Abstract
The standard means of applying a discrete search to a continuous or hybrid system is the uniform discretization of control actions and action timing. Such discretization is fixed a priori and does not allow search to benefit from information gained at run-time. This paper introduces Information-Based Alpha-Beta Search, a new algorithm that preserves and benefits from the continuous or hybrid nature of the search. In a novel merging of alpha-beta game-tree search and information-based optimization, Information-Based Alpha-Beta Search makes trajectorysampling decisions dynamically based on the maximum-likelihood of search pruning. The result is a search algorithm which, while incurring higher computational overhead for the optimization, manages to so increase the quality of the sampling, that the net effect is a significant increase in performance. We present a new piecewise-parabolic variant of the algorithm and provide empirical evidence of its performance relative to random and uniform discretizations in the context of a variant of the homicidal chauffeur game.
This work was done both at the Stanford Knowledge Systems Laboratory with support by NASA Grant NAG2-1337, and at Gettysburg College.
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References
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Neller, T.W. (2002). Information-Based Alpha-Beta Search and the Homicidal Chau.eur. In: Tomlin, C.J., Greenstreet, M.R. (eds) Hybrid Systems: Computation and Control. HSCC 2002. Lecture Notes in Computer Science, vol 2289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45873-5_26
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DOI: https://doi.org/10.1007/3-540-45873-5_26
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