Browsing by Author "Pereira, André Grahl"
Now showing items 1-18 of 18
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Análise de Algoritmos de Busca em Espaços de Estados com Memória Limitada
Schwartzhaupt, Frederico Messa (2021) [Abstract published in event] -
Análise de diferentes funções heurísticas sobre variante do jogo Pacman
Santos, Thiago Trautwein (2022) [Abstract published in event] -
Aprendendo a Detectar Estados Dead em Sokoban
Simon, Mateus Davi (2019) [Abstract published in event] -
Domain dependent heuristics and tie breakers : topics in automated planning
Corrêa, Augusto Blaas (2018) [Work completion of graduation]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) [Abstract published in event] -
Estudo da capacidade de generalização de funções heurísticas no domínio de planejamento clássico BlocksWorld por meio de redes neurais
Fantini, Eduardo (2023) [Abstract published in event] -
Estudo de modelos de aprendizado de máquina em dados tabulares para geração de funções heurísticas para planejamento clássico
Fantini, Eduardo (2022) [Abstract published in event] -
Geração de Estados Iniciais Difíceis em Problemas de Planejamento Automatizado
Almeida, Bruno Corrêa de (2020) [Abstract published in event] -
Geração de Estados Iniciais para Atomix com Busca Heurística
Simon, Mateus Davi (2018) [Abstract published in event] -
IDFS, um algoritmo iterativo para planejamento FOND
Schwartzhaupt, Frederico Messa (2022) [Abstract published in event] -
Learning deadlocks in sokoban
Boelter, Jean Persi (2018) [Work completion of graduation]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) [Work completion of graduation]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) [Work completion of graduation]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) [Work completion of graduation]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) [Abstract published in event] -
Sequencing operator counts with state-space search
Kaizer, Wesley Luciano (2020) [Dissertation]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) [Thesis]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) [Dissertation]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 ...