and complete proofs for the main lemmas. Importantly, we clarify which theorems depend on conditions such as solution compactness, satisfaction completeness and independence of constraints. Second, we generalize the original results to allow for incompleteness of the constraint solver. This is important

Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve

Almost all constraint logic programming systems include negation, yet nowhere has a sound operational model for negation in CLP been discussed. The SLDNF approach of only allowing ground negative subgoals to execute is very restrictive in constraint logic programming where most variables appearing

This paper shows how to apply memoization (caching of subgoals and associated answer substitutions) in a constraint logic programming setting. The research is is motivated by the desire to

Abstract. This paper introduces a necessary and sufficient condition for termination of constraint logic programs. The method is based on assigning a dataflow graph to a program, whose nodes are the program points and whose arcs are abstractions of the rules of a transition system, describing

Abstract. Constraint logic programming (CLP) has been proposed as a declarative paradigm for merging constraint solving and logic programming. Recently, coinductive logic programming has been proposed as a powerful extension of logic programming for handling (rational) infinite objects

Abstract. Constraint Logic Programming (CLP) is one of the most successful branches of Logic Programming; it attracts the interest of theoreticians and practitioners, and it is currently used in many commercial applications. Since the original proposal, it has developed enormously: many languages

these problems for a more expressive probabilistic constraint logic programming model. We present a log-linear probability model for probabilistic constraint logic programming. On top of this model we define an algorithm to estimate the parameters and to select the properties of log-linear models from incomplete

In this paper we describe the timetabling problem and its solvability in a Constraint Logic Programming Language. A solution to the problem has been developed and implemented in ECL i PS e , since it deals with finite domains, it has well-defined interfaces between basic building blocks

This project investigates constraint logic programming and its expansions following the introduction of uncertainty to both domains and constraints. A novel method involving alpha-cuts and dynamic arc constraint networks will be created to handle these forms of uncertainty and the final algorithm