S-semantics for logic programming: A retrospective look

The paper provides an overview of the s-semantic approach to the semantics of logic programs which had been developed about twenty years ago. The aim of such an approach was that of providing a suitable base for program analysis by means of a semantics which really captures the operational behavior of logic programs, and thus offers useful notions of observable program equivalences. The semantics is given in terms of extended interpretations, which are more expressive than Herbrand interpretations, extends the standard Herbrand semantics, and can be obtained as a result of both top-down and bottom-up constructions. The approach has been applied to several extensions of positive logic programs and used to develop semantic-based techniques for program analysis, verification and transformation.     

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.     

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.     

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.     

Compositional verification of real-time systems with Explicit Clock Temporal Logic

To specify and verify real-time systems, we consider a real-time version of temporal logic called Explicit Clock Temporal Logic. Timing properties are specified by extending the classical framework of temporal logic with a special variable which explicitly refers to a global notion of time. Programs are written in an Occam-like real-time language with synchronous message passing. To show that a program satisfies a specification, we formulate a proof system which is proved to be sound and relatively complete. The proof system is compositional, which makes it possible to decompose the design of a large system into the design of subsystems. This is shown by the verification of a small part of an avionics system.This research was supported by ESPRIT-BRA project 3096 Formal Methods and Tools for the Development of Distributed and Real-Time Systems (SPEC).     

Overview of disjunctive logic programming

The field of disjunctive programming started approximately in 1982 and has reached its first decade. The first result in the field was the development of the Generalized Closed World Assumption (GCWA). Major results have been made in this field since 1986. An overview is presented of the developments that have taken place, which include model theoretic, proof theoretic and fixpoint semantics for disjunctive, and extended normal disjunctive theories including alternative forms of negation.Dedicated to Chitta Baral, José Alberto Fernández, Jorge Lobo and Arcot Rajasekar.     

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.     

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.     

Interpreting disjunctive logic programs based on a strong sense of disjunction

This paper studies a strong form of disjunctive information in deductive databases. The basic idea is that a disjunctionA B should be considered true only in the case when neitherA norB can be inferred, but the disjunctionA B is true. Under this interpretation, databases may be inconsistent. For those databases that are consistent, it is shown that a unique minimal model exists. We study a fixpoint theory and present a sound and complete proof procedure for query processing in consistent databases. For a class of inconsistent databases, we obtain a declarative semantics by selecting an interpretation that maximizes satisfaction, and minimizes indefiniteness. Two notions of negation are introduced.     

Negation as failure for disjunctive logic programming

We generalize the Negation-as-Failure procedure for disjunctive logic programming. Then we compare our method with related methods in the literature. We also propose a new completion theory for disjunctive logic programs which allows some program clauses to have inclusive meaning and the others exclusive meaning.     

On look-ahead heuristics in disjunctive logic programming

Disjunctive logic programming (DLP), also called answer set programming (ASP), is a convenient programming paradigm which allows for solving problems in a simple and highly declarative way. The language of DLP is very expressive and able to represent even problems of high complexity (every problem in the complexity class ${{\Sigma}_{2}^{P}} = {\rm NP}^{{\rm NP}}$ ). During the last decade, efficient systems supporting DLP have become available. Virtually all of these systems internally rely on variants of the Davis–Putnam procedure (for deciding propositional satisfiability [SAT]), combined with a suitable model checker. The heuristic for the selection of the branching literal (i.e., the criterion determining the literal to be assumed true at a given stage of the computation) dramatically affects the performance of a DLP system. While heuristics for SAT have received a fair deal of research, only little work on heuristics for DLP has been done so far. In this paper, we design, implement, optimize, and experiment with a number of heuristics for DLP. We focus on different look-ahead heuristics, also called “dynamic heuristics” (the DLP equivalent of unit propagation [UP] heuristics for SAT). These are branching rules where the heuristic value of a literal Q depends on the result of taking Q true and computing its consequences. We motivate and formally define a number of look-ahead heuristics for DLP programs. Furthermore, since look-ahead heuristics are computationally expensive, we design two techniques for optimizing the burden of their computation. We implement all the proposed heuristics and optimization techniques in DLV—the state-of-the-art implementation of disjunctive logic programming, and we carry out experiments, thoroughly comparing the heuristics and optimization techniques on a large number of instances of well-known benchmark problems. The results of these experiments are very interesting, showing that the proposed techniques significantly improve the performance of the DLV system.     

Towards a logic programming methodology based on higher-order predicates

This paper outlines a logic programming methodology which applies standardized logic program recursion forms afforded by a system of general purpose recursion schemes. The recursion schemes are conceived of as quasi higher-order predicates which accept predicate arguments, thereby representing parameterized program modules. This use of higher-order predicates is analogous to higher-order functionals in functional programming. However, these quasi higher-order predicates are handled by a metalogic programming technique within ordinary logic programming. Some of the proposed recursion operators are actualizations of mathematical induction principles (e.g. structural induction as generalization of primitive recursion). Others are heuristic schemes for commonly occurring recursive program forms. The intention is to handle all recursions in logic programs through the given repertoire of higher-order predicates. We carry out a pragmatic feasibility study of the proposed recursion operators with respect to the corpus of common textbook logic programs. This pragmatic investigation is accompanied with an analysis of the theoretical expressivity. The main theoretical results concerning computability are

1. Primitive recursive functions can be re-expressed in logic programming by predicates defined solely by non-recursive clauses augmented with afold recursion predicate akin to the fold operators in functional programming.
2. General recursive functions can be re-expressed likewise sincefold allows re-expression of alinrec recursion predicate facilitating linear, unbounded recursion.

     

Dynamic composition of parameterised logic modules

We present a logic-based programming language that features meta-level composition operations over programs. Object programs are parameterised named collections of definite clauses which may contain formulae of the form A in Pexp, where A is a standard atomic formula and Pexp is a program expression obtained by applying composition operations over named object programs. The semantics of the language is presented in two different equivalent styles. An operational, top-down semantics is given by means of inference rules, while a denotational, bottom-up semantics is given via an immediate consequence operator associated with program expressions. A meta-programming implementation of the language is also presented by transforming the operational inference rules into meta-level axioms. Several programming examples are discussed to illustrate the expressive power of the language.     

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.     

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。     

The Least Fixpoint Transformation for Disjunctive Logic Programs

The paradigm of disjunctive logic programming(DLP)enhances greatly the expressive power of normal logic programming(NLP)and many(declarative)semantics have been defined for DLP to cope with various problems of knowledge representation in artificial intelligence.However,the expressive ability of the semantics and the soundness of program transformations for DLP have been rarely explored.This paper defines an immediate consequence operatro T^GP for each disjunctive program and shows that T^GP has the least and computable fixpoint Lft(P),Lft is,in fact,a program transformation for DLP,which transforms all disjunctive programs into negative programs.It is shown that Lft preserves many key semantics,including the disjunctive stable models,well-founded model,disjunctive argunent semantics DAS,three-valued models,ect.Thic means that every disjunctive program P has a unique canonical form Lft(P)with respect to these semantics.As a result,the work in this paper provides a unifying framework for studying the expressive ability of various semantics for DLP On the other hand,the computing of the above semantics for negative programs is ust a trivial task,therefore,Lft(P)is also an optimization method for DLP.Another application of Lft is to derive some interesting semantic results for DLP.     

描述逻辑SHIOQ的时态扩展

分析描述逻辑时态扩展的优缺点,同时结合实际应用需求,将时间当作具体领域加入到描述逻辑中来处理,给出带时态扩展的描述逻辑SHIOQ（T）的形式化描述,并给出SHIOQ（T）中概念、关系和实例的描述形式以及它们的语义解释,从而方便地实现时态知识的表达和推理。     

行为时序逻辑与自反线性时序逻辑的关系

为了能够将哲学逻辑中的公理系统运用到行为时序逻辑的研究中。对行为时序逻辑公式的语义进行形式化定义．从语义和语法两方面研究行为时序逻辑公理系统和具有自反性质的线性时序逻辑公理系统之间的联系．提出并证明行为时序逻辑公式转换为自反线性时序逻辑公式的定理。按照集合论和模型论的思想,定义行为时序逻辑中项和行为时序逻辑原子公式的概念。定义Lesilie Lamport所提出的行为时序逻辑公式的语义。证明自反线性时序逻辑公理系统适用于行为时序逻辑公理系统．以此为基础证明行为时序逻辑的简单规则、基本规则和附加规则。     

Compositional model-theoretic semantics for logic programs

We present a compositional model-theoretic semantics for logic programs, where the composition of programs is modelled by the composition of the admissible Herbrand models of the programs. An Herbrand model is admissible if it is supported by the assumption of a set of hypotheses. On one hand, the hypotheses supporting a model correspond to an open interpretation of the program intended to capture possible compositions with other programs. On the other hand, admissible models provide a natural model-theory for a form of hypothetical reasoning, called abduction. The application of admissibel models to programs with negation is discussed. Antonio Brogi: Dipartimento di Informatica, Università di Pisa, Corso Italia 40, 56125 Pisa, ItalyResearch interests: Programming Language Design and Semantics, Logic Programming and Artificial Intelligence