We provide an overall description of the Ciao multiparadigm programming system emphasizing some of the novel aspects and motivations behind its design and implementation. An important aspect of Ciao is that, in addition to supporting logic programming (and, in particular, Prolog), it provides the programmer with a large number of useful features from different programming paradigms and styles, and that the use of each of these features (including those of Prolog) can be turned on and off at will for each program module. Thus, a given module may be using, e.g., higher order functions and constraints, while another module may be using imperative operations, predicates, Prolog metaprogramming builtins, and concurrency. Furthermore, the language is designed to be extensible in a simple and modular way. Another important aspect of Ciao is its programming environment, which provides a powerful preprocessor (with an associated assertion language) capable of statically finding non-trivial bugs, verifying that programs comply with specifications, and performing many types of program optimizations (including automatic parallelization). Such optimizations produce code that is highly competitive with other dynamic languages or, when the (experimental) optimizing compiler is used, even that of static languages, all while retaining the interactive development environment

Abstract. When implementing a propagator for a constraint, one must decide about variants: When implementing min, should one also implement max? Should one implement linear equations both with and without coefficients? Constraint variants are ubiquitous: implementing them requires considerable effort, but yields better performance. This paper shows how to use variable views to derive perfect propagator variants: derived propagators inherit essential properties such as correctness and domain and bounds completeness. 1

I would like to take this opportunity to express my sincere gratitude towards my academic and research advisor Dr. Gabor Karsai for helping me through this academic pursuit. I am grateful to him for providing me with the necessary guidance and supervision, for constantly motivating me to finish this research, and for his humility and kindness to allow me to study at Vanderbilt University and conduct this research. I also extend my sincere thanks to Dr. Gautam Biswas, my second approver, for his thorough evaluation of this thesis and providing his detailed and invaluable feedback. I also thank Chris van Buskirk for thoroughly reviewing this thesis and providing very useful comments and advices for improving its content. I am also thankful to Vanderbilt University for giving me the opportunity to study and pursue my Masters degree part-time while working here. Finally, but not the least, I offer my sincere thanks to my wife, Reena Neema, for her motivating and cheering support, and for always being there to support my

Abstract Boolean constraints play a fundamental rôle in optimization and constraint satisfaction. The resolution of these constraints has been the subject of intense and successful work during the past decade, and SAT solvers have reached a spectacular maturity. This chapter gives a brief overview of the relevant literature on modern SAT solvers and on the recent efforts to better integrate Boolean reasoning with other constraint satisfaction techniques. As a case study that illustrates the use of SAT and CP we consider an application in computational biology: the task to build gene regulatory networks (GRNs). We report on experiments made on this problem with a combined SAT/CP approach. 1

Abstract. In this paper we describe models of the logic puzzle Kakuro as a constraint problem with finite domain variables. We show a basic model expressing the constraints of the problem and present various improvements to enhance the constraint propagation, and compare alternatives using MILP and SAT solvers. Results for different puzzle collections are given. We also propose a grading scheme predicting the difficulty of a puzzle for a human and show how problems can be tightened by removing hints. 1

Abstract. Most constraint solvers use the general AC-5 scheme [17] to handle constraint propagation. AC-5 generalizes the concept of constraint revision. Each constraint type can thus be shipped with its own revision algorithm, with various complexities and performances. Previous papers showed that the order in which constraints are revised have a non-negligible impact on performances of propagation [20,6,1]. However, most of the ideas presented on these papers are based on the use of homogeneous propagators for binary constraints defined in extension. This paper give ideas to handle heterogeneous constraints in a general revision schedule. 1

Abstract. In this paper we describe models of the logic puzzle Kakuro as a constraint problem with finite domain variables. We show a basic model expressing the constraints of the problem and present various improvements to enhance the constraint propagation, and compare alternatives using MILP and SAT solvers. Results for different puzzle collections are given. We also propose a grading scheme predicting the difficulty of a puzzle for a human and show how problems can be tightened by removing hints. 1

Discovering itemsets and conjunctive rules under constraints are popular topics in the data mining and machine learning communities, for which many algorithms have been proposed. Despite the abundance of research in this area, however, constraint programming (CP) techniques developed in the artificial intelligence community to deal with constraint satisfaction problems have never been applied to rule discovery. In [4], we show that CP can not only be applied in an intuitive, extendible way to rule discovery, but also that CP techniques significantly outperform existing approaches in data mining. 1

The relationship between constraint-based mining and constraint programming is explored by showing how the typical constraints used in pattern mining can be formulated for use in constraint programming environments. The resulting framework is surprisingly flexible and allows us to combine a wide range of mining constraints in different ways. We implement this approach in off-the-shelf constraint programming systems and evaluate it empirically. The results show that the approach is not only very expressive, but also works well on complex benchmark problems.

In this dissertation I propose a shift in the foundations of computation. Modern programming systems are not expressive enough. The traditional image of a single computer that has global effects on a large memory is too restrictive. The propagation paradigm replaces this with computing by networks of local, independent, stateless machines interconnected with stateful storage cells. In so doing, it offers great flexibility and expressive power, and has therefore been much studied, but has not yet been tamed for general-purpose computation. The novel insight that should finally permit computing with general-purpose propagation is that a cell should not be seen as storing a value, but as accumulating information about a value. Various forms of the general idea of propagation have been used with great success for various special purposes; perhaps the most immediate example is constraint propagation in constraint satisfaction systems. This success is evidence both