Fuzzified Tree Search in Real Domain Games
نویسنده
چکیده
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.
منابع مشابه
Automatic Learning of Combat Models for RTS Games
Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or “simulator”) of the game at hand. However, in some games such forward model is not readily available. In this paper we address the problem of automatically learning forward models (more specifically, combats models) for two-player attrition games. We report experiments comparing several ap...
متن کاملRobust Opponent Modeling in Real-Time Strategy Games using Bayesian Networks
Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope...
متن کاملExperiments with Game Tree Search in Real-Time Strategy Games
Game tree search algorithms such as minimax have been used with enormous success in turn-based adversarial games such as Chess or Checkers. However, such algorithms cannot be directly applied to real-time strategy (RTS) games because a number of reasons. For example, minimax assumes a turn-taking game mechanics, not present in RTS games. In this paper we present RTMM, a real-time variant of the...
متن کاملGame-Tree Search over High-Level Game States in RTS Games
From an AI point of view, Real-Time Strategy (RTS) games are hard because they have enormous state spaces, they are real-time and partially observable. In this paper, we present an approach to deploy gametree search in RTS games by using game state abstraction. We propose a high-level abstract representation of the game state, that significantly reduces the branching factor when used for game-t...
متن کاملGuided Monte Carlo Tree Search for Planning in Learned Environments
Monte Carlo tree search (MCTS) is a sampling and simulation based technique for searching in large search spaces containing both decision nodes and probabilistic events. This technique has recently become popular due to its successful application to games, e.g. Poker Van den Broeck et al. (2009) and Go Coulom (2006); Chaslot et al. (2006); Gelly and Silver (2012)). Such games have known rules a...
متن کامل