Decision process modeling across internet and real world by double helical model of chance discovery

A case is presented for the double helical processing of chance discovery — human and an automated data mining system co-work, each progressing spirally toward the creative reconstruction of ideas. Especially, the discovery of what we call chances, significant novel events, is realized in this process. The example shown here is an application to questionnaire analysis for understanding new behaviors of Internet users. Internet users are born and bred with face-to-face human relations in the real world, but their interactions with WWW are distilling new value-criteria, keeping personal real-world senses of rationality, empathy, ethics, etc. In our method for aiding the discovery based on the double-helix model, the in-depth interaction of the Internet, the fundamental (i.e., common both in the Internet and in the real world) characters and the behaviors of people are discussed with revealing unnoticed value-criteria. Yukio Ohsawa, Ph.D.: BS, U. Tokyo, 1990, MS, 1992, DS, 1995. Research associate Osaka U. (1995). Associate prof. Univ. of Tsukuba (1999-) and also researcher of Japan Science and Technology Corp (2000-). He has been working for the program com. of the Workshop on Multiagent and Cooperative Computation, Annual Conf. Japanese Soc. Artificial Intelligence, International Conf. MultiAgent Systems, Discovery Science, Pacific Asia Knowledge Discovery and Data Mining, International Conference on Web Intelligence, etc. He chaired the First International Workshop of Japanese Soc. on Artificial Intelligence, Chance Discovery International Workshop Series and the Fall Symposium on Chance Discovery from AAAI. Guest editor of Special Issues on Chance Discovery for the Journal of Contingencies and Crisis Management, Journal of Japan Society for Fuzzy Theory and intelligent informatics, regular member of editorial board for Japanese Society of Artificial Intelligence. Currently he is authoring book “Chance Discovery” from Springer Verlag, “Knowledge Managament” from Ohmsha etc. Yumiko Nara, Ph.D.: She graduated from Nara Women’s University in 1987 and obtained her Master and Ph.D. degrees from Nara Women’s University respectively in 1993 and 1996. From 1987 through 1990 she worked for Sumitomo Bank. She is at Osaka Kyoiku University as lecturer (1997–2001) and as associate professor (2002-). She serves as a member of The Japan Sociological Society, The Japan Association for Social and Economic Systems Studies, The Japan Society of Home Economics, and The Japan Risk Management Society. She is an editorial committee member of the journal of Social and Economic Systems Studies (2001-), and a council member of The Japan Risk Management Society (1997-). In 1997, she received research awards from The Japan Society of Home Economics and The Japan Risk Management Society for studies on risk management.     

Inductive logic programming for relational knowledge discovery

Inductive logic programming (ILP) is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowledge discovery resulted in advanced techniques that are practically applicable for discovering knowledge in relational databases. This paper gives a brief introduction to ILP, presents selected ILP techniques for relational knowledge discovery and reviews selected ILP applications. Nada Lavrač, Ph.D.: She is a senior research associate at the Department of Intelligent Systems, J. Stefan Institute, Ljubljana, Slovenia (since 1978) and a visiting professor at the Klagenfurt University, Austria (since 1987). Her main research interest is in machine learning, in particular inductive logic programming and intelligent data analysis in medicine. She received a BSc in Technical Mathematics and MSc in Computer Science from Ljubljana University, and a PhD in Technical Sciences from Maribor University, Slovenia. She is coauthor of KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems, The MIT Press 1989, and Inductive Logic Programming: Techniques and Applications, Ellis Horwood 1994, and coeditor of Intelligent Data Analysis in Medicine and Pharmacology, Kluwer 1997. She was the coordinator of the European Scientific Network in Inductive Logic Programming ILPNET (1993–1996) and program cochair of the 8th European Machine Learning Conference ECML’95, and 7th International Workshop on Inductive Logic Programming ILP’97. Sašo Džeroski, Ph.D.: He is a research associate at the Department of Intelligent Systems, J. Stefan Institute, Ljubljana, Slovenia (since 1989). He has held visiting researcher positions at the Turing Institute, Glasgow (UK), Katholieke Universiteit Leuven (Belgium), German National Research Center for Computer Science (GMD), Sankt Augustin (Germany) and the Foundation for Research and Technology-Hellas (FORTH), Heraklion (Greece). His research interest is in machine learning and knowledge discovery in databases, in particular inductive logic programming and its applications and knowledge discovery in environmental databases. He is co-author of Inductive Logic Programming: Techniques and Applications, Ellis Horwood 1994. He is the scientific coordinator of ILPnet2, The Network of Excellence in Inductive Logic Programming. He was program co-chair of the 7th International Workshop on Inductive Logic Programming ILP’97 and will be program co-chair of the 16th International Conference on Machine Learning ICML’99. Masayuki Numao, Ph.D.: He is an associate professor at the Department of Computer Science, Tokyo Institute of Technology. He received a bachelor of engineering in electrical and electronics engineering in 1982 and his Ph.D. in computer science in 1987 from Tokyo Institute of Technology. He was a visiting scholar at CSLI, Stanford University from 1989 to 1990. His research interests include Artificial Intelligence, Global Intelligence and Machine Learning. Numao is a member of Information Processing Society of Japan, Japanese Society for Artificial Intelligence, Japanese Cognitive Science Society, Japan Society for Software Science and Technology and AAAI.     

Intelligent systems using Web-pages as knowledge base for statistical decision making

In this paper, we propose an approach to the construction of an intelligent system that handles various domain information provided on the Internet. The intelligent system adopts statistical decision-making as its reasoning framework and automatically constructs probabilistic knowledge, required for its decision-making, from Web-pages. This construction of probabilistic knowledge is carried out using aprobability interpretation idea that transforms statements in Web-pages into constraints on the subjective probabilities of a person who describes the statements. In this paper, we particularly focus on describing the basic idea of our approach and on discussing difficulties in our approach, including our perspective. Kazunori Fujimoto: He received bachelor’s degree from Department of Electrical Engineering, Doshisha University, Japan, in 1989, and master’s degree from Division of Applied Systems Science, Kyoto University, Japan, in 1992. From there, he joined NTT Electrical Communications Laboratories, Tokyo, Japan, and has been engaged in research on Artificial Intelligence. He is currently interested in probabilistic reasoning, knowledge acquisition, and especially in quantitative approaches to research in human cognition and behavior. Mr. Fujimoto is a member of Decision Analysis Society, The Behaviormetric Society of Japan, Japanese Society for Artificial Intelligence, Information Processing Society of Japan, and Japanese Society for Fuzzy Theory and Systems. Kazumitsu Matsuzawa: He received B.S. and M.S. degrees in electronic engineering from Tokyo Institute of Technology, Tokyo, Japan, in 1975 and 1977. From there, he joined NTT Electrical Communications Laboratories, Tokyo, Japan, and has been engaged in research on computer architecture and the design of LSI. He is currently concerned with AI technology. Mr. Matsuzawa is a member of The Institute of Electronics, Information and Communication Engineers, Information Processing Society of Japan, Japanese Society for Artificial Intelligence, and Japanese Society for Fuzzy Theory and Systems.     

Public opinion channel for communities in the information age

In this paper, we propose as a new challenge a public opinion channel which can provide a novel communication medium for sharing and exchanging opinions in a community. Rather than simply developing a means of investigating public opinion, we aim at an active medium that can facilitate mutual understanding, discussion, and public opinion formation. First, we elaborate the idea of public opinion channels and identify key issues. Second, we describe our first step towards the goal using the talking virtualized egos metaphor. Finally, we discuss a research agenda towards the goal. Toyoaki Nishida, Dr.Eng.: He is a professor of Department of Information and Communication Engineering, School of Engineering, The University of Tokyo. He received the B.E., the M.E., and the Doctor of Engineering degrees from Kyoto University in 1977, 1979, and 1984 respectively. His research centers on artificial intelligence in general. His current research focuses on community computing and support systems, including knowledge sharing, knowledge media, and agent technology. He has been leading the Breakthrough 21 Nishida Project, sponsored by Ministry of Posts and Telecommunications, Japan, aiming at understanding and assisting networked communities. Since 1997, he is a trustee for JSAI (Japanese Society for Artificial Intelligence), and serves as the program chair of 1999 JSAI Annual Convention. He is an area editor (intelligent systems) of New Generation Computing and an editor of Autonomous Agents and Multiagent Systems. Nobuhiko Fujihara, Ph.D.: He is a fellow of Breakthrough 21 Nishida project, Communications Research Laboratory sponsored by Ministry of Posts and Telecommunications, Japan. He received the B.E., the M.E., and the Ph.D. in Human Sciences degrees from Osaka University in 1992, 1994, and 1998 respectively. He has a cognitive psychological background. His current research focuses on: (1) cognitive psychological analysis of human behavior in a networked community, (2) investigation of information comprehension process, (3) assessment and proposition of communication tools in networking society. Shintaro Azechi: He is a fellow of Breakthrough 21 Nishida project, Communications Research Laboratory sponsored by Ministry of Posts and Telecommunications, Japan. He received the B.E. and the M.E. of Human Sciences degrees from Osaka University in 1994 and 1996 respectively. He is a Doctoral Candidate of Graduate School of Human Sciences, Osaka University. His current researches focus on (1) human behavior in networking community (2) social infomation process in human mind (3) development of acessment technique for communication tools in networkingsociety. His approach is from social psychological view. Kaoru Sumi, Dr.Eng.: She is a Researcher of Breakthrough 21 Nishida Project. She received her Bachelor of Science at School of Physics, Science University of Tokyo. She received her Master of Systems Management at Graduate School of Systems Management, The university of Tsukuba. She received her Doctor of engineering at Graduate School of Engineering, The University of Tokyo. Her research interests include knowledge-based systems, creativity supporting systems, and their applications for facilitating human collaboration. She is a member of the Information Processing Society of Japan (IPSJ), the Japanese Society for Artificial Intelligence (JSAI). Hiroyuki Yano, Dr.Eng.: He is a senior research official of Kansai Advanced Research Center, Communications Research Laboratory, Ministry of Posts and Telecommunications. He received the B.E., the M.E., and the Doctor of Engineering degrees from Tohoku University in 1986, 1988, and 1993 respectively. His interests of research include cognitive mechanism of human communications. His current research focuses on discourse structure, human interface, and dialogue systems for human natural dialogues. He is a member of the Japanese Society for Artificial Intelligence, the Association for Natural Language Processing, and the Japanese Cognitive Science Society. Takashi Hirata: He is a doctor course student in Graduate School of Information Scienc at Nara Institute of Science and Technology (NAIST). He received a master of engineering from NAIST in 1998. His research interest is knowledge media and knowledge sharing. He is a member of Information Processing Society of Japan (IPSJ), Japan Association for Artificial Intelligence (JSAI) and The Institute of Systems, Control and Information Engineers (ISCIE).     

Chance discoveries for making decisions in complex real world

Chance discovery is to become aware of a chance and to explain its significance, especially if the chance is rare and its significance is unnoticed. This direction matches with various real requirements in human life. This paper presents the significance, viewpoints, theories, methods, and future work of chance discovery. Three keys for the progress are extracted from fundamental discussions on how to realize chance discovery: (1) communication, (2) imagination, and (3) data mining. As an approach to chance discovery, visualized data mining methods are formalized as tools aiding chance discoveries on the basis of these keys. Yukio Ohsawa, Ph.D.: He received Bechelor of Engineering (1990) from Faculty of Engineering, Master of Engineering (1992) and Ph.D. (1995) from Graduate School of Engineering, respectively of The University of Tokyo. In the doctoral course he began artificial intelligence research, especially of abductive inference. He was a research associate (1995–1999) in Osaka University on studies of text mining and related issues, and moved to the current position, associate professor in the University of Tsukuba in 1999. From 2001, he is also a researcher of TRESTO (changed to PRESTO) in Japan Science and Technology Corporation. He received best paper awards in two Annual Conferences of Japasese Society of AI (1994 and 1998), and a Journal Paper Award from JSAI in 1998. His social activities are committees of conferences e.g., International Conference of Multi-Agent Systems (ICMAS) since 1998 and Discovery Science (DS) since 2001, program chair of MultiAgent and Cooporative Computations (MACC, in Japan) in 1999, and committes of meetings including ones on Chance Discovery.     

Tutorial on ontological engineering Part 2: Ontology development,tools and languages

Practical aspects of ontological engineering are discussed in this part. First topic is the methodology of ontology development. Next, ontology representation languages and support tools are discussed as well as ontology alignment and merging which are becoming practically important to cope with distributed development of ontologies. We next discuss several ontologies developed thus far including large-scale knowledge bases such as Cyc, practical domain ontologies such as Enterprise ontology and gene ontology and generic ontologies such as PSL: Process Specification Language and SUO: Standard Upper Ontology. The first topic of ontology applications is the semantic web in which semantic interoperability, metadata and web service ontology are described. e-Learning is also a good application area of ontology in which LOM: Learning Object Metadata and ontology-aware authoring systems are discussed followed by conclusion. Riichiro Mizoguchi, Ph.D.: He is Professor of the Department of Knowledge Systems, the Institute of Scientific and Industrial Research, Osaka University. He received his B.S., M.S., and Ph.D. degrees from Osaka University in 1972, 1974 and 1977 respectively. From 1978 to 1986 he was research associate in the Institute of Scientific and Industrial Research, Osaka University. From 1986 to 1989 he was Associate Professor there. His research interests include Non-parametric data analyses, Knowledge-based systems, Ontological engineering and Intelligent learning support systems. He is a member of the Japanese Society for Artificial Intelligence, the Institute of Electronics, Information and Communica-tion Engineers, the Information Processing Society of Japan, the Japanese Society for Information and Systems in Education, Intl. AI in Education (IAIED) Soc., AAAI, IEEE and APC of AACE. Currently, he is President of IAIED Soc. and APC of AACE. He received honorable mention for the Pattern Recognition Society Award, the Institute of Electronics, Information and Communication Engineers Award, 10th Anniversary Paper Award from the Japanese Society for Artificial Intelligence and Best paper Award of ICCE99 in 1985, 1988, 1996 and 1999, respectively. He can be reached at miz@ei.sanken.osaka-u.ac.jp     

Pitch-Dependent Identification of Musical Instrument Sounds

This paper describes a musical instrument identification method that takes into consideration the pitch dependency of timbres of musical instruments. The difficulty in musical instrument identification resides in the pitch dependency of musical instrument sounds, that is, acoustic features of most musical instruments vary according to the pitch (fundamental frequency, F0). To cope with this difficulty, we propose an F0-dependent multivariate normal distribution, where each element of the mean vector is represented by a function of F0. Our method first extracts 129 features (e.g., the spectral centroid, the gradient of the straight line approximating the power envelope) from a musical instrument sound and then reduces the dimensionality of the feature space into 18 dimension. In the 18-dimensional feature space, it calculates an F0-dependent mean function and an F0-normalized covariance, and finally applies the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments shows that the proposed method improved the recognition rate from 75.73% to 79.73%. This research was partially supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Grant-in-Aid for Scientific Research (A), No.15200015, and Informatics Research Center for Development of Knowledge Society Infrastructure (COE program of MEXT, Japan). Tetsuro Kitahara received the B.S. from Tokyo University of Science in 2002 and the M.S. from Kyoto University in 2004. He is currently a Ph.D. course student at Graduate School of Informatics, Kyoto University. Since 2005, he has been a Research Fellow of the Japan Society for the Promotion of Science. His research interests include music informatics. He recieved IPSJ 65th National Convention Student Award in 2003, IPSJ 66th National Convention Student Award and TELECOM System Technology Award for Student in 2004, and IPSJ 67th National Convention Best Paper Award for Young Researcher in 2005. He is a student member of IPSJ, IEICE, JSAI, ASJ, and JSMPC. Masataka Goto received his Doctor of Engineering degree in Electronics, Information and Communication Engineering from Waseda University, Japan, in 1998. He then joined the Electrotechnical Laboratory (ETL; reorganized as the National Institute of Advanced Industrial Science and Technology (AIST) in 2001), where he has been engaged as a researcher ever since. He served concurrently as a researcher in Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Corporation (JST) from 2000 to 2003, and an associate professor of the Department of Intelligent Interaction Technologies, Graduate School of Systems and Information Engineering, University of Tsukuba since 2005. His research interests include music information processing and spoken language processing. Dr. Goto received seventeen awards including the IPSJ Best Paper Award and IPSJ Yamashita SIG Research Awards (MUS and SLP) from the Information Processing Society of Japan (IPSJ), Awaya Prize for Outstanding Presentation and Award for Outstanding Poster Presentation from the Acoustical Society of Japan (ASJ), Award for Best Presentation from the Japanese Society for Music Perception and Cognition (JSMPC), WISS 2000 Best Paper Award and Best Presentation Award, and Interaction 2003 Best Paper Award. He is a member of the IPSJ, ASJ, JSMPC, Institute of Electronics, Information and Communication Engineers (IEICE), and International Speech Communication Association (ISCA). Hiroshi G. Okuno received the B.A. and Ph.D from the University of Tokyo in 1972 and 1996, respectively. He worked for Nippon Telegraph and Telephone, Kitano Symbiotic Systems Project, and Tokyo University of Science. He is currently a professor at the Department of Intelligence Technology and Science, Graduate School of Informatics, Kyoto University. He was a visiting scholar at Stanford University, and a visiting associate professor at the University of Tokyo. He has done research in programming languages, parallel processing, and reasoning mechanism in AI, and he is currently engaged in computational auditory scene analysis, music scene analysis and robot audition. He received the best paper awards from the Japanese Society for Artificial Intelligence and the International Society for Applied Intelligence, in 1991 and 2001, respectively. He edited with David Rosenthal “Computational Auditory Scene Analysis” from Lawrence Erlbaum Associates in 1998 and with Taiichi Yuasa “Advanced Lisp Technology” from Taylor and Francis Inc. in 2002. He is a member of IPSJ, JSAI, JSSST, JSCS, ACM, AAAI, ASA, and IEEE.     

Toward a real-world knowledge medium: Building a guidance system for exhibition tours

We propose a notion of a real-world knowledge medium by presenting our ongoing project to build a guidance system for exhibition tours. In order to realize a knowledge medium usable in the real world, we focus on the context-awareness of users and their environments. Our system is a personal mobile assistant that provides visitors touring exhibitions with information based on their spatial/temporal locations and individual interests. We also describe an application of knowledge sharing used in the actual exhibition spaces. Yasuyuki Sumi, Ph.D.: He has been a researcher at ATR Media Integration & Communications Research Laboratories since 1995. His research interests include knowledge-based systems, creativity supporting systems, and their applications for facilitating human collaboration. He received his B. Eng. degree from Waseda University in 1990, and M. Eng. and D. Eng. degrees in information engineering from the University of Tokyo in 1992 and 1995, respectively. He is a member of Institutes of Electronics, Information and Communication Engineers (IEICE) of Japan, the Information Processing Society of Japan (IPSJ), the Japanese Society for Artificial Intelligence (JSAI), and American Association for Artificial Intelligence (AAAI). Kenji Mase, Ph.D.: He received the B.S. degree in Electrical Engineering and the M.S. and Ph.D. degrees in Information Engineering from Nagoya University in 1979, 1981 and 1992 respectively. He has been with ATR (Advanced Telecommunications Research Institute) Media Integration & Communications Research Laboratories since 1995 and is currently the head of Department 2. He joined the Nippon Telegraph and Telephone Corporation (NTT) in 1981 and had been with the NTT Human Interface Laboratories. He was a visiting researcher at the Media Laboratory, MIT in 1988–1989. His research interests include image sequence processing of human actions, computer graphics, computer vision, artificial intelligence and their applications for computer-aided communications and human-machine interfaces. He is a member of the Information Processing Society of Japan (IPSJ), Institutes of Electronics, Information and Communication Engineers (IEICE) of Japan and IEEE Computer Society.     

Part 3: Advanced course of ontological engineering

This article deals with advanced topics of ontological engineering to convince readers ontology is more than a rule base of terminological problems and is worth to consider a promising methodology in the next generation knowledge processing research. Needless to say, ontology in AI is tightly connected to ontology in philosophy. The first topic here is on philosophical issues which are very important to properly understand what an ontology is. After defining class, instance andis-a relation, we point out some typical inappropriate uses ofis-a relation in existing ontologies and analyze the reasons why. Other topics are basic ontological distinction, part-of relation, and so on. As an advanced example of ontology, an ontology of representation is extensively discussed. To conclude this tutorial, a success story of ontological engineering is presented. It is concerned with a new kind of application of ontology, that is, knowledge systematization. An ontology-based framework for functional knowledge sharing has been deployed into a company for two years and has been a great success. Finally, future of ontological engineering is discussed followed by concluding remarks. Riichiro Mizoguchi, Ph.D.: He is Professor of the Department of Knowledge Systems, the Institute of Scientific and Industrial Research, Osaka University. He received his B.S., M.S., and Ph.D. degrees from Osaka University in 1972, 1974 and 1977 respectively. From 1978 to 1986 he was research associate in the Institute of Scientific and Industrial Research, Osaka University. From 1986 to 1989 he was Associate Professor there. His research interests include Non-parametric data analyses, Knowledge-based systems, Ontological engineering and Intelligent learning support systems. He is a member of the Japanese Society for Artificial Intelligence, the Institute of Electronics, Information and Communica-tion Engineers, the Information Processing Society of Japan, the Japanese Society for Information and Systems in Education, Intl. AI in Education (IAIED) Soc., AAAI, IEEE and APC of AACE. Currently, he is President of IAIED Soc. and APC of AACE. He received honorable mention for the Pattern Recognition Society Award, the Institute of Electronics, Information and Communication Engineers Award, 10th Anniversary Paper Award from the Japanese Society for Artificial Intelligence and Best paper Award of ICCE99 in 1985, 1988, 1996 and 1999, respectively. He can be reached at miz@ei.sanken.osaka-u.ac.jp     

Web information visualization method employing immune network model for finding topic stream from document-set sequence

A Web information visualization method based on the document set-wise processing is proposed to find the topic stream from a sequence of document sets. Although the hugeness as well as its dynamic nature of the Web is burden for the users, it will also bring them a chance for business and research if they can notice the trends or movement of the real world from the Web. A sequence of document sets found on the Web, such as online news article sets is focused on in this paper. The proposed method employs the immune network model, in which the property of memory cell is used to find the topical relation among document sets. After several types of memory cell models are proposed and evaluated, the experimental results show that the proposed method with memory cell can find more topic streams than that without memory cell. Yasufumi Takama, D.Eng.: He received his B.S., M.S. and Dr.Eng. degrees from the University of Tokyo in 1994, 1996, and 1999, respectively. From 1999 to 2002 he was with Tokyo Institute of Technology, Japan. Since 2002, he has been Associate Professor of Department of Electronic Systems and Engineering, Tokyo Metropolitan Institute of Technology, Tokyo, Japan. He has also been participating in JST (Japan Science and Technology Corporation) since October 2000. His current research interests include artificial intelligence, Web information retrieval and visualization systems, and artificial immune systems. He is a member of JSAI (Japanese Society of Artificial Intelligence), IPS J (Information Processing Society of Japan), and SOFT (Japan Society for Fuzzy Theory and Systems). Kaoru Hirota, D.Eng.: He received his B.E., M.E. and Dr.Eng. degrees in electronics from Tokyo Institute of Technology, Tokyo, Japan, in 1974, 1976, and 1979, respectively. From 1979 to 1982 and from 1982 to 1995 he was with the Sagami Institute of Technology and Hosei University, respectively. Since 1995, he has been with the Interdisciplinary Graduate School of Science and Technology, Tokyo Institute of Technology, Yokohama, Japan. He is now a department head professor of Department of Computational Intelligence and Systems Science. Dr.Hirota is a member of IFSA (International Fuzzy Systems Association (Vice President 1991–1993), Treasurer 1997–2001), IEEE (Associate Editors of IEEE Transactions on Fuzzy Systems (1993–1995) and IEEE Transactions on Industrial Electronics (1996–2000)) and SOFT (Japan Society for Fuzzy Theory and Systems (Vice President 1995–1997, President 2001–2003)), and he is an editor in chief of Int. J. of Advanced Computational Intelligence.     

Multiagent learning towards RoboCup

This article describes the issues in multiagent learning towards RoboCup,^1≈3) especially for the real robot leagues. First, the review of the issue in the context of the related area is given, then related works from several viewpoints are reviewed. Next, our approach towards RoboCup Initiative is introduced and finally future issues are given. Minoru Asada, Ph.D.: He received B.E., M.Sc., and Ph.D., degrees in control engineering from Osaka University, in 1977, 1979, and 1982, respectively. From 1982 to 1988, he was a research associate of Control Engineering, Osaka University. In 1989, he became an associate professor of Mechanical Engineering for Computer-Controlled Machinery, Osaka University. In 1995 he became a professor of the department of Adaptive Machine Systems at the same university. From 1986 to 1987, he was a visiting researcher of Center for Automation Research, University of Maryland, College Park, MD. He received the 1992 best paper award of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS92), and the 1996 best paper award of RSJ (Robotics Society of Japan). Also, his paper was one of the finalists of IEEE Robotics and Automation Society 1995 Best Conference Paper Award. He was a general chair of IEEE/RSJ 1996 International Conference on Intelligent Robots and Systems (IROS96). Since early 1990, he has been involved in RoboCup activities and his team was the first champion team with USC team in the middle size league of the first RoboCup held in conjunction with IJCAI-97, Nagoya, Japan. Eiji Uchibe, Ph.D.: He received a Ph.D. degree in mechanical engineering from Osaka University in 1999. He is currently a research associate of the Japan Society for the Promotion of Science, in Research for the Future Program titled Cooperative Distributed Vision for Dynamic Three Dimensional Scene Understanding. His research interests are in reinforcement learning, evolutionary computation, and their applications. He is a member of IEEE, AAAI, RSJ, and JSAI.     

A separation method for DNA computing based on concentration control

A separation method for DNA computing based on concentration control is presented. The concentration control method was earlier developed and has enabled us to use DNA concentrations as input data and as filters to extract target DNA. We have also applied the method to the shortest path problems, and have shown the potential of concentration control to solve large-scale combinatorial optimization problems. However, it is still quite difficult to separate different DNA with the same length and to quantify individual DNA concentrations. To overcome these difficulties, we use DGGE and CDGE in this paper. We demonstrate that the proposed method enables us to separate different DNA with the same length efficiently, and we actually solve an instance of the shortest path problems. Masahito Yamamoto, Ph.D.: He is associate professor of information engineering at Hokkaido University. He received Ph.D. from the Graduate School of Engineering, Hokkaido University in 1996. His current research interests include DNA computing based the laboratory experiments. He is a member of Operations Research Society of Japan, Japanese Society for Artificial Intelligence, Information Processing Society of Japan etc. Atsushi Kameda, Ph.D.: He is the research staff of Japan Science and Technology Corporation, and has participated in research of DNA computing in Hokkaido University. He received his Ph.D. from Hokkaido University in 2001. For each degree he majored in molecular biology. His research theme is about the role of polyphosphate in the living body. As one of the researches relevant to it, he constructed the ATP regeneration system using two enzyme which makes polyphosphate the phosphagen. Nobuo Matsuura: He is a master course student of Division of Systems and Information Engineering of Hokkaido University. His research interests relate to DNA computing with concentration control for shortest path problems, as a means of solution of optimization problems with bimolecular. Toshikazu Shiba, Ph.D.: He is associate, professor of biochemical engineering at Hokkaido University. He received his Ph.D. from Osaka University in 1991. He majored in molecular genetics and biochemistry. His research has progressed from bacterial molecular biology (regulation of gene expression of bacterial cells) to tissue engineering (bone regeneration). Recently, he is very interested in molecular computation and trying to apply his biochemical idea to information technology. Yumi Kawazoe: She is a master course student of Division of Molecular Chemistry of Hokkaido University. Although her major is molecular biology, she is very interested in molecular computation and bioinformatics. Azuma Ohuchi, Ph.D.: He is professor of Information Engineering at the University of Hokkaido, Sapporo, Japan. He has been developing a new field of complex systems engineering, i.e., Harmonious Systems Engineering since 1995. He has published numerous papers on systems engineering, operations research, and computer science. In addition, he is currently supervising projects on DNA computing, multi-agents based artificial market systems, medical informatics, and autonomous flying objects. He was awarded “The 30th Anniversary Award for Excellent Papers” by the Information Processing Society of Japan. He is a member of Operations Research Society of Japan, Japanese Society for Artificial Intelligence, Information Processing Society of Japan, Japan Association for Medical Informatics, IEEE Computer Society, IEEE System, Man and Cybernetics Society etc. He received PhD from Hokkaido University in 1976.     

Cooperation of categorical and behavioral learning in a practical solution to the abstraction problem

Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning in parallel with task execution. We call the architectureSituation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors. Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture is able to learn efficiently and adapt to unexpected changes of the environment autonomously. Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI). Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for knowledge base systems.     

Discover unknown causes from inferred and visualized Co-occurring events

It is hard to have knowledge including all events which may have caused observed events. This makes it difficult to infer significant causes of observed events. However, unexpected relations detected between known events by a computer suggest unknown events to humans, being combined with the vast human knowledge acquired by rich experience. This paper presents a method to have a computer express “unknown” hidden causes, i.e. not included in the given knowledge. The inference method of the computer, for inferring known causes of observed time-series events, is Cost-based Cooperation of Multi-Abducers (CCMA) here aiming at detecting unexpectedly strong co-occurrences among known events. The detected relations are expressed to user, which makes significant unknown causal events easily understood. The empirical results encourages that the presented method helps in discovering significant unknown events. Yukio Ohsawa, Ph.D.: He is an Associate Professor in the Graduate School of Systems Management, University of Tsukuba. He obtained his bachelors, masters, and Ph.D. degrees in Engineering from the University of Tokyo in 1990, 1992 and 1995 respectively. He was a research associate in Osaka University from 1995 to 1999. His research interests are in discovering signs of future events affecting human life, from data, based on his background of artificial intelligence. He received the Paper Award from the Japanese Society of Artificial Intelligence in 1999 and some awards for conference papers. He has served on program commitees of several conferences and workshops on AI and agents, currently chairing Multi-agent and Cooperative Computing workshops (MACC99). Masahiko Yachida, Ph.D.: He is a professor at the Dept. of Systems Engineering of Osaka University since 1993. He obtained his B. E., M.Sc in electrical engineering and Ph.D. in control engineering from Osaka University in 1969, 1971, and 1976 respectively. He became a professor of the Dept. of Information and Computer Science of the same university in 1990, and moved to the current department as a professor. He was a research fellow at the Fachbereich Informatik, Hamburg University from 1981 to 1982, and a CDC professor at the Dept. of Computer Science, University of Minessota in 1983. He received several prizes including Ohkawa Publishing Prize, and is presently a Chairman of Technical Committee on Pattern Recognition & Media Understanding.     

Interactive learning and management of visual information via human-like software robot

To achieve smooth real-world interaction between people and computers, we developed a system that displays a three-dimensional computer-graphic human-like image from the waist up (anthropomorphic software robot: hereinafter “robot”) on the display, that interactively sees and hears, and that has fine and detailed control functions such as facial expressions, line of sight, and pointing at targets with its finger. The robot visually searches and identifies persons and objects in real space that it has learned in advance (registered space, which was our office in this case), manages the history information of the places and times it found objects and/or persons, and tells the user, indicating their three-dimensional positions with line of sight and its finger. It interactively learns new objects and persons with line of with their names and owners. By using this function, the robot can engage in simple dialogue (do a task) with the user. Osamu Hasegawa, Ph.D.: He received the B.E. and M.E. degrees from the Science University of Tokyo, in 1988, 1990 respectively. He received Ph.D. degree from the University of Tokyo, in 1993. Currently, he is a senior research scientist at the Electrotechnical Laboratory (ETL), Tsukuba, Japan. His research interests include Computer Vision and Multi-modal Human Interface. Dr. Hasegawa is a member of the AAAI, the Institute of Electronics, Information and Communication Engineers, Japan (IEICE), Information Processing Society of Japan and others. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from the University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information Processing Society of Japan. He is a member of IEICE, IEEE, IPSJ, ITE. Satoru Hayamizu, Ph.D.: He is a leader of Interactive Intermodal Integration Lab. at Electrotechnical Laboratory. He received the B.E., M.E., Ph.D. degrees from Tokyo University. Since 1981, he has been working on speech recognition, spoken dialogue, and communication with artifacts. From 1989 to 1990, he was a visiting scholar in Carnegie Mellon University and in 1994 a visiting scientist in LIMSI/CNRS.     

An information management system with the facility to support long-term creative thinking

Most previous creativity support systems sustain short-term temporal thinking that is separate from users’ daily activities. In this paper, we propose a system to support long-term idea-generation in daily life. The system consists of two subsystems: a management system for problems and ideas calledIdeaManager, and a personal information storage system callediBox. When information is registered in iBox, it searches related problems and ideas in IdeaManager and presents the results. Users then try to generate or enhance ideas for automatically retrieved problems or ideas using registered information as the hint. To evaluate and enhance our system, we carried out a six-week experiment. Based on the results, we give some proposals for future systems. Hirohito Shibata: He received his B.Sci. degree from Kanazawa University in 1992 and his M.Sci. degree from Osaka University in 1994. He was a software engineer at Fuji Xerox Co., Ld from 1994 to 2000. He is currently a doctoral student with Department of Advanced Interdisciplinary Studies, University of Tokyo. His research interests include human-computer interaction and computer support for creative activities. He is a member of Japanese Society for Artificial Intelligence (JSAI) and Japanese Cognitive Science Society (JCSS). Koichi Hori, D.Eng.: He received his B.Eng, M.Eng, and Dr.Eng. degrees in electronic engineering from the University of Tokyo in 1979, 1981, and 1984, respectively. In 1984, he joined National Institute of Japanese Literature, where he developed AI systems for literature studies. Since 1988, he has been with the University of Tokyo. He is currently a professor with Department of Advanced Interdisciplinary Studies, the University of Tokyo. From September 1989 to January 1990, he also held a visiting position at University of Compiegne, France. His current research interests include AI technology for supporting human creative activities, cognitive engineering and Intelligent CAD systems. He is a member of IEEE, ACM, IEICE, IPS J, JSAI, JSSST, and JCSS.     

Trends of internet auctions and agent-mediated Web commerce

Electronic Commerce (EC) is a promising field for applying agent and Artificial Intelligence technologies. In this article, we give an overview of the trends of Internet auctions and agent-mediated Web commerce. We describe the theoretical backgrounds of auction protocols and introduce several Internet auction sites. Furthermore, we describe various activities aimed toward utilizing agent technologies in EC and the trends in standardization efforts on agent technologies. Makoto Yokoo, Ph.D.: He received the B.E. and M.E. degrees in electrical engineering, in 1984 and 1986, respectively, from the University of Tokyo, Japan, and the Ph.D. degree in information and communication engineering in 1995 from the University of Tokyo, Japan. He is currently a distinguished technical member in NTT Communication Science Laboratories, Kyoto, Japan. He was a visiting research scientist at the Department of Electrical Engineering and Computer Science, the University of Michigan, Ann Arbor, from 1990 to 1991. His current research interests include multi-agent systems, search, and constraint satisfaction. Satoru Fujita, D.Eng.: He received his B.E. and M.E. degrees in electronic engineering from the University of Tokyo in 1984 and 1986, respectively. He also received his D.Eng. from the University of Tokyo in 1989 for his research on context comprehension in natural language understanding. He joined NEC Corporation in 1989, and is now a principal researcher of Internet Systems Research Laboratories of NEC. He is engaged in research on mobile agents, distributed systems and Web services.     

User-independent gesture recognition by relative-motion extraction and discriminant analysis

We propose a new method for user-independent gesture recognition from time-varying images. The method uses relative-motion extraction and discriminant analysis for providing online learning/recognition abilities. Efficient and robust extraction of motion information is achieved. The method is computationally inexpensive which allows real-time operation on a personal computer. The performance of the proposed method has been tested with several data sets and good generalization abilities have been observed: it is robust to changes in background and illumination conditions, to users’ external appearance and changes in spatial location, and successfully copes with the non-uniformity of the performance speed of the gestures. No manual segmentation of any kind, or use of markers, etc. is necessary. Having the above-mentioned features, the method could be successfully used as a part of more refined human-computer interfaces. Bisser R. Raytchev: He received his BS and MS degrees in electronics from Tokai University, Japan, in 1995 and 1997 respectively. He is currently a doctoral student in electronics and information sciences at Tsukuba University, Japan. His research interests include biological and computer vision, pattern recognition and neural networks. Osamu Hasegawa, Ph.D.: He received the B.E. and M.E. degrees in Mechanical Engineering from the Science University of Tokyo, in 1988, 1990 respectively. He received Ph.D. degree in Electrical Engineering from the University of Tokyo, in 1993. Currently, he is a senior research scientist at the Electrotechnical Laboratory (ETL), Tsukuba, Japan. His research interests include Computer Vision and Multi-modal Human Interface. Dr. Hasegawa is a member of the AAAI, the Institute of Electronics, Information and Communication Engineers, Japan (IEICE), Information Processing Society of Japan and others. Nobuyuki Otsu, Ph.D.: He received B.S., Mr. Eng. and Dr. Eng. in Mathematical Engineering from the University of Tokyo in 1969, 1971, and 1981, respectively. Since he joined ETL in 1971, he has been engaged in theoretical research on pattern recognition, multivariate data analysis, and applications to image recognition in particular. After taking positions of Head of Mathematical Informatics Section (since 1985) and ETL Chief Senior Scientist (since 1990), he is currently Director of Machine Understanding Division since 1991, and concurrently a professor of the post graduate school of Tsukuba University since 1992. He has been involved in the Real World Computing program and directing the R&D of the project as Head of Real World Intelligence Center at ETL. Dr. Otsu is members of Behaviormetric Society and IEICE of Japan, etc.