Listar por autor "Pereira, André Grahl"
Mostrando ítems 1-18 de 18
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Análise de Algoritmos de Busca em Espaços de Estados com Memória Limitada
Schwartzhaupt, Frederico Messa (2021) [Resumen publicado en evento] -
Análise de diferentes funções heurísticas sobre variante do jogo Pacman
Santos, Thiago Trautwein (2022) [Resumen publicado en evento] -
Aprendendo a Detectar Estados Dead em Sokoban
Simon, Mateus Davi (2019) [Resumen publicado en evento] -
Domain dependent heuristics and tie breakers : topics in automated planning
Corrêa, Augusto Blaas (2018) [Tesinas de grado]Automated planning is an important general problem solving technique in Artificial Intelligence (AI). In planning, given a initial state of the world, a goal and a set of actions, we want to find a sequence of these actions ... -
Uma estratégia eficiente para a computação de heurísticas de abstração consistentes para Multi-Agent Path Finding (MAPF), a partir da construção de Pattern Databases (PDBs)
Schwartzhaupt, Frederico Messa (2020) [Resumen publicado en evento] -
Estudo da capacidade de generalização de funções heurísticas no domínio de planejamento clássico BlocksWorld por meio de redes neurais
Fantini, Eduardo (2023) [Resumen publicado en evento] -
Estudo de modelos de aprendizado de máquina em dados tabulares para geração de funções heurísticas para planejamento clássico
Fantini, Eduardo (2022) [Resumen publicado en evento] -
Geração de Estados Iniciais Difíceis em Problemas de Planejamento Automatizado
Almeida, Bruno Corrêa de (2020) [Resumen publicado en evento] -
Geração de Estados Iniciais para Atomix com Busca Heurística
Simon, Mateus Davi (2018) [Resumen publicado en evento] -
IDFS, um algoritmo iterativo para planejamento FOND
Schwartzhaupt, Frederico Messa (2022) [Resumen publicado en evento] -
Learning deadlocks in sokoban
Boelter, Jean Persi (2018) [Tesinas de grado]In this thesis, we present an approach for deadlock detection in Sokoban based on neural networks. Sokoban is a challenging state space problem in artificial intelligence due to many characteristics, being the presence of ... -
A new greedy algorithm to estimate the Post-hoc method
Avila, Henry Bernardo Kochenborger de (2024) [Tesinas de grado]Heuristic functions estimate how far each state is from the goal condition and have been widely used to guide state-space search to solve planning tasks. Effective heuristic func tions find a good compromise between ... -
A non-admissible heuristic function based on synchronized abstract plans
Duranti, Nicolas Casagrande (2023) [Tesinas de grado]Classical Planning is a traditional Artificial Intelligence problem that consists of finding a sequence of actions, called a plan, to achieve some desired goal given an initial state. We say that the plan cost is the sum ... -
PEA∗+IDA∗ : an improved hybrid memory-restricted algorithm
Schwartzhaupt, Frederico Messa (2021) [Tesinas de grado]It is well-known that the search algorithms A∗ and Iterative Deepening A∗ (IDA∗ ) can fail to solve state-space tasks optimally due to time and memory limits. The former typically fails in memory-restricted scenarios and ... -
Planejador para Sokoban utilizando Production System Vector Notation
Almeida, Bruno Corrêa de (2021) [Resumen publicado en evento] -
Sequencing operator counts with state-space search
Kaizer, Wesley Luciano (2020) [Tesis de maestría]A search algorithm with an admissible heuristic function is the most common approach to optimally solve classical planning tasks. Recently DAVIES et al. (2015) introduced the solver OpSeq using Logic-Based Benders ... -
Solving moving-blocks problems
Pereira, André Grahl (2016) [Tesis]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 ... -
Understanding sample generation strategies for learning heuristic functions in classical planning
Bettker, Rafael Vales (2023) [Tesis de maestría]Heuristic functions are essential in guiding search algorithms to solve planning tasks. We study the problem of learning good heuristic functions for classical planning tasks with neural networks based on samples that are ...