Parallelizing Dijkstra\u27s Algorithm

Dijkstra’s algorithm is an algorithm for finding the shortest path between nodes in a graph. The algorithm published in 1959 by Dutch computer scientist Edsger W. Dijkstra, can be applied on a weighted graph. Dijkstra’s original algorithm runtime is a quadratic function of the number of vertices. In this paper, I will investigate the parallel formulation of Dijkstra’s algorithm and its speedup against the sequential one. The implementation of the parallel formulation will be performed by Message Passing Interface (MPI) and Open Multi-Processing (OpenMP). The results gained indicated that the performance of MPI and OpenMP to be significantly better than sequential for a higher number of input data scale. And the smaller number of processors/threads give the fastest result for MPI and OpenMP implementation. However, the results show that the average speedup achieved by parallelization is not satisfied. The parallel implementation of Dijkstra’s algorithm may not be the best option

Influence map-based pathfinding algorithms in video games

Path search algorithms, i.e., pathfinding algorithms, are used to solve shortest path problems by intelligent agents, ranging from computer games and applications to robotics. Pathfinding is a particular kind of search, in which the objective is to find a path between two nodes. A node is a point in space where an intelligent agent can travel. Moving agents in physical or virtual worlds is a key part of the simulation of intelligent behavior. If a game agent is not able to navigate through its surrounding environment without avoiding obstacles, it does not seem intelligent. Hence the reason why pathfinding is among the core tasks of AI in computer games. Pathfinding algorithms work well with single agents navigating through an environment. In realtime strategy (RTS) games, potential fields (PF) are used for multi-agent navigation in large and dynamic game environments. On the contrary, influence maps are not used in pathfinding. Influence maps are a spatial reasoning technique that helps bots and players to take decisions about the course of the game. Influence map represent game information, e.g., events and faction power distribution, and is ultimately used to provide game agents knowledge to take strategic or tactical decisions. Strategic decisions are based on achieving an overall goal, e.g., capture an enemy location and win the game. Tactical decisions are based on small and precise actions, e.g., where to install a turret, where to hide from the enemy. This dissertation work focuses on a novel path search method, that combines the state-of-theart pathfinding algorithms with influence maps in order to achieve better time performance and less memory space performance as well as more smooth paths in pathfinding.Algoritmos de pathfinding são usados por agentes inteligentes para resolver o problema do caminho mais curto, desde a àrea jogos de computador até à robótica. Pathfinding é um tipo particular de algoritmos de pesquisa, em que o objectivo é encontrar o caminho mais curto entre dois nós. Um nó é um ponto no espaço onde um agente inteligente consegue navegar. Agentes móveis em mundos físicos e virtuais são uma componente chave para a simulação de comportamento inteligente. Se um agente não for capaz de navegar no ambiente que o rodeia sem colidir com obstáculos, não aparenta ser inteligente. Consequentemente, pathfinding faz parte das tarefas fundamentais de inteligencia artificial em vídeo jogos. Algoritmos de pathfinding funcionam bem com agentes únicos a navegar por um ambiente. Em jogos de estratégia em tempo real (RTS), potential fields (PF) são utilizados para a navegação multi-agente em ambientes amplos e dinâmicos. Pelo contrário, os influence maps não são usados no pathfinding. Influence maps são uma técnica de raciocínio espacial que ajudam agentes inteligentes e jogadores a tomar decisões sobre o decorrer do jogo. Influence maps representam informação de jogo, por exemplo, eventos e distribuição de poder, que são usados para fornecer conhecimento aos agentes na tomada de decisões estratégicas ou táticas. As decisões estratégicas são baseadas em atingir uma meta global, por exemplo, a captura de uma zona do inimigo e ganhar o jogo. Decisões táticas são baseadas em acções pequenas e precisas, por exemplo, em que local instalar uma torre de defesa, ou onde se esconder do inimigo. Esta dissertação foca-se numa nova técnica que consiste em combinar algoritmos de pathfinding com influence maps, afim de alcançar melhores performances a nível de tempo de pesquisa e consumo de memória, assim como obter caminhos visualmente mais suaves

Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks

This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm

09491 Abstracts Collection -- Graph Search Engineering

From the 29th November to the 4th December 2009, the Dagstuhl Seminar 09491 ``Graph Search Engineering \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Integrating a parallel computer and a heterogeneous workstation cluster into a metacomputer system

Two types of parallel computers commonly used or solving large scientific problems are clusters of workstations and distributed-memory multicomputers. Each system has strengths and weaknesses for this task. Workstation clusters have a high performance to cost ratio and the advantage of the latest processors. Workstations are commonly under-utilized and can provide an inexpensive source of CPU cycles. However, clusters of workstations cannot compete with the performance of a dedicated supercomputer. This research proposes that creating a metacomputer combining different types of parallel computers can provide some of the advantages of each separate system. Specifically, I have inte­grated a distributed-memory parallel computer (the MEIKO CS-2) with a heterogeneous cluster of workstations. The integrated system uses the CHARM parallel-programming environment to provide for machine-independence and ease of programming in this heterogeneous environment. The availability of processing capacity limits the size and complexity of the types o[ problems that can be efficiently solved. By creating a meta.computer the amount of processing capacity can be increased at relatively low costs. The low cost of the system and the fact that it is easily reconfigurable make it a good choice for solving large-scale Grand Challenge type scientific problems

Parallel implementations of dynamic traffic assignment models and algorithms for dynamic shortest path problems

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Includes bibliographical references (p. 139-144).This thesis aims at the development of faster Dynamic Traffic Assignment (DTA) models to meet the computational efficiency required by real world applications. A DTA model can be decomposed into several sub-models, of which the most time consuming ones are the dynamic network loading model and the user's route choice model. We apply parallel computing technology to the dynamic network loading model to achieve faster implementations. To the best of our knowledge, this concerns the first parallel implementations of macroscopic DTA models. Two loading algorithms are studied: the iterative loading algorithm and the chronological loading algorithm. For the iterative loading algorithm, two parallelization strategies are implemented: decomposition by network topology and by time. For the chronological loading algorithm, the network topology decomposition strategy is implemented. Computational tests are carried out in a distributed-memory environment. Satisfactory speedups are achieved. We design efficient shortest path algorithms to speedup the user's route choice model. We first present a framework for static shortest path algorithms, which prioritize nodes with optimal distance labels in the scan eligible list. Then we apply the framework in dynamic FIFO, strict FIFO, and static networks. Computational tests show significant speedups. We proceed to present two other shortest path algorithms: Algorithm Delta and Algorithm Hierarchy. We also provide the evaluations of the algorithms.by Hai Jiang.S.M