Search results for "Monte Carlo tree search"
showing 3 items of 3 documents
Fuzzified Game Tree Search – Precision vs Speed
2012
Most game tree search algorithms consider finding the optimal move. That is, given an evaluation function they guarantee that selected move will be the best according to it. However, in practice most evaluation functions are themselves approximations and cannot be considered "optimal". Besides, we might be satisfied with nearly optimal solution if it gives us a considerable performance improvement. In this paper we present the approximation based implementations of the fuzzified game tree search algorithm. The paradigm of the algorithm allows us to efficiently find nearly optimal solutions so we can choose the "target quality" of the search with arbitrary precision --- either it is 100% (pr…
Fuzzified Tree Search in Real Domain Games
2011
Fuzzified game tree search algorithm is based on the idea that the exact game tree evaluation is not required to find the best move. Therefore, pruning techniques may be applied earlier resulting in faster search and greater performance. Applied to an abstract domain, it outperforms the existing ones such as Alpha-Beta, PVS, Negascout, NegaC*, SSS*/ Dual* and MTD(f). In this paper we present experimental results in real domain games, where the proposed algorithm demonstrated 10 percent performance increase over the existing algorithms.
AIs for Dominion Using Monte-Carlo Tree Search
2015
Dominion is a complex game, with hidden information and stochastic elements. This makes creating any artificial intelligence AI challenging. To this date, there is little work in the literature on AI for Dominion, and existing solutions rely upon carefully tuned finite-state solutions. This paper presents two novel AIs for Dominion based on Monte-Carlo Tree Search MCTS methods. This is achieved by employing Upper Confidence Bounds UCB and Upper Confidence Bounds applied to Trees UCT. The proposed solutions are notably better than existing work. The strongest proposal is able to win 67% of games played against a known, good finite-state solution, even when the finite-state solution has the u…