Logic Programming with Solution Preferences: A Declarative Method.

Preference logic programming (PLP) is an extension of constraint logic program­ming for declaratively specifying problems requiring optimization or comparison and selection among alternative solutions to a query. PLP essentially separates the programming of a problem itself from the criteria specification of its solution selection. This thesis presents a declarative method of specifying and executing preference logic programs based on a tabled Prolog system. The method intro­duces a formal predicate mode declaration for designating certain predicates as optimization predicates, and stating the criteria for determining their optimal so­lutions via preference rules. A flexible mode declaration scheme is implemented in a tabled Prolog system, which provides an easy implementation vehicle for programming with preferences. Finally, experimental results and performance analysis demonstrate the effectiveness of the method

Automatic Generation of CHR Constraint Solvers

In this paper, we present a framework for automatic generation of CHR solvers given the logical specification of the constraints. This approach takes advantage of the power of tabled resolution for constraint logic programming, in order to check the validity of the rules. Compared to previous works where different methods for automatic generation of constraint solvers have been proposed, our approach enables the generation of more expressive rules (even recursive and splitting rules) that can be used directly as CHR solvers.Comment: to be published in Theory and Practice of Logic Programming, 16 pages, 2 figure

Termination Proofs for Logic Programs with Tabling

Tabled logic programming is receiving increasing attention in the Logic Programming community. It avoids many of the shortcomings of SLD execution and provides a more flexible and often extremely efficient execution mechanism for logic programs. In particular, tabled execution of logic programs terminates more often than execution based on SLD-resolution. In this article, we introduce two notions of universal termination of logic programming with Tabling: quasi-termination and (the stronger notion of) LG-termination. We present sufficient conditions for these two notions of termination, namely quasi-acceptability and LG-acceptability, and we show that these conditions are also necessary in case the tabling is well-chosen. Starting from these conditions, we give modular termination proofs, i.e., proofs capable of combining termination proofs of separate programs to obtain termination proofs of combined programs. Finally, in the presence of mode information, we state sufficient conditions which form the basis for automatically proving termination in a constraint-based way.Comment: 48 pages, 6 figures, submitted to ACM Transactions on Computational Logic (TOCL

Inference with Constrained Hidden Markov Models in PRISM

A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming have advantages in terms of more compact expression and pruning opportunities during inference. We present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment