资源分类
》 资源 》论文》Using Payoff-Similarity to Speed Up Search Timothy Furtak and Michael Buro

Using Payoff-Similarity to Speed Up Search Timothy Furtak and Michael Buro

2019-11-12 | |  47 |   41 |   0
Abstract Transposition tables are a powerful tool in search domains for avoiding duplicate effort and for guiding node expansions. Traditionally, however, they have only been applicable when the current state is exactly the same as a previously explored state. We consider a generalized transposition table, whereby a similarity metric that exploits local structure is used to compare the current state with a neighbourhood of previously seen states. We illustrate this concept and forward pruning based on function approximation in the domain of Skat, and show that we can achieve speedups of 16+ over standard methods.

上一篇：Probabilistic Satis?ability: Logic-Based Algorithms and Phase Transition?

下一篇：Kernels for Global Constraints? Serge Gaspers and Stefan Szeider

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...