The purpose of this study was to examine the efficacy of DIBELS Nonsense Word Fluency Scores in the fall of first grade as a predictor of SAT-10 results. A comparison of boys and girls, three ethnic groups (Caucasian, Hispanic, African-American), and three different reading risk groups were examined using multiple regression analyses. Analysis of data from a total of 27,000 participants from a cohort of Reading First schools in 2003/2004 confirmed Nonsense Word Fluency scores in the fall of first grade to be a significant predictor of the SAT-10 reading scores in the spring. Differences found between and within groups were determined very small when Cohen's effect size was calculated. These results support for the use of Nonsense Word Fluency as a valid and useful early literacy assessment tool for determining which children likely need early additional reading instructional support in order to be successful readers.

At school, students are provided numerous opportunities to use their skills and abilities to complete tasks or solve problems. Students are considered to have academic success when they meet specific criteria on outcomes such as grade point averages (GPA), scores on standardized tests, and skill acquisition across areas like reading and math. Given the importance of academic achievement (AA) as an outcome measure, researchers have attempted to study certain variables that may relate to or predict AA. Extracurricular activities (EAs) are defined as school-sanctioned activities that students can participate in outside of the traditional school day. Participation in EAs has been associated with several benefits to students, including higher AA, noncognitive skills, and transferable skills. A comprehensive review was conducted to examine the literature on EA participation and academic performance as measured by various AA variables including the American College Test (ACT), Scholastic Aptitude Test (SAT), and GPA. Results of the study indicated that students participating in EAs, regardless of type, benefited academically compared to non-participants. AA declined for students who participated in more than two EAs. However, this project should not take the place of well controlled, empirical studies. Implications of these findings and future directions are discussed.

The propositional satisfiability (SAT) problem is of considerable theoretical and practical relevance to the artificial intelligence (AI) community and has been used to model many pervasive AI tasks such as default reasoning, diagnosis, planning, image interpretation, and constraint satisfaction. Computational methods for SAT have historically fallen into two broad categories: complete search and local search. Within the local search category, clause weighting methods are amongst the best alternatives for SAT, becoming particularly attractive on problems where a complete search is impractical or where there is a need to find good candidate solutions within a short time. The thesis is concerned with the study of improvements to clause weighting local search methods for SAT. The main contributions are: A component-based framework for the functional analysis of local search methods. A clause weighting local search heuristic that exploits longer-term memory arising from clause weight manipulations. The approach first learns which clauses are globally hardest to satisfy and then uses this information to treat these clauses differentially during weight manipulation [Ferreira Jr and Thornton, 2004]. A study of heuristic tie breaking in the domain of additive clause weighting local search methods, and the introduction of a competitive method that uses heuristic tie breaking instead of the random tie breaking approach used in most existing methods [Ferreira Jr and Thornton, 2005]. An evaluation of backbone guidance for clause weighting local search, and the introduction of backbone guidance to three state-of-the-art clause weighting local search methods [Ferreira Jr, 2006]. A new clause weighting local search method for SAT that successfully exploits synergies between the longer-term memory and tie breaking heuristics developed in the thesis to significantly improve on the performance of current state-of-the-art local search methods for SAT-encoded instances containing identifiable CSP structure. Portions of this thesis have appeared in the following refereed publications: Longer-term memory in clause weighting local search for SAT. In Proceedings of the 17th Australian Joint Conference on Artificial Intelligence, volume 3339 of Lecture Notes in Artificial Intelligence, pages 730-741, Cairns, Australia, 2004. Tie breaking in clause weighting local search for SAT. In Proceedings of the 18th Australian Joint Conference on Artificial Intelligence, volume 3809 of Lecture Notes in Artificial Intelligence, pages 70–81, Sydney, Australia, 2005. Backbone guided dynamic local search for propositional satisfiability. In Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics, AI&M, Fort Lauderdale, Florida, 2006.

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].

In this dissertation we investigate theoretical aspects of some practical approaches used in solving and understanding search problems. We concentrate on the Satisfiability problem, which is a strong representative from search problem domains. The work develops general theoretical foundations to investigate some practical aspects of satisfiability search. This results in a better understanding of the fundamental mechanics for search algorithm construction and behaviour. A theory of choice or branching heuristics is presented, accompanied by results showing a correspondence of both parameterisations and performance when the method is compared to previous empirically motivated branching techniques. The logical foundations of the backtracking mechanism are explored alongside formulations for reasoning in relevant logics which results in the development of a malleable backtracking mechanism that subsumes other intelligent backtracking proof construction techniques and allows the incorporation of proof rearrangement strategies. Moreover, empirical tests show that relevant backtracking outperforms all other forms of intelligent backtracking search tree construction methods. An investigation into modelling and generating world problem instances justifies a modularised problem model proposal which is used experimentally to highlight the practicability of search algorithms for the proposed model and related domains.

Cette thèse s'inscrit dans le cadre de la planification de tâches en intelligence artificielle. Après avoir introduit le domaine et les principaux algorithmes de planification dans le cadre classique, nous présentons un état de l'art de la planification SAT. Nous analysons en détail cette approche qui permet de bénéficier directement des améliorations apportées régulièrement aux solveurs SAT. Nous proposons de nouveaux codages qui intègrent une stratégie de moindre engagement en retardant le plus possible l'ordonnancement des actions. Nous présentons ensuite le système TSP que nous avons implémenté pour comparer équitablement les différents codages puis nous détaillons les résultats de nombreux tests expérimentaux qui démontrent la supériorité de nos codages par rapport aux codages existants. Nous présentons ensuite un état de l'art de la planification temporelle en analysant les algorithmes et l'expressivité de leurs langages de représentation. La très grande majorité de ces planificateurs ne permet pas de résoudre des problèmes réels pour lesquels la concurrence des actions est nécessaire. Nous détaillons alors les deux approches originales de notre système TLP-GP permettant de résoudre ce type de problèmes. Ces approches sont comparables à la planification SAT, une grande partie du travail de recherche étant déléguée à un solveur SMT. Nous proposons ensuite des extensions du langage de planification PDDL qui permettent une certaine prise en compte de l'incertitude, du choix, ou des transitions continues. Nous montrons enfin, grâce à une étude expérimentale, que nos algorithmes permettent de résoudre des problèmes réels nécessitant de nombreuses actions concurrentes.

The goal of this thesis is the development of support for test generation and animation based on object-oriented specifications. We aim particularly to take advantage of state-of-the-art satisfiability solving techniques by using an appropriate representation of object-oriented data. While automated test generation seeks a large set of data to execute an implementation on, animation performs computations that comply with a specification based on user-provided input data. Animation is a valuable technique for validating specifications.As a foundation of this work, we present clarifications and a partial formalization of the Object Constraint Language (OCL) as well as some extensions in order to allow for test generation and animation based on OCL specifications.For test generation, we have implemented several enhancements to HOL-TestGen, a tool built on top of the Isabelle theorem proving system that generates tests from specifications in Higher-Order Logic (HOL). We show how SMT solvers can be used to solve various types of constraints in HOL and present a modular approach to case splitting for deriving test cases. The latter facilitates the introduction of splitting rules that are tailored to object-oriented specifications.For animation, we implemented the tool OCLexec for animating OCL specifications. OCLexec generates from operation contracts corresponding Java implementations that call an SMT-based constraint solver at runtime.

On Computer Science research, traditionally, most efforts have been devoted to research hardness for the worst case of problems (proving NP completeness and comparing and reducing problems between them are the two most known). Artifcial Intelligence research, recently, has focused also on how some characteristics of concrete instances have dramatic effects on complexity and hardness while worst-case complexity remains the same. This has lead to focus research efforts on understanding which aspects and properties of problems or instances affect hardness, why very similar problems can require very diferent times to be solved. Research search based problems has been a substantial part of artificial intelligence research since its beginning. Big part of this research has been focused on developing faster and faster algorithms, better heuristics, new pruning techniques to solve ever harder problems. One aspect of this effort to create better solvers consists on benchmarking solver performance on selected problem sets, and, an, obviously, important part of that benchmarking is creating and defining new sets of hard problems. This two folded effort, on one hand to have at our disposal new problems, harder than previous ones, to test our solvers, and on the other hand, to obtain a deeper understanding on why such new problems are so hard, thus making easier to understand why some solvers outperform others, knowledge that can contribute towards designing and building better and faster algorithms and solvers. This work deals with designing better, that is harder and easy to generate, problems for CSP solvers, also usable for SAT solvers. In the first half of the work general concepts on hardness and CSP are introduced, including a complete description of the chosen problems for our study. This chosen problems are, Random Binary CSP Problems (BCSP), Quasi-group Completion Problems (QCP), Generalised Sudoku Problems (GSP), and a newly defined problem, Edge-Matching Puzzles (GEMP). Although BCSP and QCP are already well studied problems, that is not the case with GSP and GEMP. For GSP we will define new creation methods that ensure higher hardness than standard random methods. GEMP on the other hand is a newly formalised problem, we will define it, will provide also algorithms to build easily problems of tunable hardness and study its complexity and hardness. On the second part of the work we will propose and study new methods to increase the hardness of such problems. Providing both, algorithms to build harder problems and an in-depth study of the effect of such methods on hardness, specially on resolution time.

No description available.

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models.