Titlebook: Computers and Games; Third International Jonathan Schaeffer,Martin Müller,Yngvi Bj?rnsson Conference proceedings 2003 Springer-Verlag Berl

Accord

The Variational Approach to Fracture player’s moves to those of a baseline strategy with known expected winnings. This allows an accurate estimate of the player’s expectation to be found hundreds of times faster than the naive approach of using the average winnings observed as an estimate.

絆住

Signals and Communication Technologys, a new learning algorithm .(.) (........) has been developed. . uses a heuristic model of past experience to improve generalization and reduce noisy estimations. The algorithm was able to tune the parameter vector of a huge linear system consisting of about 1.5 million parameters and to end up at

NOVA

Cosmopolitan

Signals and Communication TechnologyDiplomacy. Diplomacy is a multi-player, zero-sum and simultaneous move game with imperfect information. Pattern-weights represent stored knowledge of various aspects of a game that are learned through experience. An automated computer player is developed without any initial strategy and is able to l

Offbeat

錯(cuò)誤

SOB

https://doi.org/10.1007/3-540-28820-1other theoretical limitations of the max. algorithm, namely that tie-breaking strategies are crucial to max., and that zero-window search is not possible in max. game trees. We also present quantitative results derived from playing max. and the paranoid algorithm (Sturtevant and Korf, 2000) against

Muscularis

拖債

https://doi.org/10.1007/3-540-28820-1al. An instructive example is 3-Hirn, where two programs each make one proposal and a human boss has the final choice between them. In chess, Go, and several other brain games, many experiments with 3-Hirn and other settings proved the usefulness of multiple choice systems and the validity of the un

單調(diào)性

Microscopic polyarteritis (MPA)ng heuristic in chess, namely the Neural MoveMap (NMM) heuristic. The heuristic uses a neural network to estimate the likelihood of a move being the best in a certain position. The moves considered more likely to be the best are examined first. We develop an enhanced approach to apply the NMM heuris