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障害物密度に応じた迷路探索問題の難易度指標と実時間探索アルゴリズムの性能解析

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Title: 障害物密度に応じた迷路探索問題の難易度指標と実時間探索アルゴリズムの性能解析
Other Titles: Hardness Measures for Maze Problems Parameterized by Obstacle Ratio and Performance Analysis of Real-Time Search Algorithms
Authors: 水澤, 雅高1 Browse this author
栗原, 正仁2 Browse this author →KAKEN DB
Authors(alt): Mizusawa, Masataka1
Kurihara, Masahito2
Keywords: real-time search
maze problem
state space
heuristics
phase transitions
Issue Date: 2006
Publisher: 人工知能学会
Journal Title: 人工知能学会論文誌
Volume: 21
Issue: 3
Start Page: 266
End Page: 275
Publisher DOI: 10.1527/tjsai.21.266
Abstract: Although the maze (or gridworld) is one of the most widely used benchmark problems for real-time search algorithms, it is not sufficiently clear how the difference in the density of randomly positioned obstacles affects the structure of the state spaces and the performance of the algorithms. In particular, recent studies of the so-called phase transition phenomena that could cause dramatic change in their performance in a relatively small parameter range suggest that we should evaluate the performance in a parametric way with the parameter range wide enough to cover potential transition areas. In this paper, we present two measures for characterizing the hardness of randomly generated mazes parameterized by obstacle ratio and relate them to the performance of real-time search algorithms. The first measure is the entropy calculated from the probability of existence of solutions. The second is a measure based on total initial heuristic error between the actual cost and its heuristic estimation. We show that the maze problems are the most complicated in both measures when the obstacle ratio is around 41%. We then solve the parameterized maze problems with the well-known real-time search algorithms RTA*, LRTA*, and MARTA* to relate their performance to the proposed measures. Evaluating the number of steps required for a single problem solving by the three algorithms and the number of those required for the convergence of the learning process in LRTA*, we show that they all have a peak when the obstacle ratio is around 41%. The results support the relevance of the proposed measures. We also discuss the performance of the algorithms in terms of other statistical measures to get a quantitative, deeper understanding of their behavior.
Type: article (author version)
URI: http://hdl.handle.net/2115/14559
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 栗原 正仁

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