An LP-Based Approach for Goal Recognition as Planning

Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent. In this paper, we develop a method based on the operator-counting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recognition tasks. Our method reasons explicitly about both partial and noisy observations: estimating uncertainty for the former, and satisfying observations given the unreliability of the sensor for the latter. We evaluate our approach empirically over a large data set, analyzing its components on how each can impact the quality of the solutions. In general, our approach is superior to previous methods in terms of agreement ratio, accuracy, and spread. Finally, our approach paves the way for new research on combinatorial optimization to solve goal recognition tasks.Comment: 8 pages, 4 tables, 3 figures. Published in AAAI 2021. Updated final authorship and tex

Solving motion planning problems

This work considers a family of motion planning problems with movable blocks. Such problem is de ned by a maze grid occupied by immovable blocks (walls) and free squares. There are k movable blocks (stones) and k fixed goal squares. The man is a movable block that can traverse free squares and move stones between them. The problem goal is to move the stones from their initial positions to the goal squares with the minimum number of stone moves. (Párrafo extraído del texto a modo de resumen)Sociedad Argentina de Informática e Investigación Operativa (SADIO

Solving motion planning problems

This work considers a family of motion planning problems with movable blocks. Such problem is de ned by a maze grid occupied by immovable blocks (walls) and free squares. There are k movable blocks (stones) and k fixed goal squares. The man is a movable block that can traverse free squares and move stones between them. The problem goal is to move the stones from their initial positions to the goal squares with the minimum number of stone moves. (Párrafo extraído del texto a modo de resumen)Sociedad Argentina de Informática e Investigación Operativa (SADIO

Resolvendo problemas de blocos movéis

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.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

Resolvendo problemas de blocos movéis

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.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

A Best-First Search Algorithm for FOND Planning and Heuristic Functions to Optimize Decompressed Solution Size

In this work, we study fully-observable non-deterministic (FOND) planning, which models uncertainty through actions with non-deterministic effects. We present a best-first heuristic search algorithm called AND* that searches the policy-space of the FOND task to find a solution policy. We generalize the concepts of optimality, admissibility, and goal-awareness for FOND. Using these new concepts, we formalize the concept of heuristic functions that can guide a policy-space search. We analyze different aspects of the general structure of FOND solutions to introduce and characterize a set of FOND heuristics that estimate how far a policy is from becoming a solution. One of these heuristics applies a novel insight. Guided by them AND* returns only solutions with the minimal possible number of mapped states. We systematically study these FOND heuristics theoretically and empirically. We observe that our best heuristic makes AND* much more effective than the straightforward heuristics. We believe that our work allows a better understanding of how to design algorithms and heuristics to solve FOND tasks

PEA*+IDA*: An Improved Hybrid Memory-Restricted Algorithm

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 the latter in time-restricted scenarios. Therefore, several algorithms were proposed to solve state-space tasks optimally using less memory than A* and less time than IDA*, such as A*+IDA*, a hybrid memory-restricted algorithm that combines A* and IDA*. In this paper, we present a hybrid memory-restricted algorithm that combines Partial Expansion A* (PEA*) and IDA*. This new algorithm has two phases, the same structure as the A*+IDA* algorithm. The first phase of PEA*+IDA* runs PEA* until it reaches a memory limit, and the second phase runs IDA* without duplicate detection on each node of PEA*'s Open. First, we present a model that shows how PEA*+IDA* can perform better than A*+IDA* although pure PEA* usually makes more expansions than pure A*. Later, we perform an experimental evaluation using three memory limits and show that, compared to A*+IDA* on classical planning domains, PEA*+IDA* has higher coverage and expands fewer nodes. Finally, we experimentally analyze both algorithms and show that having higher F-limits and better priority-queue composition given by PEA* have a considerable impact on the performance of the algorithms

Iterative Depth-First Search for FOND Planning

Fully Observable Non-Deterministic (FOND) planning models uncertainty through actions with non-deterministic effects. Existing FOND planning algorithms are effective and employ a wide range of techniques. However, most of the existing algorithms are not robust for dealing with both non-determinism and task size. In this paper, we develop a novel iterative depth-first search algorithm that solves FOND planning tasks and produces strong cyclic policies. Our algorithm is explicitly designed for FOND planning, addressing more directly the non-deterministic aspect of FOND planning, and it also exploits the benefits of heuristic functions to make the algorithm more effective during the iterative searching process. We compare our proposed algorithm to well-known FOND planners, and show that it has robust performance over several distinct types of FOND domains considering different metrics

Macaclasse

Formato: Animação em stop motion com recorte de papel – Duração: 1:45 min Produzido na Oficina de vídeo-história de 2010.Na rotina de uma sala de aula, com conversas, brincadeiras e professora dando aula, um macaco aparece causando confusão