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.     

Aggregation and negation-as-failure

Set-grouping and aggregation are powerful operations of practical interest in database query languages. An aggregate operation is a function that maps a set to some value, e.g., the maximum or minimum in the set, the cardinality of this set, the summation of all its members, etc. Since aggregate operations are typically non-monotonic in nature, recursive programs making use of aggregate operations must be suitably restricted in order that they have a well-defined meaning. In a recent paper we showed that partial-order clauses provide a well-structured means of formulating aggregate operations with recursion. In this paper, we consider the problem of expressing partial-order programs via negation-as-failure (NF), a well-known non-monotonic operation in logic programming. We show a natural translation of partial-order programs to normal logic programs: Anycost-monotonic partial-order programsP is translated to astratified normal program such that the declarative semantics ofP is defined as the stratified semantics of the translated program. The ability to effect such a translation is significant because the resulting normal programs do not make any explicit use of theaggregation capability, yet they are concise and intuitive. The success of this translation is due to the fact that the translated program is a stratified normal program. That would not be the case for other more general classes of programs thancost-monotonic partial-order programs. We therefore develop in stages a refined translation scheme that does not require the translated programs to be stratified, but requires the use of a suitable semantics. The class of normal programs originating from this refined translation scheme is itself interesting: Every program in this class has a clear intended total model, although these programs are in general neither stratified nor call-consistent, and do not have a stable model. The partial model given by the well-founded semantics is consistent with the intended total model and the extended well founded semantics,WFS ⁺, defines the intended model. Since there is a well-defined and efficient operational semantics for partial-order programs^{14, 15, 21)} we conclude that the gap between expression of a problem and computing its solution can be reduced with the right level of notation. Mauricio J. Osorio G., Ph.D.: He is an Associate Professor in the Department of Computer Systems Engineering, University of the Americas, Puebla, Mexico. He is the Head of the Laboratory of Theoretical Computer Science of the Center of Research (CENTIA), Puebla, Mexico. His research is currently funded by CENTIA and CONACYT (Ref. #C065-E9605). He is interested in Applications of Logic to Computer Science, with special emphasis on Logic Programming. He received his B.Sc. in Computer Science from the Universidad Autonoma de Puebla, his M.Sc. in Electrical Engineering from CINVESTAV in Mexico, and his Ph.D. from the State University of New York at Buffalo in 1995. Bharat Jayaraman, Ph.D.: He is an Associate Professor in the Department of Computer Science at the State University of New York at Buffalo. He obtained his bachelors degree in Electronics from the Indian Institute of Technology, Madras in 1975, and his Ph.D. from the University of Utah in 1981. His research interests are in Programming Languages and Declarative Modeling of Complex Systems. He has published over 50 research papers. He has served on the program committees of several conferences in the area of Programming Languages, and he is presently on the Editorial Board of the Journal of Functional and Logic Programming.     

A program specialiser for meta-level compositions of logic programs

Metal-level compositions of object logic programs are naturally implemented by means of meta-programming techniques. Metainterpreters defining program compositions however suffer from a computational overhead that is due partly to the interpretation layer present in all meta-programs, and partly to the specific interpretation layer needed to deal with program compositions. We show that meta-interpreters implementing compositions of object programs can be fruitfully specialised w.r.t. meta-level queries of the form Demo (E, G), where E denotes a program expression and G denotes a (partially instantiated) object level query. More precisely, we describe the design and implementation of declarative program specialiser that suitably transforms such meta-interpreters so as to sensibly reduce — if not to completely remove — the overhead due to the handling of program compositions. In many cases the specialiser succeeds in eliminating also the overhead due to meta-interpretation. Antonio Brogi, Ph.D.: He is currently assistant professor in the Department of Computer Science at the University of Pisa, Italy. He received his Laurea Degree in Computer Science (1987) and his Ph. D. in Computer Science (1993) from the University of Pisa. His research interests include programming language design and semantics, logic programming, deductive databases, and software coordination. Simone Contiero: He is currently a Ph. D. student at the Department of Computer Science, University of Pisa (Italy). He received his Laurea Degree in Computer Science from the University of Pisa in 1994. His research interests are in high-level programming languages, metaprogramming and logic-based coordination of software.     

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.     

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.     

A unifying approach to goal-directed evaluation

Goal-directed evaluation, as embodied in Icon and Snobol, is built on the notions of backtracking and of generating successive results, and therefore it has always been something of a challenge to specify and implement. In this article, we address this challenge using computational monads and partial evaluation. We consider a subset of Icon and we specify it with a monadic semantics and a list monad. We then consider a spectrum of monads that also fit the bill, and we relate them to each other. For example, we derive a continuation monad as a Church encoding of the list monad. The resulting semantics coincides with Gudeman’s continuation semantics of Icon. We then compile Icon programs by specializing their interpreter (i.e., by using the first Futamura projection), using type-directed partial evaluation. Through various back ends, including a run-time code generator, we generate ML code, C code, and OCaml byte code. Binding-time analysis and partial evaluation of the continuation-based interpreter automatically give rise to C programs that coincide with the result of Proebsting’s optimized compiler. Basic Research in Computer Science (www.brics. dk), funded by the Danish National Research Foundation. Olivier Danvy, Ph.D., Habilitation: He is an Associate Professor at the Department of Computer Science at the University of Aarhus, in Denmark. He obtained his Ph.D. degree in 1986 and his Habilitation in 1993 from the Université Pierre et Marie Curie (Paris VI), France. His research interests are in Programming Languages in general and in Partial Evaluation and Continuations in particular. He has published over 75 refereed research papers and edited several proceedings. He has both served on and chaired program committees of scientific meetings in the area of Programming Languages. He is presently chairing the PEPM steering committee at ACM SIGPLAN and serving as external reviewer in computer science for the Danish Universities, as board member in the BRICS PhD School, and as co-Editor-in-Chief of the journal Higher-Order and Symbolic Computation (http://www.wkap.nl/journals/hosc). Bernd Grobauer, M.Sc.: He is a Ph.D. student at the BRICS International Ph.D. school, University of Aarhus, Denmark, and will graduate in the summer of 2001. He obtained his Masters degree from the Munich University of Technology (TUM), Germany. His research interests are in formal methods (especially theorem proving) and programming languages (semantics of programming languages, program analysis, program transformation, types). He serves as editorial assistant for the journal Higher-Order and Symbolic Computation and as chairman of the BRICS Juniorklubben. Morten Rhiger, M.Sc.: He is a Ph.D. student at the BRICS International Ph.D. school, University of Aarhus, Denmark, and will graduate in the summer of 2001. He obtained his Masters degree from the University of Aarhus in 1998. His research interests are in the semantics and implementation of programming languages.     

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.     

A Prolog simulator for deterministic P systems with active membranes

In this paper we propose a new way to represent P systems with active membranes based on Logic Programming techniques. This representation allows us to express the set of rules and the configuration of the P system in each step of the evolution as literals of an appropriate language of first order logic. We provide a Prolog program to simulate, the evolution of these P systems and present some auxiliary tools to simulate the evolution of a P system with active membranes using 2-division which solves the SAT problem following the techniques presented in Reference.¹⁰ Andrés Cordón-Franco: He is a member of the Department of Computer Science and Artificial Intelligence at the University of Sevilla (Spain). He is also a member of the research group on Natural Computing of the University of Seville. His research interest includes Mathematical Logic, Logic in Computer Science, and Membrane Computing, both from a theoretical and from a practical (software implementation) point of view. Miguel A. Gutiérrez-Naranjo: He is an assistant professor in the Computer Science and Artificial Intelligence Department at University of Sevilla, Spain. He is also a member of the Research Group on Natural Computing of the University of Seville. His research interest includes Machine Learning, Logic Programming and Membrane Computing, both from a theoretical and a practical point of view. Mario J. Pérez-Jiménez, Ph.D.: He is professor of Department of Computer Science and Artificial Intelligence at University of Seville, where he is the head of the Group of Research on Natural Computing, He has published 8 books of Mathematics and Computation, and more than 90 scientific articles in prestigious scientific journals. He is member of European Molecular Computing Consortium. Fernando Sancho-Caparrini: He is a member of the Department of Computer Science and Artificial Intelligence at the University of Sevilla (Spain). He is also a member of the research group on Natural Computing of the University of Seville. His research interest includes Complex Systems, DNA Computing, Logic in Computer Science, and Membrane Computing, both from a theoretical and from a practical point of view.     

A portable approach to dynamic optimization in run-time specialization

This paper proposes arun-time bytecode specialization (BCS) technique that analyzes programs and generates specialized programs at run-time in an intermediate language. By using an intermediate language for code generation, a back-end system canoptimize the specialized programs after specialization. The system uses Java virtual machine language (JVML) as the intermediate language, which allows the system to easily achieve practicalportability and to use existing sophisticated just-in-time (JIT) compilers as its back-end. The binding-time analysis algorithm is based on a type system, and covers a non-object-oriented subset of JVML. The specializer generates programs on a per-instruction basis, and can performmethod inlining at run-time. Our performance measurements show that a non-trivial application program specialized at run-time by BCS runs approximately 3–4 times faster than the unspecialized one. Despite the large overhead of JIT compilation of specialized code, we observed that the overall performance of the application can be improved. This paper is an extended version of “A Portable Approach to Generating Optimized Specialized Code”, inProceedings of Second Symposium on Programs as Data Objects (PADO-II), Lecture Notes in Computer Science, vol. 2053, pp. 138–154, Aarhus, Denmark, May 2001.²³) Hidehiko Masuhara, D.Sc.: He is an Assistant Professor at Department of Graphics and Computer Science, Graduate School of Arts and Sciences, University of Tokyo. He received his B.S., M.S. and D.Sc. degrees from Department of Information Science, University of Tokyo in 1992, 1994, and 1999, respectively. His research interests are in programming languages, especially in mechanisms to support flexible and efficient computation such as dynamic optimization and reflection. He received the best-paper award from Information Processing Society of Japan in 1996. Akinori Yonezawa, Ph.D.: He is a Professor of computer science at Department of Computer Science, University of Tokyo. He received Ph.D. in Computer Science from the Massachusetts Institute of Technology in 1977. His current major research interests are in the areas of concurrent/parallel computation models, programming languages, object-oriented computing, and distributed computing. He is the designer of an object-oriented concurrent language ABCL/1 and the editor of several books and served as an associate editor of ACM Transaction of Programming Languages and Systems (TOPLAS). Since 1998, he has been an ACM Fellow.     

The narrowing-driven approach to functional logic program specialization

Partial evaluation is a semantics-based program optimization technique which has been investigated within different programming paradigms and applied to a wide variety of languages. Recently, a partial evaluation framework for functional logic programs has been proposed. In this framework, narrowing—the standard operational semantics of integrated languages—is used to drive the partial evaluation process. This paper surveys the essentials of narrowing-driven partial evaluation. Elvira Albert, Ph.D.: She is an associate professor in Computer Science at the Technical University of Valencia, Spain. She received her bachelors degree in computer science in 1998 and her Ph.D. in computer science in 2001, both from the Technical University of Valencia. She has investigated on program optimization and on partial evaluation for declarative multi-paradigm programming languages. Her current research interests include term rewriting, multi-paradigm declarative programming, and formal methods, in particular semantics-based program analysis, transformation, specification, verification, and debugging. Germán Vidal, Ph.D.: He is an associate professor in Computer Science at the Technical University of Valencia, Spain. He obtained his bachelors degree in computer science in 1992 and his Ph.D. in computer science in 1996, both from the Technical University of Valencia. He is active on several research topics in Functional Logic Programming. He has worked on compositionality, on abstract interpretation, and on program transformation techniques for functional logic programs. Currently, his research interests include declarative multi-paradigm programming languages, term rewriting, and semantics-based program manipulation, in particular partial evaluation.     

Learning Horn definitions: Theory and an application to planning

A Horn definition is a set of Horn clauses with the same predicate in all head literals. In this paper, we consider learning non-recursive, first-order Horn definitions from entailment. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Finally, we apply our results to learning control knowledge for efficient planning in the form of goal-decomposition rules. Chandra Reddy, Ph.D.: He is currently a doctoral student in the Department of Computer Science at Oregon State University. He is completing his Ph.D. on June 30, 1998. His dissertation is entitled “Learning Hierarchical Decomposition Rules for Planning: An Inductive Logic Programming Approach.” Earlier, he had an M. Tech in Artificial Intelligence and Robotics from University of Hyderabad, India, and an M.Sc.(tech) in Computer Science from Birla Institute of Technology and Science, India. His current research interests broadly fall under machine learning and planning/scheduling—more specifically, inductive logic programming, speedup learning, data mining, and hierarchical planning and optimization. Prasad Tadepalli, Ph.D.: He has an M.Tech in Computer Science from Indian Institute of Technology, Madras, India and a Ph.D. from Rutgers University, New Brunswick, USA. He joined Oregon State University, Corvallis, as an assistant professor in 1989. He is now an associate professor in the Department of Computer Science of Oregon State University. His main area of research is machine learning, including reinforcement learning, inductive logic programming, and computational learning theory, with applications to classification, planning, scheduling, manufacturing, and information retrieval.     

Static extensivity analysis for λ-definable functions over lattices

We employ a static analysis to examine the extensivity (∀x:x≤f(x)) of functions defined over lattices in a λ-calculus augmented with lattice operations. The need for such a verification procedure has arisen in our work on a generator system (called Zoo) of static program-analyzers. The input to Zoo is a static analysis specification that consists of lattice definitions and function definitions over the lattices. Once the extensivity of the functions is ascertained, the generated analyzer is guaranteed to terminate when the lattices have finite-heights. The extensivity analysis consists of a sound syntax-driven deductive rules whose satisfiability check is done by a constraint solving procedure. Hyunjun Eo: He is a Ph.D. candidate of Computer Science Dept. at KAIST (Korea Advanced Institute of Science and Technology). He received his B.S. and M.S. in Computer Science from KAIST in 1996 and 1998, respectively. For 1998–2003, he was a research assistant of the National Creative Research Initiative Center for Research On Program Analysis System. His research interest has been on static program analysis, program logics, and higher-order and typed languages. He is currently working on developing a tool for automatic generation of program analyzers. Kwangkeun Yi, Ph.D.: His research interest has been on semantic-based program analysis and systems application of language technologies. After his Ph.D. from University of Illinois at Urbana-Champaign he joined the Software Principles Research Department at Bell Laboratories, where he worked on various static analysis approaches for higher-order and typed programming languages. For 1995–2003, he was a faculty member in the Department of Computer Science, Korea Advanced Institute of Science and Technology. Since Fall 2003, he has been a faculty member in the School of Computer Science and Engineering, Seoul National University. Kwang-Moo Choe, Ph.D.: He is a professor of Computer Science at Korea Advanced Institute of Science and Technology. He received his B.S. from Seoul National University in 1976, and his M.S. and Ph.D. from Korea Advanced Institute of Science and Technology in 1978 and 1984, respectively. For 1985–1986, he was a technical staff of AT&T Bell Labs at Murray Hill. His research interest is formal language theory, parallel evaluation of logic programs, and optimizing compilers.     

S-Club: an overlay-based efficient service discovery mechanism in CROWN Grid

Information service plays a key role in grid system, handles resource discovery and management process. Employing existing information service architectures suffers from poor scalability, long search response time, and large traffic overhead. In this paper, we propose a service club mechanism, called S-Club, for efficient service discovery. In S-Club, an overlay based on existing Grid Information Service (GIS) mesh network of CROWN is built, so that GISs are organized as service clubs. Each club serves for a certain type of service while each GIS may join one or more clubs. S-Club is adopted in our CROWN Grid and the performance of S-Club is evaluated by comprehensive simulations. The results show that S-Club scheme significantly improves search performance and outperforms existing approaches. Chunming Hu is a research staff in the Institute of Advanced Computing Technology at the School of Computer Science and Engineering, Beihang University, Beijing, China. He received his B.E. and M.E. in Department of Computer Science and Engineering in Beihang University. He received the Ph.D. degree in School of Computer Science and Engineering of Beihang University, Beijing, China, 2005. His research interests include peer-to-peer and grid computing; distributed systems and software architectures. Yanmin Zhu is a Ph.D. candidate in the Department of Computer Science, Hong Kong University of Science and Technology. He received his B.S. degree in computer science from Xi’an Jiaotong University, Xi’an, China, in 2002. His research interests include grid computing, peer-to-peer networking, pervasive computing and sensor networks. He is a member of the IEEE and the IEEE Computer Society. Jinpeng Huai is a Professor and Vice President of Beihang University. He serves on the Steering Committee for Advanced Computing Technology Subject, the National High-Tech Program (863) as Chief Scientist. He is a member of the Consulting Committee of the Central Government’s Information Office, and Chairman of the Expert Committee in both the National e-Government Engineering Taskforce and the National e-Government Standard office. Dr. Huai and his colleagues are leading the key projects in e-Science of the National Science Foundation of China (NSFC) and Sino-UK. He has authored over 100 papers. His research interests include middleware, peer-to-peer (P2P), grid computing, trustworthiness and security. Yunhao Liu received his B.S. degree in Automation Department from Tsinghua University, China, in 1995, and an M.A. degree in Beijing Foreign Studies University, China, in 1997, and an M.S. and a Ph.D. degree in computer science and engineering at Michigan State University in 2003 and 2004, respectively. He is now an assistant professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology. His research interests include peer-to-peer computing, pervasive computing, distributed systems, network security, grid computing, and high-speed networking. He is a senior member of the IEEE Computer Society. Lionel M. Ni is chair professor and head of the Computer Science and Engineering Department at Hong Kong University of Science and Technology. Lionel M. Ni received the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, Indiana, in 1980. He was a professor of computer science and engineering at Michigan State University from 1981 to 2003, where he received the Distinguished Faculty Award in 1994. His research interests include parallel architectures, distributed systems, high-speed networks, and pervasive computing. A fellow of the IEEE and the IEEE Computer Society, he has chaired many professional conferences and has received a number of awards for authoring outstanding papers.     

Inference of abduction theories for handling incompleteness in first-order learning

In real-life domains, learning systems often have to deal with various kinds of imperfections in data such as noise, incompleteness and inexactness. This problem seriously affects the knowledge discovery process, specifically in the case of traditional Machine Learning approaches that exploit simple or constrained knowledge representations and are based on single inference mechanisms. Indeed, this limits their capability of discovering fundamental knowledge in those situations. In order to broaden the investigation and the applicability of machine learning schemes in such particular situations, it is necessary to move on to more expressive representations which require more complex inference mechanisms. However, the applicability of such new and complex inference mechanisms, such as abductive reasoning, strongly relies on a deep background knowledge about the specific application domain. This work aims at automatically discovering the meta-knowledge needed to abduction inference strategy to complete the incoming information in order to handle cases of missing knowledge. Floriana Esposito received the Laurea degree in electronic Physics from the University of Bari, Italy, in 1970. Since 1994 is Full Professor of Computer Science at the University of Bari and Dean of the Faculty of Computer Science from 1997 to 2002. She founded and chairs the Laboratory for Knowledge Acquisition and Machine Learning of the Department of Computer Science. Her research activity started in the field of numerical models and statistical pattern recognition. Then her interests moved to the field of Artificial Intelligence and Machine Learning. The current research concerns the logical and algebraic foundations of numerical and symbolic methods in machine learning with the aim of the integration, the computational models of incremental and multistrategy learning, the revision of logical theories, the knowledge discovery in data bases. Application include document classification and understanding, content based document retrieval, map interpretation and Semantic Web. She is author of more than 270 scientific papers and is in the scientific committees of many international scientific Conferences in the field of Artificial Intelligence and Machine Learning. She co-chaired ICML96, MSL98, ECML-PKDD 2003, IEA-AIE 2005, ISMIS 2006. Stefano Ferilli was born in 1972. After receiving his Laurea degree in Information Science in 1996, he got a Ph.D. in Computer Science at the University of Bari in 2001. Since 2002 he is an Assistant Professor at the Department of Computer Science of the University of Bari. His research interests are centered on Logic and Algebraic Foundations of Machine Learning, Inductive Logic Programming, Theory Revision, Multi-Strategy Learning, Knowledge Representation, Electronic Document Processing and Digital Libraries. He participated in various National and European (ESPRIT and IST) projects concerning these topics, and is a (co-)author of more than 80 papers published on National and International journals, books and conferences/workshops proceedings. Teresa M.A. Basile got the Laurea degree in Computer Science at the University of Bari, Italy (2001). In March 2005 she discussed a Ph.D. thesis in Computer Science at the University of Bari titled “A Multistrategy Framework for First-Order Rules Learning.” Since April 2005, she is a research at the Computer Science Department of the University of Bari working on methods and techniques of machine learning for the Semantic Web. Her research interests concern the investigation of symbolic machine learning techniques, in particular of the cooperation of different inferences strategies in an incremental learning framework, and their application to document classification and understanding based on their semantic. She is author of about 40 papers published on National and International journals and conferences/workshops proceedings and was/is involved in various National and European projects. Nicola Di Mauro got the Laurea degree in Computer Science at the University of Bari, Italy. From 2001 he went on making research on machine learning in the Knowledge Acquisition and Machine Learning Laboratory (LACAM) at the Department of Computer Science, University of Bari. In March 2005 he discussed a Ph.D. thesis in Computer Science at the University of Bari titled “First Order Incremental Theory Refinement” which faces the problem of Incremental Learning in ILP. Since January 2005, he is an assistant professor at the Department of Computer Science, University of Bari. His research activities concern Inductive Logic Programming (ILP), Theory Revision and Incremental Learning, Multistrategy Learning, with application to Automatic Document Processing. On such topics HE is author of about 40 scientific papers accepted for presentation and publication on international and national journals and conference proceedings. He took part to the European projects 6th FP IP-507173 VIKEF (Virtual Information and Knowledge Environment Framework) and IST-1999-20882 COLLATE (Collaboratory for Annotation, Indexing and Retrieval of Digitized Historical Archive Materials), and to various national projects co-funded by the Italian Ministry for the University and Scientific Research.     

Constrained partial deduction and the preservation of characteristic trees

Partial deduction strategies for logic programs often use an abstraction operator to guarantee the finiteness of the set of goals for which partial deductions are produced. Finding an abstraction operator which guarantees finiteness and does not lose relevant information is a difficult problem. In earlier work Gallagher and Bruynooghe proposed to base the abstraction operator oncharacteristic paths andtrees, which capture the structure of the generated incomplete SLDNF-tree for a given goal. In this paper we exhibit the advantages of characteristic trees over purely syntactical measures: if characteristic trees can be preserved upon generalisation, then we obtain an almost perfect abstraction operator, providing just enough polyvariance to avoid any loss of local specialisation. Unfortunately, the abstraction operators proposed in earlier work do not always preserve the characteristic trees upon generalisation. We show that this can lead to important specialisation losses as well as to non-termination of the partial deduction algorithm. Furthermore, this problem cannot be adequately solved in the ordinary partial deduction setting. We therefore extend the expressivity and precision of the Lloyd and Shepherdson partial deduction framework by integrating constraints. We provide formal correctness results for the so obtained generic framework ofconstrained partial deduction. Within this new framework we are, among others, able to overcome the above mentioned problems by introducing an alternative abstraction operator, based on so calledpruning constraints. We thus present a terminating partial deduction strategy which, for purely determinate unfolding rules, induces no loss of local specialisation due to the abstraction while ensuring correctness of the specialised programs. Michael Leuschel, Ph.D.: He currently works as a postdoctoral researcher at the Department of Computer Science of the Katholieke Universiteit Leuven. His present research focuses on program transformation and specialisation for declarative programming languages. Other research interests include abstract interpretation, optimised integrity checking and meta-programming. He received his degree (“Licence”) in Computer Science from the Université Libre de Bruxelles in 1990 and a Master of Artificial Intelligence from the Katholieke Universiteit Leuven in 1993, where he also received his Ph.D in 1997. Danny De Schreye, Ph.D: He is a professor at the Department of Computer Science of the Katholieke Universiteit Leuven and a senior research associate of the Belgian National Fund for Scientific Research. He obtained his Ph.D from K.U. Leuven in 1983, on the topic of operator algebras. His research interests are in the field of Logic Programming, and include program transformation and termination, knowledge representation and reasoning, and constraint programming.     

A logic-based integration of active and deductive databases

A logic-based approach to the specification of active database functionality is presented which not only endows active databases with a well-defined and well-understood formal semantics, but also tightly integrates them with deductive databases. The problem of endowing deductive databases with rule-based active behaviour has been addressed in different ways. Typical approaches include accounting for active behaviour by extending the operational semantics of deductive databases, or, conversely, accounting for deductive capabilities by constraining the operational semantics of active databases. The main contribution of the paper is an alternative approach in which a class of active databases is defined whose operational semantics is naturally integrated with the operational semantics of deductive databases without either of them strictly subsuming the other. The approach is demonstrated via the formalization of the syntax and semantics of an active-rule language that can be smoothly incorporated into existing deductive databases, due to the fact that the standard formalization of deductive databases is reused, rather than altered or extended. One distinctive feature of the paper is its use of ahistory, as defined in the Kowalski-Sergot event-calculus, to define event occurrences, database states and actions on these. This has proved to be a suitable foundation for a comprehensive logical account of the concept set underpinning active databases. The paper thus contributes a logical perspective to the ongoing task of developing a formal theory of active databases. Alvaro Adolfo Antunes Fernandes, Ph.D.: He received a B.Sc. in Economics (Rio de Janeiro, 1984), an M.Sc. in Knowledge-Based Systems (Edinburgh, 1990) and a Ph.D. in Computer Science (Heriot-Watt, 1995). He worked as a Research Associate at Heriot-Watt University from December 1990 until December 1995. In January 1996 he joined the Department of Mathematical and Computing Sciences at Goldsmiths College, University of London, as a Lecturer. His current research interests include advanced data- and knowledge-base technology, logic programming, and software engineering. M. Howard Williams, Ph.D., D.Sc.: He obtained his Ph.D. in ionospheric physics and recently a D.Sc. in Computer Science. He was appointed as the first lecturer in Computer Science at Rhodes University in 1970. During the following decade he rose to Professor of Computer Science and in 1980 was appointed as Professor of Computer Science at Heriot-Watt University. From 1980 to 1988 he served as Head of Department and then as director of research until 1992. He is now head of the Database Research Group at Heriot-Watt University. His current research interests include active databases, deductive objectoriented databases, spatial databases, parallel databases and telemedicine. Norman W. Paton, Ph.D.: He received a B.Sc. in Computing Science from the University of Aberdeen in 1986. From 1986 to 1989 he worked as a Research Assistant at the University of Aberdeen, receiving a Ph. D. in 1989. From 1989 to 1995 he was a Lecturer in Computer Science at Heriot-Watt University. Since July 1995, he has been a Senior Lecturer in Department of Computer Science at the University of Manchester. His current research interests include active databases, deductive object-oriented databases, spatial databases and database interfaces.     

The Efficiency of Critical Slicing in Fault Localization

In software testing, developing effective debugging strategies is important to guarantee the reliability of software under testing. A heuristic technique is to cause failure and therefore expose faults. Based on this approach mutation testing has been found very useful technique in detecting faults. However, it suffers from two problems with successfully testing programs: (1) requires extensive computing resources and (2) puts heavy demand on human resources. Later, empirical observations suggest that critical slicing based on Statement Deletion (Sdl) mutation operator has been found the most effective technique in reducing effort and the required computing resources in locating the program faults. The second problem of mutation testing may be solved by automating the program testing with the help of software tools. Our study focuses on determining the effectiveness of the critical slicing technique with the help of the Mothra Mutation Testing System in detecting program faults. This paper presents the results showing the performance of Mothra Mutation Testing System through conducting critical slicing testing on a selected suite of programs. Zuhoor Abdullah Al-Khanjari is an assistant professor in the Computer Science Department at Sultan Qaboos University, Sultanate of Oman. She received her BSc in mathematics and computing from Sultan Qaboos University, MSc and PhD in Computer Science (Software Engineering) from the University of Liverpool, UK. Her research interests include software testing, database management, e-learning, human-computer interaction, programming languages, intelligent search engines, and web data mining and development. ~Currently, she is the coordinator of the software engineering research group in the Department of Computer Science, College of Science, Sultan Qaboos University. She is also coordinating a program to develop e-learning based undergraduate teaching in the Department of Computer Science. Currently she is holding the position of assistant dean for postgraduate studies and research in the College of Science, Sultan Qaboos University, Sultanate of Oman. Martin Woodward is a Senior Fellow in the Computer Science Department at the University of Liverpool in the UK. After obtaining BSc and Ph.D. degrees in mathematics from the University of Nottingham, he was employed by the University of Oxford as a Research Assistant on secondment to the UK Atomic Energy Authority at the Culham Laboratory. He has been at the University of Liverpool for many years and initially worked on the so-called ‘Testbed’ project, helping to develop automated tools for software testing which are now marketed successfully by a commercial organisation. His research interests include software testing techniques, the relationship between formal methods and testing, and software visualisation. He has served as Editor of the journal ‘Software Testing, Verification and Reliability’ for the past thirteen years. Haider Ramadhan is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his BS and MS in Computer Science from University of North Carolina, and the PhD in Computer Science and AI from Sussex University. His research interests include visualization of software, systems, and process, system engineering, human-computer interaction, intelligent search engines, and Web data mining and development. Currently, he is the chairman of the Computer Science Department, College of Science, Sultan Qaboos University, Sultanate of Oman. Swamy Kutti (N. S. Kutti) is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his B.E. in Electronics Engineering from the University of Madras, M.E. in Communication Engineering from Indian Institute of Science (Bangalore), and the MSc in Computer Science from Monash University (Australia) and PhD in Computer Science from Deakin University (Australia). His research interests include Real-Time Programming, Programming Languages, Program Testing and Verification, eLearning, and Distributed Operating Systems.     

PAN: A portable,parallel Prolog: Its design,realisation and performance

PAN is a general purpose, portable environment for executing logic programs in parallel. It combines a flexible, distributed architecture which is resilient to software and platform evolution with facilities for automatically extracting and exploiting AND and OR parallelism in ordinary Prolog programs. PAN incorporates a range of compile-time and run-time techniques to deliver the performance benefits of parallel execution while rertaining sequential execution semantics. Several examples illustrate the efficiency of the controls that facilitate the execution of logic programs in a distributed manner and identify the class of applications that benefit from distributed platforms like PAN. George Xirogiannis, Ph.D.: He received his B.S. in Mathematics from the University of Ioannina, Greece in 1993, his M.S in Artificial Intelligence from the University of Bristol in 1994 and his Ph.D. in Computer Science from Heriot-Watt University, Edinburgh in 1998. His Ph.D. thesis concerns the automated execution of Prolog on distributed heterogeneous multi-processors. His research interests have progressed from knowledge-based systems to distributed logic programming and data mining. Currently, he is working as a senior IT consultant at Pricewaterhouse Coopers. He is also a Research Associate at the National Technical University of Athens, researching in knowledge and data mining. Hamish Taylor, Ph.D.: He is a lecturer in Computer Science in the Computing and Electrical Engineering Department of Heriot-Watt University in Edinburgh. He received M.A. and MLitt degrees in philosophy from Cambridge University and an M.S. and a Ph.D. degree in computer science from Heriot-Watt University, Scotland. Since 1985 he has worked on research projects concerned with implementing concurrent logic programming languages, developing formal models for automated reasoning, performance modelling parallel relational database systems, and visualisizing resources in shared web caches. His current research interests are in applications of collaborative virtual environments, parallel logic programming and networked computing technologies.     

On some operations on strings suggested by gene assembly in ciliates

We define three operations on strings and languages suggested by the process of gene assembly in hypotrichous ciliates. This process is considered to be a prine example of DNA computing in vivo. This paper is devoted to some computational aspects of these operations from a formal language point of view. The closure of the classes of regular and context-free languages under these operations is settled. Then, we consider theld-macronuclear language of a given languageL, which consists of allld-macronuclear strings obtained from the strings ofL by iteratively applying the loop-direct repeat-excision. Finally, we discuss some open problems and further directions of research. Rudolf Freund: He received his master and doctor degree in computer science from the Vienna University of Technology, Austria, in 1980 and 1982, respectively. In 1986, he received his master degree in mathematics and physics from the University Vienna, Austria. In 1988 he joined the Vienna University of Technology in Austria, where he became an Associate Professor in September 1995. He has given various lectures in theoretical computer science, especially on formal languages and automata. His research interests include array and graph grammars, regulated rewritung, infinite words, syntactic pattern recognition, neural networks, and especially models and systems for biological computing. In these fields he is author of more than sixty scientific papers. Carlos Martín-Vide: He is Professor and Head of the Research Group on Mathematical Linguistics at Rovira i Virgili University, Tarragona, Spain. His specialities are formal language theory and mathematical linguistics. His last volume edited is Where Mathematics, Computer Science, Linguistics and Biology Meet (Kluwer, 2001, with V. Mitrana). He published 150 papers in conference proceedings and journals such as: Acta Informatica, BioSystems. Computational Linguistics, Computers and Artificial Intelligence, Information Processing Letters, Information Sciences, International Journal of Computer Mathematics, New Generation Computing, Publicationes Mathematicae Debrecen, and Theoretical Computer Science. He is the editor-in-chief of the journal Grammars (Kluwer), and the chairman of the 1st International PhD School in Formal Languages and Applications (2001–2003). Victor Mitrana, Ph.D.: He is Professor of Computer Science at the Faculty of Mathematics, University of Bucharest. He received his MSc and PhD from the University of Bucharest in 1986 and 1993, respectively. In 1999 he was awarded with the “Gheorghe Lazar” Prize for Mathematics of the Romanian Academy. His research interests include: formal language theory and applications, combinatorics on words, computational models inspired from biology, mathematical linguistics. In these areas, he published three books, more than 100 papers, and edited two books. He is an associate editor of “The Korean Journal of Computational and Applied Mathematics” and an editor of “Journal of Universal Computer Science”.