Abstract
This chapter investigates available real-time search algorithms, e.g., Real-Time-A* (RTA*), Learning-Real-Time-A* (LRTA*) [Korf, 1990] and Local-Consistency-Maintenance (LCM) [Pemberton and Korf, 1992]. Though realtime search algorithms have some learning capability, previous research has been focused on the performance of each problem solving trial. For example, LRTA* learns the exact cost to the goal along the optimal path to the goal. However, there is almost no research on the learning efficiency of realtime search. This chapter is intended to evaluate the learning process to clarify the following three basic questions [Mizuno and Ishida, 1995].
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© 1997 Kluwer Academic Publishers
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Mizuno, N. (1997). Realtime Search Performance. In: Real-Time Search for Learning Autonomous Agents. The Springer International Series in Engineering and Computer Science, vol 406. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-34507-9_1
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DOI: https://doi.org/10.1007/978-0-585-34507-9_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-7923-9944-5
Online ISBN: 978-0-585-34507-9
eBook Packages: Springer Book Archive