Implementing Constructive Intensional Negation

Although negation is an active area of research in logic programming, sound and complete implementations are still absent from actual Prolog systems. One of the most promising techniques in the literature is intensional negation (IN), which follows a transformational approach: for each predicate p in a program its negative counterpart intneg(p) is generated. However, implementations of IN have not been included in Prolog environments due, in part, to the lack of details and explicit techniques, such as the treatment of universally quantified goals. In this paper, we describe a variant of IN, which we have called constructive intensional negation (CIN). Unlike earlier proposals, CIN does not resort to a dedicated resolution strategy when dealing with universally quantified formulae, which has been instrumental in having an effective implementation. Therefore, pure SLD resolution is used, what enables the reuse of existing Prolog implementation technology. Among the contributions of this work we can mention not only a full implementation being tested for its integration in the Ciao Prolog system but also some formal results ensuring soundness and completeness with their associated proofs.

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.     

Performance of an OR-parallel logic programming system

The research focus in parallel logic programming is shifting rapidly from theoretical considerations and simulation on uniprocessors to implementation on true multiprocessors. This report presents performance figures from such a system,Boplog, for OR-parallel Horn clause logic programs on the BBN Butterfly Parallel Processor. Boplog is designed expressly for a large scale shared memory multiprocessor with an Omega interconnect. The target machine and underlying execution model are described briefly. The primary focus of the paper is on detailed statistics taken from the execution of benchmark programs to assess the performance of the model and clarify the impact of design and architecture decisions. They show that while speedup of this implementation on highly OR-parallel problems is very good, overall performance is poor. Despite its speed drawback, many aspcts of the implementation and its performance can prove useful in designing future systems for similar machines. A binding model that prohibits constant time access to bindings, and the inability of the machine to support an ambitious use of machine memory appear to be most damaging factors.This work was carried out at the University of Utah, Salt Lake City, Utah. It was supported by a University of Utah Graduate Research Fellowship, the National Science Foundation under Grant DCR-856000, and by an unrestricted gift from L. M. Ericsson Telefon AB, Stockholm, Sweden, Production of the document was supported by the Rockwell International Science Center.     

Well-founded semantics and stratification for ordered logic programs

This paper present an extension of traditional logic programming, called ordered logic (OL) programming, to support classical negation as well as constructs from the object-oriented paradigm. In particular, such an extension allows to cope with the notions of object, multiple inheritance and non-monotonic reasoning. The contribution of the work is mainly twofold. First, a rich wellfounded semantics for ordered logic programs is defined. Second, an efficient method for the well-founded model computation of a meaningful class of ordered logic programs, called stratified programs, is provided.     

On the complexity of some inductive logic programming problems

The bounded ILP-consistency problem for function-free Horn clauses is described as follows. Given at setE ⁺ andE ^? of function-free ground Horn clauses and an integerk polynomial inE ⁺∪E ^?, does there exist a function-free Horn clauseC with no more thank literals such thatC subsumes each element inE ⁺ andC does not subsume any element inE ^?? It is shown that this problem is Σ ₂ ^P complete. We derive some related results on the complexity of ILP and discuss the usefulness of such complexity results.     

From extended feature models to constraint logic programming

Since feature models for realistic product families may be quite complicated, the automated analysis of feature models is desirable. Although several approaches reported in the literature address this issue, complex cross-tree relationships involving attributes in extended feature models have not been handled. In this article, we introduce a mapping from extended feature models to constraint logic programming over finite domains. This mapping is used to translate into constraint logic programs; basic, cardinality-based and extended feature models, which can include complex cross-tree relationships involving attributes. This translation enables the use of off-the-shelf constraint solvers for the automated analysis of extended feature models involving such complex relationships. We also present the performance results of some well-known analysis operations on an example translated model.     

Translations and similarity-based logic programming

In order to provide approximate reasoning capabilities, in Gerla G, Sessa MI (1999) Chen G, Ying M, Cai K-Y (Eds) Fuzzy Logic and Soft computing, 19–31, Kluwer Academic Publishers, Boston an extension of Logic Programming has been proposed. Logic programs on function-free languages are considered, and approximate and imprecise information are represented by introducing a similarity relation ? in the set of predicate names and object names of the language. The inference system exploits the classical resolution rule of the Logic Programming paradigm. Moreover, the notion of fuzzy least Herbrand model is also provided. In this paper, by introducing the general notion of structural translation of languages, we generalize these results to the case of logic programs with function symbols. Some properties of the similarity relations are also proven.     

约束逻辑程序设计综述

一、引言 约束逻辑程序设计(Constraint Logic Program-ming．CLP)是基于人工智能(AI)中约束满足问题(Constraint Satisfaction Problem．CSP)模型的一种程序设计风范。CLP是逻辑程序设计(LP)的一种推广,是八十年代发展起来的一种新的逻辑程序设计方法。由于它继承了LP简单易懂的说明性描述方法并结合了CSP在求解问题时的效率,使它在解决很多AI问题(如组合问题、资源分配、事务安排等)时有不凡的表现。更由于AI领域中绝大多数问题可以用CLP来表示,所以这一方法已引起了人们的广泛注意,并在八十年代后期得以迅速发展。     

约束归纳逻辑程序设计方法的研究

提出了一种新的约束归纳逻辑程序设计方法。该方法能够与自顶向下的归纳逻辑程序设计系统结合,通过在自顶向下归纳方法的一步特殊化操作中引入Fisher判别分析等方法,使得系统能够导出不受变量个数限制的多种形式的线性约束,在不需要用户诱导,不依赖约束求解器的情况下,学习出覆盖正例而排斥负例的含约束的Horn子句程序。     

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.