking System Ph.D Dissertation, MIT, 1999

15

5 Fruchter, R., Bricks, bits & interaction, presented at JSAI-Synsophy International Workshop on Social Intelligence Design, Matsue, Japan, May 21-22,2001

6 Fujihara, N., How to evaluate application of conversational intelligence, to be presented at KES-2001, Osaka, Sept 6, 7 & 8 September 2001, Japan, 2001

7 Fukuhara, T., Nishida, T., and Uemura, S., Public Opinion Channel: a system for augmenting social intelligence of a community, presented at JSAI-Synsophy International Workshop on Social Intelligence Design, Matsue, Japan, May 21 -22,2001

8 Fukuhata, T., Nishida, T., and Uemura, S., Voice Cafe: conversational support system in a group, KES 2001, to be presented at KES-2001, Osaka, Sept 6, 7 &

11 Kautz, H., Selman B., and Shah, M., Referral Web: combining social networks

and collaborative filtering, Communications of the ACM, 40 (3) pp 63-65,

1997

12 Kubota, H., Nishida, T., and Koda, T., Exchanging tacit community knowledge

by talking-virtualized-egos, in: Proceedings of Agent 2000, pp.285-292, 2000

13 Nakata, K., Knowledge as Social Medium, New Generation Computing, Vol

17, No 4, pp 395-405, 1999

14 Nishida, T., Facilitating community knowledge evolution by talking vitrualized

egos, in: Hans-Joerg Bullinger and Juegen Ziegler (eds.), Human-Computer

Interaction VOLUME 2, Lawrence Erlbaum Associates, Pub., pp 437-441,

1999

15 Nishida, T., Fujihara, N., Azechi, S., Sumi, K., and Hirata, T., Public Opinion

Channel for communities in the information age, New Generation Computing,

Vol 17, No 4, pp 417-427, 1999

16 Nishida, T (ed.), Dynamic Knowledge Interaction, CRC Press LLC, 2000

17 Nishida, T., Towards dynamic knowledge interaction, Keynote Paper, in: Proc

KES-2000, pp 1-12,2000

18 Nonaka, I and Takeuchi, H., The knowledge-creating company: How Japanese

companies create the dynamics of innovation, Oxford University Press, New

York, 1995

19 Ohguro, T., FaintPop: In touch with the social relationships, presented at Synsophy International Workshop on Social Intelligence Design, Matsue, Japan, May 21-22, 2001

JSAI-20 Okamoto, M., Isbister, K., Nakanishi, H and Ishida, T., Supporting

cross-cultural communication in real-world encounters, The 8th International

Conference on Human-Computer Interaction (HCI-99), Volume 2, pp

23 Schlichter, J., Koch, M., and Xu, C , Awareness — the common link between

groupware and community support systems, in: Ishida, T (ed.), Community

Computing and Support Systems — Social Interaction in Networked Communities, LNCS 1519, Springer, Berlin, pp 77-93, 1998

24 Sumi, K and Nishida, T., Context-aware and personalized communication

support system, IEEE Intelligent Systems, in press, 2001

25 Tajifel, H and Turner, J.C., The social identity theory of intergroup behavior,

in: Worchel, S and Austin, W G (eds.), Psychology of Intergroup Relations,

2nd Edition, Nelson-Hall, pp 7-24, 1986

26 Thomas, J C , Collaborative innovation tools, presented at JSAI-Synsophy International Workshop on Social Intelligence Design, Matsue, Japan, May 21-22,2001

Traditional query processing provides exact answers to queries It usually requires that users fully understand the database structure and content to issue a query Due to the complexity of the database applications, so called global queries can be posed which traditional query answering systems can not handle In this paper a query answering system based on distributed data mining is presented to rectify these problems

1 Introduction

In many fields, such as medical, banking and educational, similar databases are kept at many sites Each database stores information about local events and uses attributes suitable for a local task, but since the local situations are similar, the majority of attributes are compatible among databases An attribute may be missing in one database, while it occurs in many others Missing attributes lead to problems A user may issue a query to a local

database S\ in search for objects that match a desired description, only to realize that one component a\ of that description is missing in S\ so that the

query cannot be answered The same query may work in other databases but

the user is interested in identifying suitable objects only in S\

Clearly, the task of integrating established database systems is complicated not only by the differences between the sets of attributes but also by differ-ences in structure and semantics of data We call such systems heterogeneous The notion of an intermediate model, proposed by [Maluf and Wiederholdf, is very useful in dealing with the heterogeneity problem, because it describes the database content at a relatively high abstract level, sufficient to guarantee ho-mogeneous representation of all databases Discovery layers and action layers introduced in this paper, can be used for a similar purpose Discovery layer contains rules extracted from a database Actions layer contains, so called, action rules (see [Ras and Wieczorkowskaf) showing what minimal changes in

a database are needed to re-classify some of its objects

17

18

2 Distributed Knowledge Systems

In this section, we recall the notion of an information system and a distributed information system (DIS) Next, we introduce local queries and give their stan-dard semantics Finally, we show the structure of discovery layers and action layers

By an information system we mean S = (X, A, V), where X is a finite set

of objects, A is a finite set of attributes, and V = [J{V a : a G A} is a set of

their values We assume that:

• V a , V(, are disjoint for any a, b G A such that a ^ b,

• a : X —> V a is a function for every a £ A

Instead of a, we may write a^s] to denote that a in an attribute in S

By a distributed information system we mean a pair DS = ({Si}i^i,L)

where:

• / is a set of sites

• Si = (Xi, Ai,Vi) is an information system for any i G I,

• L is a symmetric, binary relation on the set /

A distributed information system DS = ({Si}i^i,L) is consistent if the

following condition holds:

(V;)(Vj)(Vz e Xi n X,)(Va e A z n A,) {a [Si] {x) = (a [Sj] )(x))

In the remainder of this paper we assume that DS = ({5j}ie/, L) is sistent Also, we assume that Sj = (Xj,Aj,Vj) where Vj — \J{Vj a : a G Aj}, for any j & I

con-We use A to denote the set of all attributes in DS, A = [){Aj : j G / } Also, by V we mean (J{Vj : j G / }

Before, we introduce the notion of a discovery layer, we begin with a

defini-tion of s(i)-terms and their standard interpretadefini-tion Mj in DS = ({Sj}j e i,L),

where Sj = (Xj,Aj,Vj) and Vj = \J{V ja : a G Aj}, for any j G /

By a set of s(i)-terms (also called a set of local queries for site i) we mean

a least set Tj such that:

• 0 , 1 G T u

• w G Ti for any w G Vj,

19

• if t i , t 2 eTi, then (*i + t 2 ), (h * t 2 ), ~ h £T t

By a set of s(2)-formulas we m e a n a least set F; such t h a t :

• if ti,t 2 G Ti, then (h = t 2 ) G F h

Definition of D 5 - t e r m s (also called a set of global queries) a n d DS-formulas

is quite similar (we only replace Tj by (J{Tj : i £ 1} and F, by F in two

defi-nitions above)

We say t h a t :

• s ( i ) - t e r m t is primitive if it is of t h e form l\{w : w G Ui} for any Ui C V»,

• s ( i ) - t e r m t = Y\{w : w G t/j} where U, C Vi is simple if £/$ n Via is a singleton set for any a £ Ai,

• s(«)-term is in disjunctive normal form (DNF) if t = ^T,{tj : j G J} where each tj is primitive

Similar definitions we have for D S - t e r m s

Clearly, it is easy t o give an example of a local query T h e expression:

select * from Flights

where airline = "Delta"

and departureJime — "morning"

and departure-airport = "Charlotte"

is an example of a non-local query (Z>S-term) in a d a t a b a s e

Flights(airline, departure dime, arrival-time,

departure-airport, arrival-airport)

Semantics of s ( i ) - t e r m s is defined by s t a n d a r d interpretation M* in a

dis-t r i b u dis-t e d informadis-tion sysdis-tem DS — ({Sj}j e i,L) as follows:

• M i ( 0 ) = 0, Mi(l) = Xt

• Mi(w) = {x G Xi : if w G Vja then w = h,(x, a)} for any w G V,,

• if ti,t 2 are s(i)-terms, then

M J ( i i + t 2 ) = M 1 ( ( 1 ) U M i ( i 2 ) ,

M i (t 1 *t 2 ) = M i {t 1 )nM i (t 2 ),

M i ( ~ t 1 ) = X i - M i ( t i )

Afifa = i2) =

(if Mi(ti) = M i ( i2) t h e n T else F )

where T s t a n d s for True and F for False

20

The sound and complete axiomatization of the above semantics is quite standard and for instance is given in paper by [Ras]6

Now, we are ready to introduce the notion of (fc,i)-rules, for any i £ I

We use them to form a discovery layer at site i £ I

By (fc,i)-rule in DS = ({SJ}J^I,L), k,i £ I, we mean a triple (c,t,s) such

Let us assume that r\ = (ci, t\, s\), r-i — (02, *2> ^2) a re (k, i)-rules We say

that: r i , r2 are strongly consistent, if either ci,c2 are values of two different

attributes in S k or a DNF form equivalent to t\ * £2 does not contain simple

conjuncts

Now, we are ready to define a discovery layer D k { Its elements can be

seen as approximate descriptions of values of attributes from V k — Vi in terms

of values of attributes from V k n V*

To be more precise, we say that D k i is a set of (k, i)-rules such that:

if (c, t, s) £ D k i and t\ = ~ (t + s), then (~ c, tl, s) e D k {

By a discovery layer for site i, denoted by Di, we mean any subset of

\J{D ki : (k,i) e L}

3 Actions Layer

In this section we introduce the notion of actions layer which is a basic part of

a distributed knowledge system (DKS)

Information systems can be seen as decision tables In any decision ble together with the set of attributes a partition of that set into conditions and decisions is given Additionally, we assume that the set of conditions is

ta-partitioned into stable conditions and flexible conditions Attribute a 6 A is called stable for the set X if its values assigned to objects from X can not be

21

changed in time Otherwise, it is called flexible Date of birth is an example

of a stable attribute Interest rate on any customer account is an example of a

flexible attribute For simplicity reason, we consider decision tables with only

one decision We adopt the following definition of a decision table:

A decision table is any information system of the form S = {X, A\ U A 2 U

{d},V), where d §t Ai U A 2 is a distinguished attribute called decision The

elements of A\ are called stable conditions, whereas the elements of A 2 U {d}

are called flexible conditions

The goal is to change values of attributes in Ai for some objects in X so

the values of the attribute d for these objects may change as well Rules in a

discovery layer defining d in terms of A\ U A 2 are extracted from S and used

to discover new rules called action rules7 These new rules provide suggestions

for re-classification of objects from S in terms of the attribute d It can be

done because d is flexible

Now, let us assume that (a,v —> w) denotes the fact that the value of

attribute o has been changed from v to w Similarly, the term {a, v —> w){x)

means that a{x) = v has been changed to a{x) = w Saying another words,

the property {a, v) of object x has been changed to property {a, w)

Assume now that S = {X, AiUA 2 U{d}, V) is a decision table, where A\ is a

set of stable attributes and A 2 is a set of flexible attributes Assume that rules

ri,r 2 have been extracted from S and ri/Ai = r 2 /A 2 ,d{n) = ki,d{r 2 ) = k 2

and hi < k 2 Also, assume that {b x , b 2 , , b p ) is a list of all attributes in

Dom{ri) D Dom{r 2 ) fl A 2 on which r\,r 2 differ and ri{b\) = Wi,7-1(62) =

v 2 , ,n{b p ) =v p

By (ri,r2)-action rule on x € X we mean a statement:

[(61,vi —>wi) A {b 2 ,v 2 —>• w 2 ) A A{b p ,v p —> w p )]{x) => [{d,fa) —>

{d,k 2 )]{x)

If the value of the above rule is true on x then the rule is valid for x

Otherwise is false

Action layer for a site i, denoted by Acti, contains ( r i , ^ - a c t i o n rules

constructed from rules r i , r 2 in a discovery layer Di

4 Distributed Knowledge S y s t e m

In this section, we introduce the notion of a distributed knowledge system

By Distributed Knowledge System {DKS) we mean DS = {{{Si, D t , Acti)} ie i, L)

where {{Si}i e i,L) is a distributed information system, Di = \J{D ki : {k,i) e

L} is a discovery layer and Acti is a n action layer for i £ I

Figure 1 shows the basic architecture of DKS (a query answering system

QAS that handles global queries is also added to each site of DKS)

Opera-22

Knowledge Exchange

Knowledge Exchange

Knowledge Exchange

Actions

Layer

Actions Layer Actions Layer Actions Layer

Operational

Semantics

QAS

Knowledge Exchange Discovery Layer

Discovery Layer

Knowledge Exchange

mining j j j mining j j j

Operational Semantics Operational Semantics

Discovery Layer

Operational Semantics

QAS

Figure 1: Distributed Knowledge System (DKS)

global query

Transformation engine based on logical axioms and operational semantics Ni local query at site i

(lower approximation)

QAS for site i

Figure 2: Query Answering System (QAS)

tional semantics reflects the dynamic nature of definitions of attribute values

in a query (see [Ras and Zytkowf)

Figure 2 shows a part of QAS which is responsible for query tion This part of QAS can be replaced by a rough transformation engine

transforma-shown in Figure 3

If for each non-local attribute we collect rules from many sites of DKS and

then resolve all inconsistencies among them (see [Rasp), then the local dence in resulting operational definitions is high since they represent consensus

confi-of many sites

Assume now that N is a standard interpretation of global queries as

in-troduced for instance in [Rasf It corresponds to a pessimistic approach to

QRAS for site i

Figure 3: Query Rough Answering System (QRAS)

evaluation of global queries because of the way the non-local attribute values are interpreted (their lower approximation is taken)

We can replace Ni by a new interpretation Ji representing optimistic

ap-proach to evaluation of global queries Namely, we define:

• Ji(w) =X- Ni(~ w),

• J i ( ~ w)=X- Ni(w),

• Jj(i) = Ni(t) for any other t

In optimistic approach to evaluation of queries, upper approximation of

non-local terms w, ~ w is taken

Following this line of thought, we can propose rough operational semantics

Ri defined as Ri(t) = [Ni(t), Ji(t)] for any global query t Rough operational

semantics has a natural advantage of either N t or Jj Clearly, if interpretations

Ni and Ji of a term t give us the same sets of objects, then both approximations

(lower and upper) are semantically equal

5 Query Answering Based on Reducts

In this section we recall the notion of a reduct (see [Pawlakf) and show how

it can be used to improve query answering process in DKS

Let us assume that 5 — (X, A, V), is an information system and V —

\J{V a '• a, € A} Let B C A We say that x,y £ X are indiscernible by B,

denoted [x « B y], if (Vo € B)[a{x) — a(y)]

Now, assume that both Bi,-02 are subsets of A We say that B\ depends

on B 2 if « B C « B I - Also, we say that B\ is a covering of B2 if B2 depends on

global query

Rough transformation engine based on logical axioms and operational semantics Ni and Ji

S3 = (X3, {b, e, g, h}, V3) are information systems,

• User submits a query q = q(c, e, / ) to the query answering system QAS associated with system Si,

• Systems Si, S 2 , S3 are parts of DKS

Attribute / is non-local for a system Si so the query answering system associated with Si has to contact other sites of DKS requesting a definition

of / in terms of {d, c, e,g} Such a request is denoted by < / : d,c,e,g > Assume that the system S 2 is contacted The definition of / , extracted from

52, involves only attributes {d, c, e, g} D {a, b, c, d, / } = {c, d} There are three /-reducts (coverings off) in 52- They are: {a, b}, {a, c}, {b, c} The optimal /-reduct is the one which has minimal number of elements outside {c, d} Let

us assume that {b, c} is chosen as an optimal /-reduct in 52

Then, the definition of / in terms of attributes {b, c} will be extracted from 52 and the query answering system of 52 will contact other sites of DKS requesting a definition of b (which is non-local for Si) in terms of attributes

{d,c,e,g} If definition of b is found, then it is sent to QAS of the site 1

Figure 4 shows the process of resolving query q in the example above

We will use the graph in Figure 5 to represent visually the fact: R[i] is an a-reduct at site i containing attribute b

Let us adopt the following definition By < ai,A >-linear set of reducts

we mean a set {< Oj, R[i] >: 1 < i < k} such that:

• a, £ A, for any 1 < i < k

• ai + i G R[i], for any 1 < i < k — 1

• R[i] is an a^-reduct at site i and card(^4 — i?[i]) = l, for any 1 < i < k — 1

• R[k] C A

25

overlap X2

c 3 - >

- > f 1

- > f 3 f4 rules extracted

at site 2

Coverings of b: y7 {e}, {g,h}

Covering {e} is chosen as optimal one

yi y2

y3

y4

y5

y6 y7 y8

b

b1

b1 b1 b1

b2

b2

b2 b2

e

e1 e1

e1 e1

e2

e2

e3 e3

g h

gi g2

gi

g2

g2

g2 g3

g3

hi h2

Figure 4: Process of resolving a query by QAS in DKS

26

a3 R[1] I 1 R[2] I

; *

R[1], R[2], , R[k-1] should have minimal number of attributes outside A R[k] is a subset of A

lR[k-1]

R[k]

Figure 6: < a\,A >-linear set of reducts

Figure 6 visually represents < a\, A >-linear set of reducts Clearly, the existence of < a, A >-linear set of reducts is sufficient for attribute a to be definable in DKS The existence of < a, A >-directed set of reducts (defined below) is necessary for attribute a to be definable in DKS

By < ai, A >-directed set of reducts we mean a smallest, non-empty set {< a,, R[i], Si >: 1 < i < k} such that:

• ai $ A, for any 1 < i < k

• Si is a site of DKS, for any 1 < i < k

• R[i] is an a^-reduct at site Sj, for any 1 < i < k

• (Va G U{#[*] :i<k}- A){3j < k)[a = a,]

• R[k] C A

Clearly, for every (ai,A) we have to search for the smallest < ai,A

>-directed set of reducts, to guarantee the smallest number of steps needed to

learn the definition of attribute a\ while keeping the confidence of what we

learn still the highest

6 Conclusion

Query answering system for DKS can handle two types of queries:

Queries asking for all objects at a site i which satisfy a given description

(any attributes are allowed to be used here) In such a case, query answering system will search for operational definitions of all attributes not-existing at

the site i, before it can process the query locally

Queries asking for actions which have to be undertaken in order to change

the classification of some objects at site i Such queries can be processed