Nesta tese, nós estudamos a classe de problemas de blocos-móveis. Um problema de blocos-móveis consiste em k blocos móveis dispostos em um labirinto em grade quadrangular onde há um bloco móvel adicional chamado de o homem, que é o único bloco que pode ser movido diretamente. Em particular, cada problema de blocos-móveis é definido pelo conjunto de movimentos disponíveis, pela descrição do objetivo e pelo o que acontece quando o homem tenta mover um bloco. Sokoban é o problema de blocos-móveis mais conhecido e pesquisado. Nós investigamos a complexidade computacional de problemas de blocos-móveis. Antes desta tese, a maior parte da literatura cientifica abordou problemas de blocos-móveis apenas com movimentos de EMPURRAR, na maioria dos casos provando que esses problemas são PSPACE-complete. Nós consideramos dois conjuntos de problemas: apenas movimentos de PUXAR, e movimentos de EMPURRAR e PUXAR combinados. Nossas reduções usam a Lógica de Restrições Não Determinística. Nós provamos que muitos problemas apenas com movimentos de PUXAR são PSPACE-complete. Além disso, nós provamos que o conjunto de problemas com movimentos de EMPURRAR e PUXAR é PSPACE-complete. A nossa contribuição nessa linha de pesquisa é aprimorar o conhecimento sobre o panorama da complexidade de problemas de blocos-móveis. Nosso principal objetivo com essa tese é resolver otimamente problemas de blocos-móveis com foco em Sokoban. Métodos baseados em busca heurística e heurísticas de abstrações como banco de dados de padrões são as abordagens mais efetivas para resolver otimamente esses problemas. Nós fazemos muitas contribuições nessa linha de pesquisa. Nós introduzimos novas funções heurísticas usando bancos de dados padrão com a ideia de estados objetivos intermediários. Propomos uma técnica baseada em bancos de dados padrão para detectar impasses. Propomos regras de desempate que exploram a estrutura do problema. Usando estas funções heurísticas e regras de desempate nós aumentamos o número de instâncias resolvidas de forma ótima de Sokoban e outros problemas em comparação com os métodos anteriores. / In this thesis, we study the class of moving-blocks problems. A moving-blocks problem consists of k movable blocks placed on a grid-square maze where there is an additional movable block called the man, which is the only block that can be moved directly. In particular, each moving-blocks problem is defined by the set of moves available, by the goal description and by what happens when the man attempts to move a block. Sokoban is the best known and researched moving-blocks problem. We study moving-blocks problems in theory and practice. We investigate the computational complexity of problems of moving-blocks. Prior to this thesis, most of the scientific literature addressed moving-blocks problems with PUSH moves only, in most of the cases proving that these problems are PSPACE-complete. We consider two sets of problems: PULL moves only, and PUSH and PULL moves combined. Our reductions are from Nondeterministic Constraint Logic. We prove that many problems with PULL moves only are PSPACE-complete. In addition, we prove that the entire set of PUSH and PULL moves is PSPACE-complete. Our contribution in this research line is to enhance the knowledge on the complexity landscape of moving-blocks problems. Our main objective in this thesis is to optimally solve moving-blocks problems with a focus on Sokoban. Methods based on heuristic search and abstraction heuristics such as pattern databases are the most effective approaches to optimally solve these problems. We make many contributions in this research line. We introduce novel heuristic functions using pattern databases with the idea of intermediate goal states. We propose a technique based on pattern databases to detect deadlocks. We propose tie-breaking rules that exploit the structure of the problem. Using these heuristic functions and tie-breaking rules we increase the number of optimally solved instances of Sokoban and other problems compared to previous methods.

Dinaminiai kelio paieškos algoritmai apjungia euristinės ir plečiamos (angl. incremental) paieškos metodus, sprendžiant eiles panašių paieškos uždavinių tiek žinant visą informaciją apie aplinką, tiek neturint jokios informacijos. Yra trys plečiamą paiešką naudojančių algoritmų klasės. Šiame darbe pateikiama trumpa dinaminių kelio paieškos algoritmų, naudojančių plečiamos paieškos metodus analizė. Pagrindinis darbo tikslas – visų trijų plečiamos paieškos klasių algoritmų, kuriuos naudojant gaunamas optimalus kelio paieškos sprendinys aplinkose, kur perėjimų tarp viršūnių svoriai gali didėti ir mažėti, palyginimas. Algoritmai lyginami trijose skirtingose situacijose: stacionarioje, judėjimo link tikslo (angl. goal – directed) bei judančio taikinio (angl. moving – target). Tyrimo rezultatai parodė, jog A* ir FSA* nepasiekiamų viršūnių kiekiui esant ~16000 yra ~23,6% našesni už GAA* ir trečios plečiamos klasės algoritmus, o pasiekiamumą keičiančių viršūnių kiekiui esant ~8000 – 42,3%. Nepasiekiamų viršūnių kiekiui kintant nuo 1000 iki 16000 trečios plečiamos klasės algoritmai yra vidutiniškai ~58,7% našesni už GAA* ir ~54% našesni už A* ir FSA*. Pasiekiamumą keičiančių viršūnių kiekiui kintant nuo 500 iki 8000 trečios plečiamos klasės algoritmai yra vidutiniškai ~69,3% našesni už GAA* ir ~47,8% našesni už A* ir FSA*. / Dynamic path finding algorithms combine heuristic and incremental search methods to solve a series of similar search tasks in both known and unknown environments. There are three classes of incremental search algorithms. In this document we provide a brief summary of dynamic path finding algorithms, that uses incremental search methods, but its main focus is on comparing main algorithms of all three incremental classes, that are guaranteed to give optimal solution in environment where action costs can increase and decrease over time, and showing their strong and weak sides. The algorithms are compared in three different situations: stationary, goal – directed and moving – target. At the end of the document conclusions are given based on performed work. In this paper, research showed that A* and FSA* are ~23,6% more efficient than GAA* and third incremental class algorithms when the amount of untraversable cells is ~16000 and ~42,3% more efficient when the amount of traversability changing cells is ~8000. When the amount of untraversable cells is between 1000 and 16000, the third incremental class algorithms are ~58,7% more efficient than GAA* and ~54% – than A* and FSA*. When the amount of traversability changing cells is between 500 and 8000, the third incremental class algorithms are ~69,3% more efficient than GAA* and ~47,8% – than A* and FSA*.

Artificial Intelligence Planning is about acting in order to achieve a desired goal. Under incomplete information, the task of finding the actions needed to achieve the goal can be modelled as a search problem in the belief space. This task is costly, as belief space is exponential in the number of states, which is exponential in the number of variables. Good belief representations and heuristics are thus critical for scaling up in this setting. The translation-based approach to automated planning with incomplete information deals with both issues by casting the problem of search in belief space to a search problem in state space, where each node of the search space represents a belief state. We develop plan synthesis tools that use translated versions of planning problems under uncertainty, with partial or null sensing available. We show formally under which conditions the introduced translations are polynomial, and capture all and only the plans of the original problems. We study empirically the value of these translations. / La Planificación es la disciplina de Inteligencia Artificial que estudia los procesos de razonamiento necesarios para conseguir las acciones que logren un objetivo dado. En presencia de información incompleta, el problema de planificación puede ser modelado como una búsqueda en el espacio de estados de creencia, cada uno de ellos representando un conjunto de estados posibles. Este problema es costoso ya que el numero de estados de creencia puede ser exponencial en el número de estados, lo cual es exponencial en el número de variables del problema. El uso de buenas representaciónes de los estados y de heurísticas informadas resultan cruciales para escalar en este espacio de búsqueda. En esta tesis se presentan traducciones para planificación con información incompleta, que transforman el problema de búsqueda en el espacio de estados de creencia, en búsqueda en espacio de estados, donde cada nodo representa un estado de creencia. Hemos desarrollado herramientas para la generación de planes para el problema traducido, ya sea con percepción parcial o nula. A su vez, demostramos formalmente bajo qué circunstancias las traducciones son polinómicas, completas y correctas. La evaluación empírica remarca el valor de dichas traducciones

Nesta tese, nós estudamos a classe de problemas de blocos-móveis. Um problema de blocos-móveis consiste em k blocos móveis dispostos em um labirinto em grade quadrangular onde há um bloco móvel adicional chamado de o homem, que é o único bloco que pode ser movido diretamente. Em particular, cada problema de blocos-móveis é definido pelo conjunto de movimentos disponíveis, pela descrição do objetivo e pelo o que acontece quando o homem tenta mover um bloco. Sokoban é o problema de blocos-móveis mais conhecido e pesquisado. Nós investigamos a complexidade computacional de problemas de blocos-móveis. Antes desta tese, a maior parte da literatura cientifica abordou problemas de blocos-móveis apenas com movimentos de EMPURRAR, na maioria dos casos provando que esses problemas são PSPACE-complete. Nós consideramos dois conjuntos de problemas: apenas movimentos de PUXAR, e movimentos de EMPURRAR e PUXAR combinados. Nossas reduções usam a Lógica de Restrições Não Determinística. Nós provamos que muitos problemas apenas com movimentos de PUXAR são PSPACE-complete. Além disso, nós provamos que o conjunto de problemas com movimentos de EMPURRAR e PUXAR é PSPACE-complete. A nossa contribuição nessa linha de pesquisa é aprimorar o conhecimento sobre o panorama da complexidade de problemas de blocos-móveis. Nosso principal objetivo com essa tese é resolver otimamente problemas de blocos-móveis com foco em Sokoban. Métodos baseados em busca heurística e heurísticas de abstrações como banco de dados de padrões são as abordagens mais efetivas para resolver otimamente esses problemas. Nós fazemos muitas contribuições nessa linha de pesquisa. Nós introduzimos novas funções heurísticas usando bancos de dados padrão com a ideia de estados objetivos intermediários. Propomos uma técnica baseada em bancos de dados padrão para detectar impasses. Propomos regras de desempate que exploram a estrutura do problema. Usando estas funções heurísticas e regras de desempate nós aumentamos o número de instâncias resolvidas de forma ótima de Sokoban e outros problemas em comparação com os métodos anteriores. / In this thesis, we study the class of moving-blocks problems. A moving-blocks problem consists of k movable blocks placed on a grid-square maze where there is an additional movable block called the man, which is the only block that can be moved directly. In particular, each moving-blocks problem is defined by the set of moves available, by the goal description and by what happens when the man attempts to move a block. Sokoban is the best known and researched moving-blocks problem. We study moving-blocks problems in theory and practice. We investigate the computational complexity of problems of moving-blocks. Prior to this thesis, most of the scientific literature addressed moving-blocks problems with PUSH moves only, in most of the cases proving that these problems are PSPACE-complete. We consider two sets of problems: PULL moves only, and PUSH and PULL moves combined. Our reductions are from Nondeterministic Constraint Logic. We prove that many problems with PULL moves only are PSPACE-complete. In addition, we prove that the entire set of PUSH and PULL moves is PSPACE-complete. Our contribution in this research line is to enhance the knowledge on the complexity landscape of moving-blocks problems. Our main objective in this thesis is to optimally solve moving-blocks problems with a focus on Sokoban. Methods based on heuristic search and abstraction heuristics such as pattern databases are the most effective approaches to optimally solve these problems. We make many contributions in this research line. We introduce novel heuristic functions using pattern databases with the idea of intermediate goal states. We propose a technique based on pattern databases to detect deadlocks. We propose tie-breaking rules that exploit the structure of the problem. Using these heuristic functions and tie-breaking rules we increase the number of optimally solved instances of Sokoban and other problems compared to previous methods.

Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with optimal results from the literature, and with GAOD, iMeme-Net, HAIN, BMD- heuristics. Our results are also compared with CNM and Louvain which are heuristics for Modularity Maximization that solve instances with thousands of nodes. The tests were carried out by using graphs from the “Stanford Large Network Dataset Collection”. The experiments have shown that our eight heuristics found solutions for graphs with hundreds of thousands of nodes. Our results have also shown that five of our heuristics surpassed the current state-of-the-art Modularity Density Maximization heuristic solvers for large graphs. A third contribution is the proposal of six column generation methods. These methods use exact and heuristic auxiliary solvers and an initial variable generator. Comparisons among our proposed column generations and state-of-the-art algorithms were also carried out. The results showed that: (i) two of our methods surpassed the state-of-the-art algorithms in terms of time, and (ii) our methods proved the optimal value for larger instances than current approaches can tackle. Our results suggest clear improvements to the state-of-the-art results for the Modularity Density Maximization problem.

Made available in DSpace on 2016-06-02T19:05:23Z (GMT). No. of bitstreams: 1 DissEGRM.pdf: 5921557 bytes, checksum: 89005165cd9d839d8283e0713abe1fb8 (MD5) Previous issue date: 2005-05-30 / Financiadora de Estudos e Projetos / The Petri Net based Search has been shown as a promising way to solve Flexible manufacturing Systems (FMS) Scheduling Problem. However, the response time is critical since it s a system with high computational complexity. Focusing the reduction of response time, this work proposes a heuristic for Petri Net based Search to solve FMS Scheduling problem of makespan minimization. Experiments showed improvements on response time reduction comparing with prior works / Abordagens de Busca baseadas em Rede de Petri (PN) têm sido mostradas como uma forma promissora de resolver o problema da Programação da Produção de Sistemas Flexíveis de Manufatura (FMS). Entretanto, o tempo de resposta é crítico, uma vez que se trata de um sistema de alta complexidade computacional. Focando a redução do tempo de resposta do sistema, este trabalho propõe uma heurística para busca baseada em Rede de Petri para resolver o problema de programação de FMS na minimização do makespan. Experimentos mostraram um avanço na melhoria do tempo de resposta em relação a trabalhos anteriores

Nesta tese, nós estudamos a classe de problemas de blocos-móveis. Um problema de blocos-móveis consiste em k blocos móveis dispostos em um labirinto em grade quadrangular onde há um bloco móvel adicional chamado de o homem, que é o único bloco que pode ser movido diretamente. Em particular, cada problema de blocos-móveis é definido pelo conjunto de movimentos disponíveis, pela descrição do objetivo e pelo o que acontece quando o homem tenta mover um bloco. Sokoban é o problema de blocos-móveis mais conhecido e pesquisado. Nós investigamos a complexidade computacional de problemas de blocos-móveis. Antes desta tese, a maior parte da literatura cientifica abordou problemas de blocos-móveis apenas com movimentos de EMPURRAR, na maioria dos casos provando que esses problemas são PSPACE-complete. Nós consideramos dois conjuntos de problemas: apenas movimentos de PUXAR, e movimentos de EMPURRAR e PUXAR combinados. Nossas reduções usam a Lógica de Restrições Não Determinística. Nós provamos que muitos problemas apenas com movimentos de PUXAR são PSPACE-complete. Além disso, nós provamos que o conjunto de problemas com movimentos de EMPURRAR e PUXAR é PSPACE-complete. A nossa contribuição nessa linha de pesquisa é aprimorar o conhecimento sobre o panorama da complexidade de problemas de blocos-móveis. Nosso principal objetivo com essa tese é resolver otimamente problemas de blocos-móveis com foco em Sokoban. Métodos baseados em busca heurística e heurísticas de abstrações como banco de dados de padrões são as abordagens mais efetivas para resolver otimamente esses problemas. Nós fazemos muitas contribuições nessa linha de pesquisa. Nós introduzimos novas funções heurísticas usando bancos de dados padrão com a ideia de estados objetivos intermediários. Propomos uma técnica baseada em bancos de dados padrão para detectar impasses. Propomos regras de desempate que exploram a estrutura do problema. Usando estas funções heurísticas e regras de desempate nós aumentamos o número de instâncias resolvidas de forma ótima de Sokoban e outros problemas em comparação com os métodos anteriores. / In this thesis, we study the class of moving-blocks problems. A moving-blocks problem consists of k movable blocks placed on a grid-square maze where there is an additional movable block called the man, which is the only block that can be moved directly. In particular, each moving-blocks problem is defined by the set of moves available, by the goal description and by what happens when the man attempts to move a block. Sokoban is the best known and researched moving-blocks problem. We study moving-blocks problems in theory and practice. We investigate the computational complexity of problems of moving-blocks. Prior to this thesis, most of the scientific literature addressed moving-blocks problems with PUSH moves only, in most of the cases proving that these problems are PSPACE-complete. We consider two sets of problems: PULL moves only, and PUSH and PULL moves combined. Our reductions are from Nondeterministic Constraint Logic. We prove that many problems with PULL moves only are PSPACE-complete. In addition, we prove that the entire set of PUSH and PULL moves is PSPACE-complete. Our contribution in this research line is to enhance the knowledge on the complexity landscape of moving-blocks problems. Our main objective in this thesis is to optimally solve moving-blocks problems with a focus on Sokoban. Methods based on heuristic search and abstraction heuristics such as pattern databases are the most effective approaches to optimally solve these problems. We make many contributions in this research line. We introduce novel heuristic functions using pattern databases with the idea of intermediate goal states. We propose a technique based on pattern databases to detect deadlocks. We propose tie-breaking rules that exploit the structure of the problem. Using these heuristic functions and tie-breaking rules we increase the number of optimally solved instances of Sokoban and other problems compared to previous methods.

Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with optimal results from the literature, and with GAOD, iMeme-Net, HAIN, BMD- heuristics. Our results are also compared with CNM and Louvain which are heuristics for Modularity Maximization that solve instances with thousands of nodes. The tests were carried out by using graphs from the “Stanford Large Network Dataset Collection”. The experiments have shown that our eight heuristics found solutions for graphs with hundreds of thousands of nodes. Our results have also shown that five of our heuristics surpassed the current state-of-the-art Modularity Density Maximization heuristic solvers for large graphs. A third contribution is the proposal of six column generation methods. These methods use exact and heuristic auxiliary solvers and an initial variable generator. Comparisons among our proposed column generations and state-of-the-art algorithms were also carried out. The results showed that: (i) two of our methods surpassed the state-of-the-art algorithms in terms of time, and (ii) our methods proved the optimal value for larger instances than current approaches can tackle. Our results suggest clear improvements to the state-of-the-art results for the Modularity Density Maximization problem.

Background: In computer science, optimization can be defined as finding the most cost-effective or notable achievable performance under certain circumstances, maximizing desired factors, and minimizing undesirable results. Many problems in the real world are continuous, and it isn't easy to find global solutions. However, computer technological development increases the speed of computations [1]. The optimization method, an efficient numerical simulator, and a realistic depiction of physical operations that we intend to describe and optimize for any optimization issue are all interconnected components of the optimization process [2]. Objectives: A literature review on existing optimization algorithms is performed. Ten different benchmark functions are considered and are implemented on the existing chosen algorithms like GA (Genetic Algorithm), ACO (Ant ColonyOptimization) Method, and Plant Intelligence Behaviour optimization algorithm to measure the efficiency of these approaches based on the factors or metrics like CPU Time, Optimality, Accuracy, and Mean Best Standard Deviation. Methods: In this research work, a mixed-method approach is used. A literature review is performed based on the existing optimization algorithms. On the other hand, an experiment is conducted by using ten different benchmark functions with the current optimization algorithms like PSO algorithm, ACO algorithm, GA, and PIBO to measure their efficiency based on the four different factors like CPU Time, Optimality, Accuracy, Mean Best Standard Deviation. This tells us which optimization algorithms perform better. Results: The experiment findings are represented within this section. Using the standard functions on the suggested method and other methods, the various metrics like CPU Time, Optimality, Accuracy, and Mean Best Standard Deviation are considered, and the results are tabulated. Graphs are made using the data obtained. Analysis and Discussion: The research questions are addressed based on the experiment's results that have been conducted. Conclusion: We finally conclude the research by analyzing the existing optimization methods and the algorithms' performance. The PIBO performs much better and can be depicted from the results of the optimal metrics, best mean, standard deviation, and accuracy, and has a significant drawback of CPU Time where its time taken is much higher when compared to the PSO algorithm and almost close to GA and performs much better than ACO algorithm.

Classical planning is the problem of nding a sequence of actions that take an agent from an initial state to a desired goal situation, assuming deter- ministic outcomes for actions and perfect information. Satis cing planning seeks to quickly nd low-cost solutions with no guarantees of optimality. The most e ective approach for satis cing planning has proved to be heuristic search using non-admissible heuristics. In this thesis, we introduce several such heuristics that are able to take into account costs on actions, and there- fore try to minimize the more general metric of cost, rather than length, of plans, and investigate their properties and performance. In addition, we show how the problem of planning with soft goals can be compiled into a classical planning problem with costs, a setting in which cost-sensitive heuristics such as those presented here are essential. / La plani caci on cl asica es el problema que consiste en hallar una secuencia de acciones que lleven a un agente desde un estado inicial a un objetivo, asum- iendo resultados determin sticos e informaci on completa. La plani caci on \satis cing" busca encontrar una soluci on de bajo coste, sin garant as de op- timalidad. La b usqueda heur stica guiada por heur sticas no admisibles es el enfoque que ha tenido mas exito. Esta tesis presenta varias heur sticas de ese g enero que consideran costes en las acciones, y por lo tanto encuentran soluciones que minimizan el coste, en lugar de la longitud del plan. Adem as, demostramos que el problema de plani caci on con \soft goals", u objetivos opcionales, se puede reducir a un problema de plani caci on clasica con costes en las acciones, escenario en el que heur sticas sensibles a costes, tal como las aqu presentadas, son esenciales.