Search and simulation in games and planning

这是启发式搜索研究的激动人心的时刻。通过搜索技术的巨大改进，三年前似乎完全无法实现的研究目标已经实现，新的应用已经成为可能。“游戏和规划中的搜索和模拟”提出了在快速发展的模拟增强搜索方法领域的研究。统计“蒙特-卡罗”方法通过重复随机抽样探索搜索空间，已被证明是一种非常强大的动态技术，克服了以前更静态方法的局限性。这些技术已经导致了应用程序的快速发展，如古老的围棋游戏，独立于游戏的一般游戏，甚至是经典的独立于领域的规划。

These are exciting times for research in heuristic search. Research goals that seemed completely out of reach just three years ago have been achieved through huge improvements in search techniques, and new applications have become possible. "Search and Simulation in Games and Planning" proposes research in the fast developing field of simulation-enhanced search methods. Statistical "Monte-Carlo" methods, which explore a search space by repeated random sampling, have proven to be a very powerful, dynamic technique that overcomes the limitations of more static previous approaches. These techniques have lead to rapid progress in applications as diverse as the ancient game of Go, game-independent General Game Playing, and even classical domain-independent planning.