Speeding up problem solving by abstraction: a graph oriented approach

Holte, Robert C.; Mkadmi, T.; Zimmer, Robert; MacDonald, Alan

doi:10.1016/0004-3702(95)00111-5

Cited by 74 publications

(56 citation statements)

References 23 publications

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“…Holtë et al [8] proposed STAR abstraction, which groups vertices within a certain distance from a particular vertex into a single abstraction. To refine abstract paths, an alternating search direction (AltO) technique is used, which creates a tree rooted at the goal abstraction to the level equal to the starting abstraction's, then backtracks from the starting abstraction to the goal only by climbing back up the tree.…”

Section: Node-centred Abstractionmentioning

confidence: 99%

Subregion graph: A path planning acceleration structure for characters with various motion types in very large environments

2015

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Modern computer graphics applications commonly feature very large virtual environments and diverse characters which perform different kinds of motions. To accelerate path planning in such a scenario, we propose the subregion graph data structure. It consists of subregions, which are clusters of locally connected waypoints inside a region, as well as subregion connectivities. We also present a fast algorithm to automatically generate a subregion graph from an enhanced waypoint graph map representation, which also supports various motion types and can be created from large virtual environments. Nevertheless, a subregion graph can be generated from any graphbased map representation. Our experiments show that a subregion graph is very compact relative to the input waypoint graph. By firstly planning a subregion path, and then limiting waypoint-level planning to this subregion path, over 8 times average speedup can be achieved, while average length ratios remain as low as 102.5%.

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Section: Node-centred Abstractionmentioning

confidence: 99%

Subregion graph: A path planning acceleration structure for characters with various motion types in very large environments

2015

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“…It therefore cannot be slower (except possibly for the small amount of overhead introduced by the bookkeeping actions), but will often expand fewer states and thus be faster. This principle has been used in [3] and later resulted in the independent development of Adaptive A*. The overhead of Adaptive A* over A* is small.…”

Section: Principle Of Adaptive A*mentioning

confidence: 99%

Speeding up moving-target search

Koenig¹,

Likhachev²,

Sun³

2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

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In this paper, we study moving-target search, where an agent (= hunter) has to catch a moving target (= prey). The agent does not necessarily know the terrain initially but can observe it within a certain sensor range around itself. It uses the strategy to always move on a shortest presumed unblocked path toward the target, which is a reasonable strategy for computer-controlled characters in video games. We study how the agent can find such paths faster by exploiting the fact that it performs A* searches repeatedly. To this end, we extend Adaptive A*, an incremental heuristic search method, to moving-target search and demonstrate experimentally that the resulting MT-Adaptive A* is faster than isolated A* searches and, in many situations, also D* Lite, a state-of-the-art incremental heuristic search method. In particular, it is faster than D* Lite by about one order of magnitude for moving-target search in known and initially unknown mazes if both search methods use the same informed heuristics.

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“…This will help in maintaining the quality of the paths and reduce the process for reassigning the spatial information for a new node. Star abstraction was proposed and developed by Holte et al [2]. The star abstraction is also known as radius of abstraction.…”

Section: Related Workmentioning

confidence: 99%

“…Many research reports have shown and proposed techniques for pathfinding by developing effective search techniques, or improving the usage of computer memory effectively [1,2]. Finding the path, in computer games and simulation, is a major component of Artificial Intelligent (AI) agents or Non Player Characters (NPCs) [3].…”

Section: Introductionmentioning

confidence: 99%

A comparison study on the reduction of search space using frame node technique for simulation and computer games

Pongphankae

Fung

2011

2011 International Conference on Machine Learning and Cybernetics

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Abstract:In simulation and computer game domains, two issues related to pathfinding problems are search algorithm and search space. This paper describes and compares four techniques used for the reduction of search space generation in 2-D simulation and game world environments. In order to improve the computational overheads, reducing size of search space is necessary. The size of search space is related to the number of nodes and edges. A high number of nodes and edges will have implications on the usage of computer memory. Therefore, reducing the number of nodes and edges will bring direct benefits to the memory usage and reduction of the search time. In this study, four techniques for the reduction of search space size are compared and investigated. In this paper, frame nodes used in the hierarchical adaptive flood filled (HAFF) technique is presented. This paper also presents the comparison results from the application of four hierarchical search spaces.

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Speeding up problem solving by abstraction: a graph oriented approach

Cited by 74 publications

References 23 publications

Subregion graph: A path planning acceleration structure for characters with various motion types in very large environments

Subregion graph: A path planning acceleration structure for characters with various motion types in very large environments

Speeding up moving-target search

A comparison study on the reduction of search space using frame node technique for simulation and computer games

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