Recent research has shown that it is often preferable to encode real-world problems as propositional satisfiability (SAT) problems and then solve using a general purpose SAT solver. However, much of the valuable information and structure of these realistic problems is flattened out and hidden inside the corresponding Conjunctive Normal Form (CNF) encodings of the SAT domain. Recently, systematic SAT solvers have been progressively improved and are now able to solve many highly structured practical problems containing millions of clauses. In contrast, state-of-the-art Stochastic Local Search (SLS) solvers still have difficulty in solving structured problems, apparently because they are unable to exploit hidden structure as well as the systematic solvers. In this thesis, we study and evaluate different ways to effectively recognise, model and efficiently exploit useful structures hidden in realistic problems. A summary of the main contributions is as follows: 1. We first investigate an off-line processing phase that applies resolution-based pre-processors to input formulas before running SLS solvers on these problems. We report an extensive empirical examination of the impact of SAT pre-processing on the performance of contemporary SLS techniques. It emerges that while all the solvers examined do indeed benefit from pre-processing, the effects of different pre-processors are far from uniform across solvers and across problems. Our results suggest that SLS solvers need to be equipped with multiple pre-processors if they are ever to match the performance of systematic solvers on highly structured problems. [Part of this study was published at the AAAI-05 conference]. 2. We then look at potential approaches to bridging the gap between SAT and constraint satisfaction problem (CSP) formalisms. One approach has been to develop a many-valued SAT formalism (MV-SAT) as an intermediate paradigm between SAT and CSP, and then to translate existing highly efficient SAT solvers to the MV-SAT domain. In this study, we follow a different route, developing SAT solvers that can automatically recognise CSP structure hidden in SAT encodings. This allows us to look more closely at how constraint weighting can be implemented in the SAT and CSP domains. Our experimental results show that a SAT-based mechanism to handle weights, together with a CSP-based method to instantiate variables, is superior to other combinations of SAT and CSP-based approaches. In addition, SLS solvers based on this many-valued weighting approach outperform other existing approaches to handle many-valued CSP structures. [Part of this study was published at the AAAI-05 conference]. 3. Finally, we propose and evaluate six different schemes to encode temporal reasoning problems, in particular the Interval Algebra (IA) networks, into SAT CNF formulas. We then empirically examine the performance of local search as well as systematic solvers on the new temporal SAT representations, in comparison with solvers that operate on native IA representations. Our empirical results show that zChaff (a state-of-the-art complete SAT solver) together with the best IA-to-SAT encoding scheme, can solve temporal problems significantly faster than existing IA solvers working on the equivalent native IA networks. [Part of this study was published at the CP-05 workshop].

One of the challenges for the constraint satisfaction community has been to develop an automated approach to solving Constraint Satisfaction Problems (CSPs) rather than creating specific algorithms for specific problems. Much of this work has concentrated on the development and improvement of general purpose backtracking techniques. However, the success of relatively simple local search techniques on larger satisfiability problems [Selman et a!. 1992] and CSPs such as the n-queens [Minton et al. 1992] has caused interest in applying local search to constraint satisfaction. In this thesis we look at the usefulness of constraint weighting as a local search technique for constraint satisfaction. The work is based on the clause weighting ideas of Selman and Kautz [1993] and Moths [1993] and applies, evaluates and extends these ideas from the satisfiability domain to the more general domain of CSPs. Specifically, the contributions of the thesis are: 1. The introduction of a local search taxonomy. We examine the various better known local search techniques and recognise four basic strategies: restart, randomness, memory and weighting. 2. The extension of the CSP modelling framework. In order to represent and efficiently solve more realistic problems we extend the C SP modelling framework to include array-based domains and array-based domain use constraints. 3. The empirical evaluation of constraint weighting. We compare the performance of three constraint weighting strategies on a range of CSP and satisflability problems and with several other local search techniques. We find that no one technique dominates in all problem domains. 4. The characterisation of constraint weighting performance. Based on our empirical study we identiIS' the weighting behaviours and problem features that favour constrtt weighting. We conclude weighting does better on structured problems where the algorithm can recognise a harder sub-group of constraints. 5. The extension of constraint weighting. We introduce an efficient arc weighting algorithm that additionally weights connections between constraints that are simultaneously violated at a local minimum. This algorithm is empirically shown to outperform standard constraint weighting on a range of CSPs and within a general constraint solving system. Also we look at combining constraint weighting with other local search heuristics and find that these hybrid techniques can do well on problems where the parent algorithms are evenly matched. 6. The application of constraint weighting to over constrained domains. Our empirical work suggests constraint weighting does well for problems with distinctions between constraint groups. This led us to investigate solving real-world over constrained problems with hard and soft constraint groups and to introduce two dynamic constraint weighting heuristics that maintain a distinction between hard and soft constraint groups while still adding weights to violated constraints in a local minimum. In an empirical study, the dynamic schemes are shown to outperform other fixed weighting and non-weighting systems on a range of real world problems. In addition, the performance of weighting is shown to degrade less severely when soft constraints are added to the system, suggesting constraint weighting is especially applicable to realistic, hard and soft constraint problems

<p>Many problems in business, industry, and academia can be modelled as constraint programs consisting of matrices of decision variables. Such “matrix models” often have symmetry. In particular, they often have row and column symmetry as the rows and columns can freely be permuted without affecting the satisfiability of assignments. Existing methods have difficulty in dealing with the super-exponential number of symmetries in a problem with row and column symmetry. We therefore propose some ordering constraints which can effectively break such symmetries. To use these constraints in practice, we develop some efficient linear time propagators. We demonstrate the effectiveness of these symmetry breaking ordering constraints on a wide range of problems. We also show how such ordering constraints can be used to deal with partial symmetries, as well as value symmetries.</p>

This thesis presents a new approach for automated test data generation of imperative programs containing integer, boolean and/or float variables. A test program (with procedure calls) is represented by an Interprocedural Control Flow Graph (ICFG). The classical testing criteria (statement, branch, and path coverage), widely used in unit testing, are extended to the ICFG. Path coverage is the core of our approach. Given a specified path of the ICFG, a path constraint is derived and solved to obtain a test case. The constraint solving is carried out based on a consistency notion. For statement (and branch) coverage, paths reaching a specified node or branch are dynamically constructed. The search for suitable paths is guided by the interprocedural control dependences of the program. The search is also pruned by our consistency filter. Finally, test data are generated by the application of the proposed path coverage algorithm. A prototype system implements our approach for C programs. Experimental results, including complex numerical programs, demonstrate the feasibility of the method and the efficiency of the system, as well as its versatility and flexibility to different classes of problems (integer and/or float variables; arrays, procedures, path coverage, statement coverage).

This thesis presents a new approach for automated test data generation of imperative programs containing integer, boolean and/or float variables. A test program (with procedure calls) is represented by an Interprocedural Control Flow Graph (ICFG). The classical testing criteria (statement, branch, and path coverage), widely used in unit testing, are extended to the ICFG. Path coverage is the core of our approach. Given a specified path of the ICFG, a path constraint is derived and solved to obtain a test case. The constraint solving is carried out based on a consistency notion. For statement (and branch) coverage, paths reaching a specified node or branch are dynamically constructed. The search for suitable paths is guided by the interprocedural control dependences of the program. The search is also pruned by our consistency filter. Finally, test data are generated by the application of the proposed path coverage algorithm. A prototype system implements our approach for C programs. Experimental results, including complex numerical programs, demonstrate the feasibility of the method and the efficiency of the system, as well as its versatility and flexibility to different classes of problems (integer and/or float variables; arrays, procedures, path coverage, statement coverage).

Many problems in business, industry, and academia can be modelled as constraint programs consisting of matrices of decision variables. Such “matrix models” often have symmetry. In particular, they often have row and column symmetry as the rows and columns can freely be permuted without affecting the satisfiability of assignments. Existing methods have difficulty in dealing with the super-exponential number of symmetries in a problem with row and column symmetry. We therefore propose some ordering constraints which can effectively break such symmetries. To use these constraints in practice, we develop some efficient linear time propagators. We demonstrate the effectiveness of these symmetry breaking ordering constraints on a wide range of problems. We also show how such ordering constraints can be used to deal with partial symmetries, as well as value symmetries.

Pervasive computing implies the invisibility of the technology involved in providing ubiquity, such that technology is integrated into the environment and non-intrusive. In such a manner, computing and networking resources become diffused into physical environments, enabling users to exploit their provided functionalities such that functionality is distributed, enabling it to be controlled, monitored, managed, and extended beyond what it was initially designed to do. Moreover, computer awareness moves towards user-centricity, whereby systems seamlessly adapt to the characteristics, preferences, and current situations of users and their respective surrounding environments. Users exploit such functionalities in the form of a virtual device, whereby a collection of heterogeneous devices in the vicinity of the user are behaving as one single homogeneous device for the benefit of the user in solving some given task. This dissertation investigates the problem of dynamic composition and management of virtual devices for ad hoc multimedia service delivery and proposes an autonomous policy driven framework for virtual device management. The framework consists of a hierarchical structure of distributed elements, including autonomic elements, all working towards the self-management of virtual devices. The research presented in this dissertation addresses the functionalities of these components. More specifically, contributions are made towards the autonomous management of virtual devices, moving away from infrastructure based schemes with heavy user involvement to decentralized and zero touch (i.e., no user involvement) solutions. In doing so, the components and methodology behind a policy-driven autonomous framework for the dynamic discovery, selection, and composition of multimodal multi-device services are presented. The framework operates in an ad hoc network setting and introduces a Service Overlay Network (SON) based definition of a virtual device. Furthermore, device and service discovery, composition, integration, and adaptation schemes are designed for Mobile Ad hoc Network Environments (MANETs) enabling users to generate, on-the-fly, complex strong specific systems, embedding in a distributed manner, QoS models providing compositions that form the best possible virtual device at the time of need. Experimental studies are presented to demonstrate the performance of the proposed schemes.

Pervasive computing implies the invisibility of the technology involved in providing ubiquity, such that technology is integrated into the environment and non-intrusive. In such a manner, computing and networking resources become diffused into physical environments, enabling users to exploit their provided functionalities such that functionality is distributed, enabling it to be controlled, monitored, managed, and extended beyond what it was initially designed to do. Moreover, computer awareness moves towards user-centricity, whereby systems seamlessly adapt to the characteristics, preferences, and current situations of users and their respective surrounding environments. Users exploit such functionalities in the form of a virtual device, whereby a collection of heterogeneous devices in the vicinity of the user are behaving as one single homogeneous device for the benefit of the user in solving some given task. This dissertation investigates the problem of dynamic composition and management of virtual devices for ad hoc multimedia service delivery and proposes an autonomous policy driven framework for virtual device management. The framework consists of a hierarchical structure of distributed elements, including autonomic elements, all working towards the self-management of virtual devices. The research presented in this dissertation addresses the functionalities of these components. More specifically, contributions are made towards the autonomous management of virtual devices, moving away from infrastructure based schemes with heavy user involvement to decentralized and zero touch (i.e., no user involvement) solutions. In doing so, the components and methodology behind a policy-driven autonomous framework for the dynamic discovery, selection, and composition of multimodal multi-device services are presented. The framework operates in an ad hoc network setting and introduces a Service Overlay Network (SON) based definition of a virtual device. Furthermore, device and service discovery, composition, integration, and adaptation schemes are designed for Mobile Ad hoc Network Environments (MANETs) enabling users to generate, on-the-fly, complex strong specific systems, embedding in a distributed manner, QoS models providing compositions that form the best possible virtual device at the time of need. Experimental studies are presented to demonstrate the performance of the proposed schemes.

This dissertation explores approaches to the satisfiability problem, focusing on local search methods. The research endeavours to better understand how and why some local search methods are effective. At the root of this understanding are a set of metrics that characterize the behaviour of local search methods. Based on this understanding, two new local search methods are proposed and tested, the first, SDF, demonstrating the value of the insights drawn from the metrics, and the second, ESG, achieving state-of-the-art performance and generalizing the approach to arbitrary 0-1 integer linear programming problems. This generality is demonstrated by applying ESG to combinatorial auction winner determination. Further augmentations to local search are proposed and examined, exploring hybrids that incorporate aspects of backtrack search methods.

Monitoring and debugging distributed systems is inherently a difficult problem. Events collected during the execution of distributed systems can enable developers to diagnose and fix faults. Process-time diagrams are normally used to view the relationships between the events and understand the interaction between processes over time. A major difficulty with analyzing these sets of events is that they are usually very large. Therefore, being able to search through the event-data sets can enable users to get to points of interest quickly and find out if patterns in the dataset represent the expected behaviour of the system. A lot of research work has been done to improve the search algorithm for finding event-patterns in large partial-order datasets. In this thesis, we improve on this work by parallelizing the search algorithm. This is useful as many computers these days have more than one core or processor. Therefore, it makes sense to exploit this available computing power as part of an effort to improve the speed of the algorithm. The search problem itself can be modeled as a Constraint Satisfaction Problem (CSP). We develop a simple and efficient way of generating tasks (to be executed by the cores) that guarantees that no two cores will ever repeat the same work-effort during the search. Our approach is generic and can be applied to any CSP consisting of a large domain space. We also implement an efficient dynamic work-stealing strategy that ensures the cores are kept busy throughout the execution of the parallel algorithm. We evaluate the efficiency and scalability of our algorithm through experiments and show that we can achieve efficiencies of up to 80% on a 24-core machine.