Realtime Search Performance

  • Norifumi Mizuno
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 406)


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].


Actual Cost Goal State Optimal Decision Problem Solver Problem Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1997

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  • Norifumi Mizuno

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