Graph search methods for non-order-preserving evaluation functions: applications to job sequencing problems

AbstractGraph search with A∗ is frequently faster than tree search. But A∗ graph search operates correctly only when the evaluation function is order-preserving. In the non-orderpreserving case, no paths can be discarded and the entire explicit graph must be stored in memory. Such situations arise in one-machine minimum penalty job sequencing problems when setup times are sequence dependent. GREC, the unlimited memory version of a memory-constrained search algorithm of the authors called MREC, has a clear advantage over A∗in that it is able to find optimal solutions to such problems. At the same time, it is as efficient as A∗ in solving graph search problems with order-preserving evaluation functions. Experimental results indicate that in the non-order-preserving case, GREC is faster than both best-first and depth-first tree search, and can solve problem instances of larger size than best-first tree search

Performance Evaluation of Pathfinding Algorithms

Pathfinding is the search for an optimal path from a start location to a goal location in a given environment. In Artificial Intelligence pathfinding algorithms are typically designed as a kind of graph search. These algorithms are applicable in a wide variety of applications such as computer games, robotics, networks, and navigation systems. The performance of these algorithms is affected by several factors such as the problem size, path length, the number and distribution of obstacles, data structures and heuristics. When new pathfinding algorithms are proposed in the literature, their performance is often investigated empirically (if at all). Proper experimental design and analysis is crucial to provide an informative and non- misleading evaluation. In this research, we survey many papers and classify them according to their methodology, experimental design, and analytical techniques. We identify some weaknesses in these areas that are all too frequently found in reported approaches. We first found the pitfalls in pathfinding research and then provide solutions by creating example problems. Our research shows that spurious effects, control conditions provide solutions to avoid these pitfalls

Best-first and/or search for graphical models

Abstract The paper presents and evaluates the power of best-first search over AND/OR search spaces in graphical models. The main virtue of the AND/OR representation is its sensitivity to the structure of the graphical model, which can translate into significant time savings. Indeed, in recent years depth-first AND/OR Branch-and-Bound algorithms were shown to be very effective when exploring such search spaces, especially when using caching. Since best-first strategies are known to be superior to depth-first when memory is utilized, exploring the best-first control strategy is called for. In this paper we introduce two classes of best-first AND/OR search algorithms: those that explore a context-minimal AND/OR search graph and use static variable orderings, and those that use dynamic variable orderings but explore an AND/OR search tree. The superiority of the best-first search approach is demonstrated empirically on various real-world benchmarks

MIDA: An IDA Search with Dynamic Control

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Aeronautics and Space Administration / NCC 2-481National Science Foundation / MIP 88-1058

Parallele Bewegungsplanung in dynamischen Umgebungen

Dieser interne Bericht gibt einen Ueberblick ueber die aktuellen Forschungsergebnisse aus dem gleichnamigen Projekt. Hierbei wird das Problem der praktikablen Bewegungsplanung fuer Industrieroboter in dynamischen Umgebungen angegangen. Der Grundalgorithmus ohne wesentliche off-line Berechnungen basiert auf der A*-Suche und arbeitet im impliziten, diskretisierten Konfigurationsraum. Die Kollisionen werden im kartesischen Arbeitsraum durch hierarchische Abstandsberechnung im gegebenen CAD-Modell erkannt. Eine zyklische Aufteilung des Suchraums auf die einzelnen Prozessoren ermoeglicht eine gut skalierbare Parallelverarbeitung fuer nachrichten-gekoppelte Rechnersysteme. Die Leistungsfaehigkeit des Bewegungsplaners wird an einem Satz von Benchmark-Problemen validiert. Unterstuetzt durch eine optimale Diskretisierung zeigt der neuartige Ansatz einen linearen Speedup. Fuer Umgebungen mit unbewegten Hindernissen liegen die Laufzeiten im Sekundenbereich. Zur weiteren Beschleunigung der Bewegungsplanung wird erstmalig eine heuristische hierarchische Suche im impliziten Konfigurationsraum eingefuehrt. Fuer zweidimensionale Benchmark-Probleme ergibt die Hierarchisierung eine starke Reduktion des Suchaufwandes

Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy

Large-scale, parallel clusters composed of commodity processors are increasingly available, enabling the use of vast processing capabilities and distributed RAM to solve hard search problems. We investigate Hash-Distributed A* (HDA*), a simple approach to parallel best-first search that asynchronously distributes and schedules work among processors based on a hash function of the search state. We use this approach to parallelize the A* algorithm in an optimal sequential version of the Fast Downward planner, as well as a 24-puzzle solver. The scaling behavior of HDA* is evaluated experimentally on a shared memory, multicore machine with 8 cores, a cluster of commodity machines using up to 64 cores, and large-scale high-performance clusters, using up to 2400 processors. We show that this approach scales well, allowing the effective utilization of large amounts of distributed memory to optimally solve problems which require terabytes of RAM. We also compare HDA* to Transposition-table Driven Scheduling (TDS), a hash-based parallelization of IDA*, and show that, in planning, HDA* significantly outperforms TDS. A simple hybrid which combines HDA* and TDS to exploit strengths of both algorithms is proposed and evaluated.Comment: in press, to appear in Artificial Intelligenc

Побудова найкоротшого маршруту на карті з урахуванням областей видимості проміжних пунктів

В бакалаврській дипломній роботі було запропоновано та реалізовано алгоритм побудови найкоротшого маршруту на карті з урахуванням областей видимості проміжних пунктів. Програма дозволяє розрахувати оптимальний маршрут на основі загальнодоступних геопросторових даних від проекту OpenStreetMap та параметрів, отриманих від користувача, оцінюючи не лише координати пунктів, але і їх межі по відношенню до інших об’єктів на карті. Програмний продукт було створено на мові Java.In the bachelor thesis work, an algorithm for constructing the shortest route on the map considering the areas of visibility of intermediate points was proposed and implemented. The program allows user to calculate the optimal route based on publicly available geospatial data from the OpenStreetMap project and the parameters received from the user, evaluating not only the coordinates of the points, but also their bounds and relations with other objects on the map. The software product was implemented in Java