We propose a novel formalism, called Frame Logic (abbr., F-logic), that accounts in a clean and declarative fashion for most of the structural aspects of object-oriented and frame-based languages. These features include object identity, complex objects, inheritance, polymorphic types, query methods, encapsulation, and others. In a sense, F-logic stands in the same relationship to the objectoriented paradigm as classical predicate calculus stands to relational programming. F-logic has a model-theoretic semantics and a sound and complete resolution-based proof theory. A small number of fundamental concepts that come from object-oriented programming have direct representation in F-logic; other, secondary aspects of this paradigm are easily modeled as well. The paper also discusses semantic issues pertaining to programming with a deductive object-oriented language based on a subset of F-logic.

SLD resolution with negation as finite failure (SLDNF) reflects the procedural interpretation of predicate calculus as a programming language and forms the computational basis for Prolog systems. Despite its advantages for stack-based memory management, SLDNF is often not appropriate for query evaluation for three reasons: a) it may not terminate due to infinite positive recursion; b) it may not terminate due to infinite recursion through negation; c) it may repeatedly evaluate the same literal in a rule body, leading to unacceptable performance. We address three problems fir a goal-oriented query evaluation of general logic programs by presenting tabled evaluation with delaying (SLG resolution).

In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider exten- sions of the language of definite logic programs by classical (strong) negation, disjunc- tion, and some modal operators and show how each of the added features extends the representational power of the language.

Query processing and optimization in mediator systems that access distributed non-proprietary sources pose many novel problems. Cost-based query optimization is hard because the mediator does not have access to source statistics information and furthermore it may not be easy to model the source's performance. At the same time, querying remote sources may be very expensive because of high connection overhead, long computation time, financial charges, and temporary unavailability. We propose a costbased optimization technique that caches statistics of actual calls to the sources and consequently estimates the cost of the possible execution plans based on the statistics cache. We investigate issues pertaining to the design of the statistics cache and experimentally analyze various tradeoffs. We also present a query result caching mechanism that allows us to effectively use results of prior queries when the source is not readily available. We employ the novel invariants mechanism, which s...

This report describes SP, a system for specialising logic programs. The report functions as a user's manual for SP, and also contains the algorithms employed and arguments for their correctness. A number of examples of program specialisation are given in Appendix A. Contents 1 Program Specialisation 4 2 Transformations in SP 5 3 Unfolding Rules 10 4 Approximation 13 5 The Specialisation Algorithm 16 6 How to Use SP 20 7 Discussion 23 A Examples of Specialisation 28 B Unfoldability Conditions for Built-ins 36 1 Program Specialisation SP is a system for specialising logic programs. Before describing the system, it is worth reviewing briefly the aims and interesting applications of program specialisation. To specialise a program is to restrict its behaviour in some way. The purpose of specialisation is to exploit the restriction to gain efficiency. A specialised program is equivalent, within the bounds of the restriction imposed, to the original unspecialised program, but should be ...

We demonstrate the feasibility of using the XSB tabled logic programming system as a programmable fixed-point engine for implementing efficient local model checkers. In particular, we present XMC, an XSBbased local model checker for a CCS-like value-passing language and the alternation-free fragment of the modal mu-calculus. XMC is written in under 200 lines of XSB code, which constitute a declarative specification of CCS and the modal mu-calculus at the level of semantic equations. In order to gauge the performance of XMC as an algorithmic model checker, we conducted a series of benchmarking experiments designed to compare the performance of XMC with the local model checkers implemented in C/C++ in the Concurrency Factory and SPIN specification and verification environments. After applying certain newly developed logic-programmingbased optimizations (along with some standard ones), XMC's performance became extremely competitive with that of the Factory and shows promise in its comparison with SPIN.

The area of deductive databases has matured in recent years, and it now seems appropriate to re ect upon what has been achieved and what the future holds. In this paper, we provide an overview of the area and briefly describe a number of projects that have led to implemented systems.

Abstract: Mode and data dependency analyses find many applications in the generation of efficient exe-cutable code for logic programs. For example, mode information can be used to generate specialized unification instructions where permissible; to detect determinacy and functionality of programs; to gen-erate index structures more intelligently; to reduce the amount of runtime tests in systems that support goal suspension; and in the integration of logic and functional languages. Data dependency information can be used for various source-level optimizing transformations, to improve backtracking behavior, and to parallelize logic programs. This paper describes and proves correct an algorithm for the static infer-ence of modes and data dependencies in a program. The algorithm is shown to be quite efficient for pro-grams commonly encountered in practice.

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, thatrunsfora class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have beendeveloped independently in each research eld. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can signi cantly outperform the Inside-Outside algorithm. 1.

The well-founded model provides a natural and robust semantics for logic programs with negative literals in rule bodies. Although various procedural semantics have been proposed for query evaluation under the well-founded semantics, the practical issues of implementation for effective and efficient computation of queries have been rarely discussed. This paper investigates two major implementation issues of query evaluation under the well-founded semantics, namely (a) to ensure that negative literals be resolved only after their positive counterparts have been completely evaluated, and (b) to detect and handle potential negative loops. We present efficient incremental algorithms for maintaining positive and negative dependencies among subgoals in a top-down evaluation. Both completely evaluated subgoals and potential negative loops are detected by inspecting the dependency information of a single subgoal. Our implementation can be viewed as an effective successor to SLDNF resolution, ex...