Solving colored nonograms

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Solving Conflicting Beliefs with a Distributed Belief Revision Approach

The ability to solve conflicting beliefs is crucial for multi- agent systems where the information is dynamic, incomplete and dis- tributed over a group of autonomous agents. The proposed distributed belief revision approach consists of a distributed truth maintenance sy- stem and a set of autonomous belief revision methodologies. The agents have partial views and, frequently, hold disparate beliefs which are au- tomatically detected by system’s reason maintenance mechanism. The nature of these conflicts is dynamic and requires adequate methodolo- gies for conflict resolution. The two types of conflicting beliefs addressed in this paper are Context Dependent and Context Independent Conflicts which result, in the first case, from the assignment, by different agents, of opposite belief statuses to the same belief, and, in the latter case, from holding contradictory distinct beliefs. The belief revision methodology for solving Context Independent Con- flicts is, basically, a selection process based on the assessment of the cre- dibility of the opposing belief statuses. The belief revision methodology for solving Context Dependent Conflicts is, essentially, a search process for a consensual alternative based on a “next best” relaxation strategy.

Modern techniques for constraint solving the CASPER experience

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Efficient Legendre spectral tau algorithm for solving two-sided space-time Caputo fractional advection-dispersion equation

In this paper we present the operational matrices of the left Caputo fractional derivative, right Caputo fractional derivative and Riemann–Liouville fractional integral for shifted Legendre polynomials. We develop an accurate numerical algorithm to solve the two-sided space–time fractional advection–dispersion equation (FADE) based on a spectral shifted Legendre tau (SLT) method in combination with the derived shifted Legendre operational matrices. The fractional derivatives are described in the Caputo sense. We propose a spectral SLT method, both in temporal and spatial discretizations for the two-sided space–time FADE. This technique reduces the two-sided space–time FADE to a system of algebraic equations that simplifies the problem. Numerical results carried out to confirm the spectral accuracy and efficiency of the proposed algorithm. By selecting relatively few Legendre polynomial degrees, we are able to get very accurate approximations, demonstrating the utility of the new approach over other numerical methods.

An efficient numerical scheme for solving multi-dimensional fractional optimal control problems with a quadratic performance index

The shifted Legendre orthogonal polynomials are used for the numerical solution of a new formulation for the multi-dimensional fractional optimal control problem (M-DFOCP) with a quadratic performance index. The fractional derivatives are described in the Caputo sense. The Lagrange multiplier method for the constrained extremum and the operational matrix of fractional integrals are used together with the help of the properties of the shifted Legendre orthonormal polynomials. The method reduces the M-DFOCP to a simpler problem that consists of solving a system of algebraic equations. For confirming the efficiency and accuracy of the proposed scheme, some test problems are implemented with their approximate solutions.

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

A firefly dynamic penalty approach for solving engineering design problems

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

Comprensión en resolución de problemas en Física: niveles de representación e inferencias basadas en el conocimiento. Comprehension in Physics Problem-Solving: levels of representation and knowledge-based inferences.

Se propone desarrollar e integrar estudios sobre Modelado y Resolución de Problemas en Física que asumen como factores explicativos: características de la situación planteada, conocimiento de la persona que resuelve y proceso puesto en juego durante la resolución. Interesa comprender cómo los estudiantes acceden al conocimiento previo, qué procedimientos usan para recuperar algunos conocimientos y desechar otros, cuáles son los criterios que dan coherencia a sus decisiones, cómo se relacionan estas decisiones con algunas características de la tarea, entre otras. Todo ello con miras a estudiar relaciones causales entre las dificultades encontradas y el retraso o abandono en las carreras.Se propone organizar el trabajo en tres ejes, los dos primeros de construcción teórica y un tercero de implementación y transferencia. Se pretende.1.-Estudiar los procesos de construcción de las representaciones mentales en resolución de problemas de física, tanto en expertos como en estudiantes de diferentes niveles académicos.2.-Analizar y clasificar las inferencias que se producen durante las tareas de comprensión en resolución de problemas de física. Asociar dichas inferencias con procesos de transición entre representaciones mentales de diferente naturaleza.3.-Desarrollar materiales y diseños instruccionales en la enseñanza de la Física, fundamentado en un conocimiento de los requerimientos psicológicos de los estudiantes en diversas tareas de aprendizaje.En términos generales se plantea un enfoque interpretativo a la luz de marcos de la psicología cognitiva y de los desarrollos propios del grupo. Se trabajará con muestras intencionales de alumnos y profesores de física. Se utilizarán protocolos verbales y registros escritos producidos durante la ejecución de las tareas con el fin de identificar indicadores de comprensión, inferencias, y diferentes niveles de representación. Se prevé analizar material escrito de circulación corriente sea comercial o preparado por los docentes de las carreras involucradas.Las características del objeto de estudio y el distinto nivel de desarrollo en que se encuentran los diferentes ojetivos específicos llevan a que el abordaje contemple -según consideracion de Juni y Urbano (2006)- tanto la lógica cualitativa como la cuantitativa.

Robust learning in safety-related domains : machine learning methods for solving safety-related application problems

Magdeburg, Univ., Fak. für Informatik, Diss., 2009

An Empirical Examination of the R&D Boundaries of the Firm - A Problem-Solving Perspective

We consider, both theoretically and empirically, how different organization modes are aligned to govern the efficient solving of technological problems. The data set is a sample from the Chinese consumer electronics industry. Following mainly the problem solving perspective (PSP) within the knowledge based view (KBV), we develop and test several PSP and KBV hypotheses, in conjunction with competing transaction cost economics (TCE) alternatives, in an examination of the determinants of the R&D organization mode. The results show that a firm’s existing knowledge base is the single most important explanatory variable. Problem complexity and decomposability are also found to be important, consistent with the theoretical predictions of the PSP, but it is suggested that these two dimensions need to be treated as separate variables. TCE hypotheses also receive some support, but the estimation results seem more supportive of the PSP and the KBV than the TCE.

Relaxation methods for solving linear inequality systems: Converging results

The problem of finding a feasible solution to a linear inequality system arises in numerous contexts. In [12] an algorithm, called extended relaxation method, that solves the feasibility problem, has been proposed by the authors. Convergence of the algorithm has been proven. In this paper, we onsider a class of extended relaxation methods depending on a parameter and prove their convergence. Numerical experiments have been provided, as well.

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases.

When solving 22-7 is much more difficult than 99-12.

We describe the case of a 69-year-old professor of mathematics (GV) who was examined 2 years after left-hemispheric capsular-thalamic haemorrhage. GV showed disproportionate impairment in subtractions requiring borrowing (22 - 7). For large subtraction problems without borrowing (99 - 12) performance was almost flawless. Subtractions with borrowing mostly relied on inadequate attempts to invert subtractions into the corresponding additions (22 - 7 = x as 7 + x = 22). The hypothesis is advanced that difficulty in the inhibitory components of attention tasks (Stroop test, go-no-go task) might be the responsible factor of his calculation impairment. A deficit in subtractions with borrowing might be related to left-hemispheric damage involving thalamo-cortical connections.

The association between need for touch and desire for unique products and consumer (inter)dependent problem-solving

Some people cannot buy products without first touching them, believing that doing so will create more assurance and information and reduce uncertainty. The international consumer marketing literature suggests an instrument to measure consumers' necessity for pohysical contact, called Need for Touch (NFT). This paper analyzes whether the Need for Touch structure is empirically consistent. Based on a literature review, we suggest six hypotheses in order to assess the nomological, convergent, and discriminant validity of the phenomenon. Departing from these, data supported four assumptions in the predicted direction. Need for Touch was associated with Need for Input and with Need for Cognition. Need for Touch was not associated with traditional marketing channels. The results also showed the dual characterization of Need for Touch as a bi-dimensional construct. The moderator effect indicated that when the consumer has a higher (vs. lower) Need for Touch autotelic score, the experiential motivation for shopping played a more (vs. less) important role in impulsive motivation. Our Study 3 supports the NFT structure and shows new associations with the need for unique products and dependent decisions.