Game outcomes are generally thought to be the primary maintaining variables in gambling behavior. The influence of language and other verbal behaviors have been shown to moderate subsequent game play. The purpose of this study is to measure the behavioral sensitivity to changing contingencies when rules are given or self-generated compared to those that are not given rules. To date no study has compared contingency based trial and error learning, externally delivered rules and contextually cued responding when measuring behavioral sensitivity to changing contingencies in the context of roulette. An analysis of such behavior will shed light on the cause and effect relationships that describe gambling. Twenty-eight undergraduate and graduate students were recruited to play a game of simulated roulette with varying probabilities for each game option. Participants were assigned to 3 groups and were either given rules regarding game play, were not given rules or asked to complete a relational training exercise. Results demonstrated varying mean response allocation for each group and game option. Those who were given rules or who completed a relational training procedure demonstrated more flexible responding when confronted with changing contingencies.

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The topological entropy of the subdivision map of a finite subdivision rule restricted to the 1-skeleton of its model subdivision complex, which we call textbf{core entropy}, is examined. We consider core entropy for finite subdivision rules realizing quadratic Misiurewicz polynomials and matings of such polynomials. It is shown that for a non-restrictive class of finite subdivision rules realizing quadratic Misiurewicz polynomials, core entropy equals Thurston's core entropy. We also show that the core entropy of formal and degenerate matings of Misiurewicz polynomials is determined by Thurston's core entropy of the mated polynomials. / Doctor of Philosophy / Imagine taking a programmable calculator, inputting a number, and repeatedly pushing one of the buttons which corresponds to one of the calculator's built-in functions. For example, starting by inputting 0.5 and hitting the "x2" button over and over, or starting with 1.47 and repeatedly pressing the "sin(x)" button. The calculator may eventually return numbers that get closer and closer to a specific value, it may repeatedly cycle through some collection of specific numbers, it may not exhibit a clear pattern at all. It is of interest to understand, in some average sense, when, how often, and in what manner these patterns are exhibited and, in a quantitative fashion, compare how complicated the patterns are for different buttons on the calculator corresponding to different functions. For example, is the "x2" button, in some average sense, more or less "complex", in terms of the patterns exhibited by the above procedure, than the "sin(x)" button? Modeling or simulating physical phenomena such as particle motion or the orbits of collections of celestial bodies often entails the use of computer programs. These computer programs carry out calculations which often involves repeated application of various pre-programmed functions. Repeatedly pushing a button on a calculator can be viewed as a simplified version of what goes on with the calculations that a computer carries out in simulating physical phenomena. Understanding how to compare the patterns exhibited by simple, fundamental collections of functions makes for a good starting point for understanding the models that represent various physical phenomena. This work contributes to this endeavor by investigating a quantity which measures the complexity of some fundamental functions.

A implementação das regras de negócio em sistemas de informação não é uma atividade trivial. Isso acontece porque os modelos utilizados para representá-las não determinam como elas podem ser efetivamente implementadas em situações reais. Neste trabalho é proposta um método para a transformação das regras de negócio descritas em SBVR para um modelo de implementação. Essa tradução é feita mapeando todos os elementos relevantes do modelo em SBVR em um modelo em $\\pi$-calculus baseado em eventos. Neste modelo, existe uma representação explícita de todos os eventos que precisam ser monitorados a fim de identificar quando a regra pode ter sido violada. O objetivo é facilitar o gerenciamento das regras de negócio ao permitir que elas permaneçam separadas dos outros elementos da arquitetura e com isso possam ser gerenciadas de forma independente. / The implementation of business rules in an information system is not an easy task. This happens because most of the models used to represent them capture only the meaning of the rules and do not provide means to make them to be effectively implemented. In this work we provide an approach for the translation from a SBVR semantic description of business rules into a implementation model. This was done by mapping all relevant elements of the SBVR semantic representation into a $\\pi$-calculus event-driven model. On this model will be described explicitly all the events that must be monitored in order to identify when some rule may have been violated. This makes the business rule implementation easier because there is a explicit indication of the moment when make sense to do a verification in order to see if any rule was violated. Besides that, it easier the business rule management since the rules stay separate from the others elements of the system architecture and in this way can be managed independently.

The goal of data mining is to help market managers find relationships among items from large datasets to increase profits. Among the mining techniques, the Apriori algorithm is the most basic and important for association rule mining. Although a lot of mining approaches have been proposed based on the Apriori algorithm, most of them focus on positive association rules, such as R1: ¡§If milk is bought, then bread is bought¡¨. However, rule R1 may confuses users and makes wrong decision if the negative relation rules are not considered. For example, the rule such as R2: ¡§If milk is not bought, then bread is bought¡¨ may also be found. Then, the rule R2 conflicts with the positive rule R1. So, if two rules such as ¡§If milk is bought, then bread is bought¡¨ and ¡§If milk is not bought, then bread is not bought¡¨ are found at the same time, the rules which is called coherent rule may be more valuable.In this thesis, we thus propose two algorithms for solving this problem. The first proposed algorithm is named Highly Coherent Rule Mining algorithm (HCRM), which takes the properties of propositional logic into consideration and is based on Apriori approach for finding coherent rules. The lower and upper bounds of itemsets are also tightened to remove unnecessary check. Besides, in order to improve the efficiency of the mining process, the second algorithm, namely Projection-based Coherent Mining Algorithm (PCA), based on data projection is proposed for speeding up the execution time. Experiments are conducted on real and simulation datasets to demonstrate the performance of the proposed approaches and the results show that both HCRM and PCA can find more reliable rules and PCA is more efficient.

A implementação das regras de negócio em sistemas de informação não é uma atividade trivial. Isso acontece porque os modelos utilizados para representá-las não determinam como elas podem ser efetivamente implementadas em situações reais. Neste trabalho é proposta um método para a transformação das regras de negócio descritas em SBVR para um modelo de implementação. Essa tradução é feita mapeando todos os elementos relevantes do modelo em SBVR em um modelo em $\\pi$-calculus baseado em eventos. Neste modelo, existe uma representação explícita de todos os eventos que precisam ser monitorados a fim de identificar quando a regra pode ter sido violada. O objetivo é facilitar o gerenciamento das regras de negócio ao permitir que elas permaneçam separadas dos outros elementos da arquitetura e com isso possam ser gerenciadas de forma independente. / The implementation of business rules in an information system is not an easy task. This happens because most of the models used to represent them capture only the meaning of the rules and do not provide means to make them to be effectively implemented. In this work we provide an approach for the translation from a SBVR semantic description of business rules into a implementation model. This was done by mapping all relevant elements of the SBVR semantic representation into a $\\pi$-calculus event-driven model. On this model will be described explicitly all the events that must be monitored in order to identify when some rule may have been violated. This makes the business rule implementation easier because there is a explicit indication of the moment when make sense to do a verification in order to see if any rule was violated. Besides that, it easier the business rule management since the rules stay separate from the others elements of the system architecture and in this way can be managed independently.

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No description available.

A key problem in automating proof by mathematical induction is choosing an induction rule suitable for a given conjecture. Since Boyer & Moore’s NQTHM system the standard approach has been based on recursion analysis, which uses a combination of induction rules based on the relevant recursive function definitions. However, there are practical examples on which such techniques are known to fail. Recent research has tried to improve automation by delaying the choice of inductive rule until later in the proof, but these techniques suffer from two serious problems. Firstly, a lack of search control: specifically, in controlling the application of ‘speculative’ proof steps that partially commit to a choice of induction rule. Secondly, a lack of generality: they place significant restrictions on the form of induction rule that can be chosen. In this thesis we describe a new delayed commitment strategy for inductive proof that addresses these problems. The strategy dynamically creates an appropriate induction rule by proving schematic proof goals, where unknown rule structure is represented by meta-variables which become instantiated during the proof. This is accompanied by a proof that the generated rule is valid. The strategy achieves improved control over speculative proof steps via a novel speculation critic. It also generates a wider range of useful induction rules than other delayed commitment techniques, partly because it removes unnecessary restrictions on the individual proof cases, and partly because of a new technique for generating the rule’s overall case structure. The basic version of the strategy has been implemented using the lamdaClam proof planner. The system was extended with a novel proof critics architecture for this purpose. An evaluation shows the strategy is a useful and practical technique, and demonstrates its advantages.