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.     

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.     

Efficient discovery of optimal word-association patterns in large text databases

We study efficient discovery of proximity word-association patterns, defined by a sequence of strings and a proximity gap, from a collection of texts with the positive and the negative labels. We present an algorithm that finds alld-stringsk-proximity word-association patterns that maximize the number of texts whose matching agree with their labels. It runs in expected time complexityO(k ^d−1n log ^d n) and spaceO(k ^d−1n) with the total lengthn of texts, if texts are uniformly random strings. We also show that the problem to find one of the best word-association patterns with arbitrarily many strings in MAX SNP-hard. Shinichi Shimozono, Ph.D.: He is an Associate Professor of the Department of Artificial Intelligence at Kyushu Institute of Technology Iizuka, Japan. He obtained the B.S. degree in Physics from Kyushu University, awarded M.S. degree from Graduate School of Information Science in Kyushu University, and his Dr. Sci. degree in 1996 from Kyushu University. His research interests are primarily in the design and analysis of algorithms for intractable problems. Hiroki Arimura, Ph.D.: He is an Associate Professor of the Department of Informatics at Kyushu University, Fukuoka, Japan. He is also a researcher with Precursory Research for Embryonic Science and Technology, Japan Science and Technology Corporation (JST) since 1999. He received the B.S. degree in 1988 in Physics, the M.S. degree in 1979 and the Dr.Sci. degree in 1994 in Information Systems from Kyushu University. His research interests include data mining, computational learning theory, and inductive logic programming. Setsuo Arikawa, Ph.D.: He is a Professor of the Department of Informatics and the Director of University Library at Kyushu University, Fukuoka, Japan. He received the B.S. degree in 1964, the M.S. degree in 1966 and the Dr.Sci. degree in 1969 all in Mathematics from Kyushu University. His research interests include Discovery Science, Algorithmic Learning Theory, Logic and Inference/Reasoning in AI, Pattern Matching Algorithms and Library Science. He is the principal investigator of the Discovery Science Project sponsored by the Grant-in Aid for Scientific Research on Priority Area from the Ministry of ESSC, Japan.     

Transformation rules for CNOT-based quantum circuits and their applications

This paper introduces a simple but nontrivial set of local transformation rules for designingControl-NOT(CNOT)-based combinatorial circuits. We also provide a proof that the rule set iscomplete, namely, for any two equivalent circuits,S ₁ andS ₂, there is a sequence of transformations, each of them in the rule set, which changesS ₁ toS ₂. Two applications of the rule set are also presented. One is to simulate Resolution with only polynomial overhead by the rule set. Therefore we can conclude that the rule set is reasonably powerful. The other is to reduce the cost of CNOT-based circuits by using the transformations in the rule set. This implies that the rule set might be used for the practical circuit design. Currently Graduate School of Information Science, Nara Institute of Science and Technology Kazuo Iwama, Ph.D.: Professor of Informatics, Kyoto University, Kyoto 606-8501, Japan. Received BE, ME, and Ph.D. degrees in Electrical Engineering from Kyoto University in 1978, 1980 and 1985, respectively. His research interests include algorithms, complexity theory and quantum computation. Editorial board of Information Processing Letters and Parallel Computing. Council Member of European Association for Theoretical Computer Science (EATCS). Shigeru Yamashita, Ph.D.: Associate Professor of Graduate School of Information Science, Nara Instutute of Science and Technology, Nara 630-0192, Japan. He received his B.E., M.E. and Ph.D. degrees in information science from Kyoto University, Kyoto, Japan, in 1993, 1995 and 2001, respectively. His research interests include new type of computer architectures and quantum computation. He received the 2000 IEEE Circuits and Systems Society Transactions on Computer-Aided Design of Integrated Circuits and Systems Best Paper Award.     

A Fuzzy set based approach to generalized landscape theory of aggregation

In this paper, we firstly reformulate the landscape theory of aggregation (Axelrod and Bennett, 1993) in terms of an optimization problem, and then straightforwardly propose a fuzzy-set-theoretic based extension for it. To illustrate efficiency of the proposal, we make a simulation with the proposed framework for the international alignment of the Second World War in Europe. It is shown that the obtained results are essentially comparable to those given by the original theory. Consequently, the fuzzy-set-theoretic based extension of landscape theory can allow us to analyze a wide variety of aggregation processes in politics, economics, and society in a more flexible manner. Shigemasa Suganuma: He received the M.S. degree in knowledge science from Japan Advanced Institute of Science and Technology,, Ishikawa, Japan in 2000. He currently takes a doctor's course in School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST). His research interest includes agent based simulation and its application to social and political concerns, industry and environmental behavior. Van-Nam Huynh, Ph.D.: He received the B.S. in Mathematics (1990) and Ph.D. (1999) from University of Quinhon, Vietnam and Institute of Information Technology, Vietnam Academy of Science and Technology, respectively. From April 2001 to March 2002, he was a postdoctoral fellow awarded by INOUE Foundation for Science at JAIST. He is currently a Research Associate in School of Knowledge Science, JAIST, Japan. His current research interests include fuzzy logic and approximate reasoning, uncertainty formalisms in knowledge-based systems, decision making. Yoshiteru Nakamori, Ph.D.: He received the B.S., M.S., and Ph.D. degrees all in applied mathematics and physics from Kyoto University, Kyoto, Japan. He is currently a Professor in School of Knowledge Science, JAIST. His research interests include development of modeling methodology based on hard as well as soft data, and support systems for soft thinking around hard data. Shouyang Wang, Ph.D.: He received the Ph.D. degree in Operations Research from Chinsese Academy of Sciences (CAS), Beijing in 1986. He is currently a Bairen distinguished professor of Management Science at Academy of Mathematics and Systems Sciences of CAS and a Lotus chair professor of Hunan University in Changsha. He is the editor-in-chief or a co-editor of 12 journals. He has published 120 journal articles. His current research interest includes decision analysis, system engineering and knowledge management.     

Temporal disjunctive logic programming

In this paper we introduce the logic programming languageDisjunctive Chronolog which combines the programming paradigms of temporal and disjunctive logic programming. Disjunctive Chronolog is capable of expressing dynamic behaviour as well as uncertainty, two notions that are very common in a variety of real systems. We present the minimal temporal model semantics and the fixpoint semantics for the new programming language and demonstrate their equivalence. We also show how proof procedures developed for disjunctive logic programs can be easily extended to apply to Disjunctive Chronolog programs. Manolis Gergatsoulis, Ph.D.: He received his B.Sc. in Physics in 1983, the M.Sc. and the Ph.D. degrees in Computer Science in 1986 and 1995 respectively all from the University of Athens, Greece. Since 1996 he is a Research Associate in the Institute of Informatics and Telecommunications, NCSR ‘Demokritos’, Athens. His research interests include logic and temporal programming, program transformations and synthesis, as well as theory of programming languages. Panagiotis Rondogiannis, Ph.D.: He received his B.Sc. from the Department of Computer Engineering and Informatics, University of Patras, Greece, in 1989, and his M.Sc. and Ph.D. from the Department of Computer Science, University of Victoria, Canada, in 1991 and 1994 respectively. From 1995 to 1996 he served in the Greek army. From 1996 to 1997 he was a visiting professor in the Department of Computer Science, University of Ioannina, Greece, and since 1997 he is a Lecturer in the same Department. In January 2000 he was elected Assistant Professor in the Department of Informatics at the University of Athens. His research interests include functional, logic and temporal programming, as well as theory of programming languages. Themis Panayiotopoulos, Ph.D.: He received his Diploma on Electrical Engineering from the Department of Electrical Engineering, National Technical Univesity of Athens, in 1984, and his Ph.D. on Artificial Intelligence from the above mentioned department in 1989. From 1991 to 1994 he was a visiting professor at the Department of Mathematics, University of the Aegean, Samos, Greece and a Research Associate at the Institute of Informatics and Telecommunications of “Democritos” National Research Center. Since 1995 he is an Assistant Prof. at the Department of Computer Science, University of Piraeus. His research interests include temporal programming, logic programming, expert systems and intelligent agent architectures.     

Cautious induction: An alternative to clause-at-a-time hypothesis construction in inductive logic programming

Hypotheses constructed by inductive logic programming (ILP) systems are finite sets of definite clauses. Top-down ILP systems usually adopt the following greedy clause-at-a-time strategy to construct such a hypothesis: start with the empty set of clauses and repeatedly add the clause that most improves the quality of the set. This paper formulates and analyses an alternative method for constructing hypotheses. The method, calledcautious induction, consists of a first stage, which finds a finite set of candidate clauses, and a second stage, which selects a finite subset of these clauses to form a hypothesis. By using a less greedy method in the second stage, cautious induction can find hypotheses of higher quality than can be found with a clause-at-a-time algorithm. We have implemented a top-down, cautious ILP system called CILS. This paper presents CILS and compares it to Progol, a top-down clause-at-a-time ILP system. The sizes of the search spaces confronted by the two systems are analysed and an experiment examines their performance on a series of mutagenesis learning problems. Simon Anthony, BEng.: Simon, perhaps better known as “Mr. Cautious” in Inductive Logic Programming (ILP) circles, completed a BEng in Information Engineering at the University of York in 1995. He remained at York as a research student in the Intelligent Systems Group. Concentrating on ILP, his research interests are Cautious Induction and developing number handling techniques using Constraint Logic Programming. Alan M. Frisch, Ph.D.: He is the Reader in Intelligent Systems at the University of York (UK), and he heads the Intelligent Systems Group in the Department of Computer Science. He was awarded a Ph. D. in Computer Science from the University of Rochester (USA) in 1986 and has held faculty positions at the University of Sussex (UK) and the University of Illinois at Urbana-Champaign (USA). For over 15 years Dr. Frisch has been conducting research on a wide range of topics in the area of automated reasoning, including knowledge retrieval, probabilistic inference, constraint solving, parsing as deduction, inductive logic programming and the integration of constraint solvers into automated deduction systems.     

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.     

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.     

Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7]. Shi-Jay Chen was born in 1972, in Taipei, Taiwan, Republic of China. He received the B.S. degree in information management from the Kaohsiung Polytechnic Institute, Kaohsiung, Taiwan, and the M.S. degree in information management from the Chaoyang University of Technology, Taichung, Taiwan, in 1997 and 1999, respectively. He received the Ph.D. degree at the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, in October 2004. His research interests include fuzzy systems, multicriteria fuzzy decisionmaking, and artificial intelligence. Shyi-Ming Chen was born on January 16, 1960, in Taipei, Taiwan, Republic of China. He received the Ph.D. degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in June 1991. From August 1987 to July 1989 and from August 1990 to July 1991, he was with the Department of Electronic Engineering, Fu-Jen University, Taipei, Taiwan. From August 1991 to July 1996, he was an Associate Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1996 to July 1998, he was a Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1998 to July 2001, he was a Professor in the Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. Since August 2001, he has been a Professor in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He was a Visiting Scholar in the Department of Electrical Engineering and Computer Science, University of California, Berkeley, in 1999. He was a Visiting Scholar in the Institute of Information Science, Academia Sinica, Republic of China, in 2003. He has published more than 250 papers in referred journals, conference proceedings and book chapters. His research interests include fuzzy systems, information retrieval, knowledge-based systems, artificial intelligence, neural networks, data mining, and genetic algorithms. Dr. Chen has received several honors and awards, including the 1994 Outstanding Paper Award o f the Journal of Information and Education, the 1995 Outstanding Paper Award of the Computer Society of the Republic of China, the 1995 and 1996 Acer Dragon Thesis Awards for Outstanding M.S. Thesis Supervision, the 1995 Xerox Foundation Award for Outstanding M.S. Thesis Supervision, the 1996 Chinese Institute of Electrical Engineering Award for Outstanding M.S. Thesis Supervision, the 1997 National Science Council Award, Republic of China, for Outstanding Undergraduate Student's Project Supervision, the 1997 Outstanding Youth Electrical Engineer Award of the Chinese Institute of Electrical Engineering, Republic of China, the Best Paper Award of the 1999 National Computer Symposium, Republic of China, the 1999 Outstanding Paper Award of the Computer Society of the Republic of China, the 2001 Institute of Information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the 2001 Outstanding Talented Person Award, Republic of China, for the contributions in Information Technology, the 2002 Institute of information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the Outstanding Electrical Engineering Professor Award granted by the Chinese Institute of Electrical Engineering (CIEE), Republic of China, the 2002 Chinese Fuzzy Systems Association Best Thesis Award for Outstanding M.S. Thesis Supervision, the 2003 Outstanding Paper Award of the Technological and Vocational Education Society, Republic of China, the 2003 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision, the 2005 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 Taiwan Fuzzy Systems Association Award for Outstanding Ph.D. Dissertation Supervision, and the 2006 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision. Dr. Chen is currently the President of the Taiwanese Association for Artificial Intelligence (TAAI). He is a Senior Member of the IEEE, a member of the ACM, the International Fuzzy Systems Association (IFSA), and the Phi Tau Phi Scholastic Honor Society. He was an administrative committee member of the Chinese Fuzzy Systems Association (CFSA) from 1998 to 2004. He is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics - Part C, an Associate Editor of the IEEE Computational Intelligence Magazine, an Associate Editor of the Journal of Intelligent & Fuzzy Systems, an Editorial Board Member of the International Journal of Applied Intelligence, an Editor of the New Mathematics and Natural Computation Journal, an Associate Editor of the International Journal of Fuzzy Systems, an Editorial Board Member of the International Journal of Information and Communication Technology, an Editorial Board Member of the WSEAS Transactions on Systems, an Editor of the Journal of Advanced Computational Intelligence and Intelligent Informatics, an Associate Editor of the WSEAS Transactions on Computers, an Editorial Board Member of the International Journal of Computational Intelligence and Applications, an Editorial Board Member of the Advances in Fuzzy Sets and Systems Journal, an Editor of the International Journal of Soft Computing, an Editor of the Asian Journal of Information Technology, an Editorial Board Member of the International Journal of Intelligence Systems Technologies and Applications, an Editor of the Asian Journal of Information Management, an Associate Editor of the International Journal of Innovative Computing, Information and Control, and an Editorial Board Member of the International Journal of Computer Applications in Technology. He was an Editor of the Journal of the Chinese Grey System Association from 1998 to 2003. He is listed in International Who's Who of Professionals, Marquis Who's Who in the World, and Marquis Who's Who in Science and Engineering.     

Constructive negation and constraint logic programming with sets

The aim of this paper is to extend theConstructive Negation technique to the case ofCLP(SεT), a Constraint Logic Programming (CLP) language based on hereditarily (and hybrid) finite sets. The challenging aspects of the problem originate from the fact that the structure on whichCLP(SεT) is based is notadmissible closed, and this does not allow to reuse the results presented in the literature concerning the relationships betweenCLP and constructive negation. We propose a new constraint satisfaction algorithm, capable of correctly handling constructive negation for large classes ofCLP(SεT) programs; we also provide a syntactic characterization of such classes of programs. The resulting algorithm provides a novel constraint simplification procedure to handle constructive negation, suitable to theories where unification admits multiple most general unifiers. We also show, using a general result, that it is impossible to construct an interpreter forCLP(SεT) with constructive negation which is guaranteed to work for any arbitrary program; we identify classes of programs for which the implementation of the constructive negation technique is feasible. Agostino Dovier, Ph.D.: He is a researcher in the Department of Science and Technology at the University of Verona, Italy. He obtained his master degree in Computer Science from the University of Udine, Italy, in 1991 and his Ph.D. in Computer Science from the University of Pisa, Italy, in 1996. His research interests are in Programming Languages and Constraints over complex domains, such as Sets and Multisets. He has published over 20 research papers in International Journals and Conferences. He is teaching a course entitled “Special Languages and Techniques for Programming” at the University of Verona. Enrico Pontelli, Ph.D.: He is an Assistant Professor in the Department of Computer Science at the New Mexico State University. He obtained his Laurea degree from the University of Udine (Italy) in 1991, his Master degree from the University of Houston in 1992, and his Ph.D. degree from New Mexico State University in 1997. His research interests are in Programming Languages, Parallel Processing, and Constraint Programming. He has published over 50 papers and served on the program committees of different conferences. He is presently the Associate Director of the Laboratory for Logic, Databases, and Advanced Programming. Gianfranco Rossi, Ph.D.: He received his degree in Computer Science from the University of Pisa in 1979. From 1981 to 1983 he was employed at Intecs Co. System House in Pisa. From November 1983 to February 1989 he was a researcher at the Dipartimento di Informatica of the University of Turin. Since March 1989 he is an Associate Professor of Computer Science, currently with the University of Parma. He is the author of several papers dealing mainly with programming languages, in particular logic programming languages and Prolog, and extended unification algorithms. His current research interests are (logic) programming languages with sets and set unification algorithms.     

A study on effects of market systems in an artificial market

In an artificial market approach with multi-agent systems, the static equilibrium concept is often used in market systems to approximate continuous market auctions. However, differences between the static equilibrium concept and continuous auctions have not been discussed in the context of an artificial market study. In this paper, we construct an artificial market model with both of them, namely, the Itayose and Zaraba method, and show simple characteristic differences between these methods based on computer simulations. The result indicates the further need to model the market system by studying artificial markets. Hidenori Kawamura, Ph.D.: He received Ph.D. degree from Division of Systems and Information Engineering, Graduate School of Engineering, Hokkaido University, Japan in 2000. He is currently an instructor in Graduate School of Information Science and Technology, Hokkaido University, Japan. His research interests include multiagent systems, mass user support, artificial intelligence, complex systems, and tourism informatics. He is a member of IPSJ, JSAI, IEICE, ORSJ, JSTI and AAAI. Yasushi Okada, Ph.D.: He is a master course student in Graduate School of Engineering, Hokkaido University, Japan. He studies multiagent systems. Azuma Ohuchi, Ph.D.: He received his Ph.D. degree in 1974 from Hokkaido University. He is currently the professor in Graduate School of Information Science and Technology, Hokkaido University Japan. His research interstes include systems information engineering, artificial intelligence, complex systems, tourism informatics and medical systems. He is a member of the IPSJ, JSAI, IEEJ, ORSJ, Soc. Contr. Eng., Jap. OR Soc., Soc. Med. Informatics, Hosp. Manag., JSTI and IEEE-SMC. Koichi Kurumatani, Ph.D.: He received his Ph.D. Degree in 1989 from The University of Tokyo. He is currently a leader of Multiagent Research Team in Cyber Assist Research Center (CARC), National Institute of Advanced Industrial Science and Technology (AIST), Japan. His research interests include multiagent systems and mass user support. He is a member of JSAI, IPSJ, JSTI and AAAI.     

GigaE PM: a high performance communication facility using a Gigabit Ethernet

A high performance communication facility, called theGigaE PM, has been designed and implemented for parallel applications on clusters of computers using a Gigabit Ethernet. The GigaE PM provides not only a reliable high bandwidth and low latency communication, but also supports existing network protocols such as TCP/IP. A reliable communication mechanism for a parallel application is implemented on the firmware on a NIC while existing network protocols are handled by an operating system kernel. A prototype system has been implemented using an Essential Communications Gigabit Ethernet card. The performance results show that a 58.3 μs round trip time for a four byte user message, and 56.7 MBytes/sec bandwidth for a 1,468 byte message have been achieved on Intel Pentium II 400 MHz PCs. We have implemented MPICH-PM on top of the GigaE PM, and evaluated the NAS parallel benchmark performance. The results show that the IS class S performance on the GigaE PM is 1.8 times faster than that on TCP/IP. Shinji Sumimoto: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. He received BS degree in electrical engineering from Doshisha University. His research interest include parallel and distributed systems, real-time systems, and high performance communication facilities. He is a member of Information Processing Society of Japan. Hiroshi Tezuka: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. His research interests include real-time systems and operating system kernel. He is a member of the Information Processing Society of Japan, and Japan Society for Software Science and Technology. Atsushi Hori, Ph.D.: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. His current research interests include parallel operating system. He received B.S. and M.S. degrees in Electrical Engineering from Waseda University, and received Ph.D. from the University of Tokyo. He worked as a researcher in Mitsubishi Research Institute from 1981 to 1992. Hiroshi Harada: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. His research interests include distributed/parallel systems and distributed shared memory. He received BS degree in physics from Science University of Tokyo. He is a member of ACM and Information Processing Society of Japan. Toshiyuki Takahashi: He is a Researcher at Real World Computing Partnership since 1998. He received his B.S. and M.S. from the Department of Information Sciences of Science University of Tokyo in 1993 and 1995. He was a student of the Information Science Department of the University of Tokyo from 1995 to 1998. His current interests are in meta-level architecture for programming languages and high-performance software technologies. He is a member of Information Processing Society of Japan. Yutaka Ishikawa, Ph.D.: He is the chief of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. He is currently temporary retirement from Electrotechnical Laboratory, MITI. His research interests include distributed/parallel systems, object-oriented programming languages, and real-time systems. He received the B.S., M.S. and Ph.D degrees in electrical engineering from Keio University. He is a member of the IEEE Computer Society, ACM, Information Processing Society of Japan, and Japan Society for Software Science and Technology.     

Blackboard relations in the μLog coordination model

This paper describescoordination relations, that are relations that induce the presence or absence of data on some dataspaces from the presence or absence of other data on other dataspaces. To that end we build upon previous work on the μLog model and show that the coordination relations can be easily incorporated in it. This is achieved, on the one hand, by means of novel auxiliary operations, not classically used in Linda-like languages, and, on the other hand, by a translation technique reducing the extended μLog model to the core model augmented with the auxiliary operations. Among the most significant ones are multiple read and get operations on a blackboard, readall and getall operations, and tests for the absence of data on blackboards. Although simple, the form of coordination relations we propose is quite powerful as evidenced by a few examples including relations coming from the object-oriented paradigm such as inheritance relations. Jean-Marie Jacquet, Ph.D.: He is Professor at the Institute of Informatics at the University of Namur, Belgium, and, at an honorary title, Research Associate of the Belgian National Fund for Scientific Research. He obtained a Master in Mathematics from the University of Liège in 1982, a Master in Computer Science from the University of Namur in 1984 and a Ph.D. in Computer Science from the University of Namur in 1989. His research interest are in Programming Languages and Coordination models. He has served as a reviewer and program committee member of several conferences. Koen de Bosschere, Ph.D.: He holds the degree of master of Science in Engineering of the Ghent University, and a Ph.D. from the same University. He is currently research associate with the Fund for Scientific Research — Flanders and senior lecturer at the Ghent University, where he teaches courses on computer architecture, operating systems and declarative programming languages. His research interests are coordination in parallel logic programming, computer architecture and systems software.     

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.     

Wiener estimation method in estimating of spectral reflectance from RGB images

Color is one of the most important features in digital images. The representation of color in digital form with a three-component image (RGB) is not very accurate, hence the use of a multiple-component spectral image is justified. At the moment, acquiring a spectral image is not as easy and as fast as acquiring a conventional three-component image. One answer to this problem is to use a regular digital RGB camera and estimate its RGB image into a spectral image by the Wiener estimation method, which is based on the use of a priori knowledge. In this paper, the Wiener estimation method is used to estimate the spectra of icons. The experimental results of the spectral estimation are presented. The text was submitted by the authors in English. Pekka Tapani Stigell. Year of birth 1976. Year of graduation and name of institution: Last year undergraduate student in the Department of Computer Science in the University of Joensuu, Finland. Affiliation: InFotoics Center, Department of Computer Science, University of Joensuu. Position: Trainee. Area of research: Color research. Number of publications: 1. Membership to scientific societies: Pattern Recognition Society of Finland, member-society of IAPR (International Association for Pattern Recognition). Prizes for achievements in research or applications: The best young scientist award in PRIA-7-2004 (shared with two other scientists). Kimiyoshi Miyata. Year of birth: 1966. Year of graduation and name of institution: 2000. Graduate School of Science and Technology, Chiba University, Japan. Year of graduation: 1990, BE degree (Chiba University), 1992, ME degree (Chiba University), 2000, Ph.D degree (Chiba University). Affiliation: Museum Science Division, Research Department, National Museum of Japanese History. Position: Assistant Professor. Area of research: Improvement of image quality, color management, application of imaging science and technology to museum activities. Number of publications: 11. Membership to scientific societies: Society of Photographic Science and Technology of Japan, Optical Society of Japan, Institute of Image Electronics Engineers of Japan, Society for Imaging Science and Technology. Prizes for achievements in research or applications: Progressing Award from Society of Photographic Science and Technology of Japan in 2000, Itek Award from Society for Imaging Science and Technology in 2000. Markku Hauta-Kasari. Year of birth: 1970. Graduation and name of the institution: University of Technology, Lappeenranta, Finland. Year of graduation: 1999, Ph.D. degree (University of Technology, Lappeenranta). Affiliation: InFotonics Center, Department of Computer Science, University of Joensuu. Position: Director. Area of research: Color research, neural computation, pattern recognition, optical pattern recognition, computer vision, image processing. Number of publications: Articles in refereed international scientific journals: 5, Articles in refereed international scientific conferences: 9, Other Scientific Publications: 40. Membership to academies: Chairman of the Pattern Recognition Society of Finland May 2003. Membership to scientific societies: Pattern Recognition Society of Finland, member-society of IAPR (International Association for Pattern Recognition), Finnish Information Processing Association, Finnish Union of University Researchers and Teachers, Optical Society of Japan, Optical Society of America. Prizes for achievements in research or applications: The best Ph.D.-thesis award in the field of pattern recognition in 1998–1999 in Finland. Award was issued by the Pattern Recognition Society of Finland on April 25, 2000.     

A-SATCHMORE: SATCHMORE with availability checking

We present an improvement of SATCHMORE, calledA-SATCHMORE, by incorporating availability checking into relevancy. Because some atoms unavailable to the further computation are also marked relevant, SATCHMORE suffers from a potential explosion of the search space. Addressing this weakness of SATCHMORE, we show that an atom does not need to be marked relevant unless it is available to the further computation and no non-Horn clause needs to be selected unless all its consequent atoms are marked availably relevant, i.e., unless it is totally availably relevant. In this way,A-SATCHMORE is able to further restrict the ues of non-Horn clauses (therefore to reduce the search space) and makes the proof more goal-oriented. Our theorem prover,A-SATCHMORE, can be simply implemented in PROLOG based on SATCHMORE. We discuss how to incorporate availability cheeking into relevancy, describe our improvement and present the implementation. We also prove that our theorem prover is sound and complete, and provide examples to show the power of our availability approach. This research is supported in part by the Japanese Ministry of Education and the Artificial Intelligence Research Promotion Foundation. Lifeng He, Ph.D: He received the B. E. degree from Northwest Institute of Light Industry, China, in 1982, the M. S. and Ph.D. degrees in AI and computer science from Nagoya Institute of Technology, Japan, in 1994 and 1997, respectively. He currently works at the Institute of Open System in Nagoya, Japan. His research interests include automated reasoning, theorem proving, logic programming, knowledge bases, multi-agent cooperation and modal logic. Yuyan Chao, M. S.: She received the B. E. degree from Northwest Institute of Light Industry, China, in 1984, and the M. S. degree from Nagoya University, Japan, in 1997. She is currently a doctoral candidate in the Department of Human Information, Nagoya University. Her research interests include image processing, graphic understanding, CAD and theorem proving. Yuka Shimajiri, M. S.: She currently works as a Assistant Professor in Department of Artificial Intelligence and Computer Science at the Nagoya Institute of Technology. She received her B.Eng. and M.Eng. from the Nagoya Institute of Technology in 1994 and 1996, respectively. Her current research interests include logic programming and automated deduction. She is a member of IPSJ and JSAI. Hirohisa Seki, Ph.D.: He received the B. E., M. E. and Ph.D degrees from the University of Tokyo in 1979, 1981 and 1991 respectively. He joined the Central Research Laboratory of Mitsubishi Electric Corporation in 1981. From 1985 to 1989, he was with the Institute for New Generation Computer Technology (ICOT). Since 1992, he has been an Associate Professor in the Department of AI and Computer Science at Nagoya Institute of Technology. His current research interests include logic programming, deductive databases and automated deduction. He is a member of ACM, IEEE, IPSJ and JSAI. Hidenori Itoh, Ph.D.: He received the B. S. degree from Fukui University, in 1969, the M. S. degree and Ph.D degree from Nagoya University, Japan, in 1971 and 1974, respectively. From 1974 to 1985, he worked at Nippon Telephone and Telegraph Laboratories, developing operating systems. From 1985 to 1989, he was with the Institute for New Generation Computer Technology, developing knowledge base systems. Since 1989, he has become a professor at the Nagoya Institute of Technology. His current research interests include image processing, parallel computing, fuzzy logic and knowledge processing.     

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