arXiv subjects

Maurice Bruynooghe

Publications and source records attributed to Maurice Bruynooghe.

25 records · Page 2Linked to original sources

arXiv2002

Logic program specialisation through partial deduction: Control issues

Program specialisation aims at improving the overall performance of programs by performing source to source transformations. A common approach within functional and logic programming, known respectively as partial evaluation and partial deduction, is to exploit partial knowledge about the input. It is achieved through a well-automated application of parts of the Burstall-Darlington unfold/fold transformation framework. The main challenge in developing systems is to design automatic control that ensures correctness, efficiency, and termination. This survey and tutorial presents the main developments in controlling partial deduction over the past 10 years and analyses their respective merits and shortcomings. It ends with an assessment of current achievements and sketches some remaining research challenges.

cs.PL↗

arXiv2001

Practical Aspects for a Working Compile Time Garbage Collection System for Mercury

Compile-time garbage collection (CTGC) is still a very uncommon feature within compilers. In previous work we have developed a compile-time structure reuse system for Mercury, a logic programming language. This system indicates which datastructures can safely be reused at run-time. As preliminary experiments were promising, we have continued this work and have now a working and well performing near-to-ship CTGC-system built into the Melbourne Mercury Compiler (MMC). In this paper we present the multiple design decisions leading to this system, we report the results of using CTGC for a set of benchmarks, including a real-world program, and finally we discuss further possible improvements. Benchmarks show substantial memory savings and a noticeable reduction in execution time.

cs.PL↗

arXiv2000

Proving Failure of Queries for Definite Logic Programs Using XSB-Prolog

Proving failure of queries for definite logic programs can be done by constructing a finite model of the program in which the query is false. A general purpose model generator for first order logic can be used for this. A recent paper presented at PLILP98 shows how the peculiarities of definite programs can be exploited to obtain a better solution. There a procedure is described which combines abduction with tabulation and uses a meta-interpreter for heuristic control of the search. The current paper shows how similar results can be obtained by direct execution under the standard tabulation of the XSB-Prolog system. The loss of control is compensated for by better intelligent backtracking and more accurate failure analysis.

cs.LO↗

arXiv2000

Detecting Unsolvable Queries for Definite Logic Programs

In solving a query, the SLD proof procedure for definite programs sometimes searches an infinite space for a non existing solution. For example, querying a planner for an unreachable goal state. Such programs motivate the development of methods to prove the absence of a solution. Considering the definite program and the query ``<- Q'' as clauses of a first order theory, one can apply model generators which search for a finite interpretation in which the program clauses as well as the clause ``false <- Q'' are true. This paper develops a new approach which exploits the fact that all clauses are definite. It is based on a goal directed abductive search in the space of finite pre-interpretations for a pre-interpretation such that ``Q'' is false in the least model of the program based on it. Several methods for efficiently searching the space of pre-interpretations are presented. Experimental results confirm that our approach find solutions with less search than with the use of a first order model generator.

cs.LO↗

arXiv2000

A Polyvariant Binding-Time Analysis for Off-line Partial Deduction

We study the notion of binding-time analysis for logic programs. We formalise the unfolding aspect of an on-line partial deduction system as a Prolog program. Using abstract interpretation, we collect information about the run-time behaviour of the program. We use this information to make the control decisions about the unfolding at analysis time and to turn the on-line system into an off-line system. We report on some initial experiments.

cs.PL↗

arXiv2000

Logic Programming for Describing and Solving Planning Problems

A logic programming paradigm which expresses solutions to problems as stable models has recently been promoted as a declarative approach to solving various combinatorial and search problems, including planning problems. In this paradigm, all program rules are considered as constraints and solutions are stable models of the rule set. This is a rather radical departure from the standard paradigm of logic programming. In this paper we revisit abductive logic programming and argue that it allows a programming style which is as declarative as programming based on stable models. However, within abductive logic programming, one has two kinds of rules. On the one hand predicate definitions (which may depend on the abducibles) which are nothing else than standard logic programs (with their non-monotonic semantics when containing with negation); on the other hand rules which constrain the models for the abducibles. In this sense abductive logic programming is a smooth extension of the standard paradigm of logic programming, not a radical departure.

cs.AI↗

arXiv2000

A Comparison of Logic Programming Approaches for Representation and Solving of Constraint Satisfaction Problems

Many logic programming based approaches can be used to describe and solve combinatorial search problems. On the one hand there are definite programs and constraint logic programs that compute a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are approaches based on stable model semantics, abduction, and first-order logic model generation that compute solutions as models of some theory. This paper compares these different approaches from point of view of knowledge representation (how declarative are the programs) and from point of view of performance (how good are they at solving typical problems).

cs.LO↗