The results found a phenomenon that 6 pieces version provides more entertainments for players and a new question which is proposed at the end of this paper. Consider the impact of board size on game experience, the study compare the playabilty among three sizes board of Chinese checkers which are 6 pieces, 10 pieces and 15 pieces. It is recommended that more master level players participate in the game will make the game more interesting.
The results show that the three players' battle mode has the highest game refinement value among 4 battle modes and the recommended number of players is three people. The experiments in this study investigated the entertainment of Chinese checkers by creating different battle modes of Chinese checkers. This research aims to explore the entertainment of popular multiplayer game-Chinese checkers. A game refinement measure is derived from a game information progress model and has been applied in various games. Game refinement theory has been used as a reliable tool for measuring the attractiveness and entertainment of games. The results found the justification for the ten pieces to be the mainstream version, while six pieces version provides consistent play experience regardless of player number. Consider the impact of board piece settings on game experience the study compares the playability among three board pieces of Chinese checkers, which are six pieces, ten pieces, and 15 pieces. The results show that the three players battle mode has the highest game refinement value among four battle modes, and the recommended number of players is three people. This study aims to explore the entertainment of popular multiplayer game-Chinese checkers. 228–233.Game refinement theory has been used as a reliable tool for measuring the attractiveness and entertainment of games, which is derived from a game information progress model and has been applied in various games. In: Thirteenth National Conference of the American Association for Artificial Intelligence (AAAI 1996), pp. Pearl, J.: Asymptotic properties of minimax trees and game-searching procedures. Knuth, D., Moore, R.: An analysis of alpha-beta pruning. Korf, R.: Multiplayer alpha-beta pruning. Hoyle, E., Frey, R., Morehead, A., Mott-Smith, G.: The Authoritative Guide to the Official Rules of All Popular Games of Skill and Chance. AAAI Press, Menlo Park (2000)īillings, D., Davidson, A., Schaeffer, J., Szafron, D.: The challenge of poker.
In: Sixteenth National Conference of the American Association for Artificial Intelligence (AAAI 2000), pp. Sturtevant, N., Korf, R.: On pruning techniques for multi-player games. In: Fifth National Conference of the American Association for Artificial Intelligence (AAAI 1986), pp. Luckhardt, C., Irani, K.: An algorithmic solution of N-person games. Journal of Artificial Intelligence Research 14, 303–358 (2001) Ginsberg, M.: GIB: Imperfect information in a computationally challenging game. Schaeffer, J., Culberson, J., Treloar, N., Knight, B., Lu, P., Szafron, D.: A world championship caliber checkers program. Princeton University Press, Princeton (2002) This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. We also confirm the expected results for the asymptotic branching factor improvements of the paranoid algorithm over max n. We also present quantitative results derived from playing max n and the paranoid algorithm (Sturtevant and Korf, 2000) against each other on various multi-player game domains, showing that paranoid widely outperforms max n in Chinese checkers, by a lesser amount in Hearts and that they are evenly matched in Spades. This paper presents other theoretical limitations of the max n algorithm, namely that tie-breaking strategies are crucial to max n, and that zero-window search is not possible in max n game trees.
The max n algorithm for playing multi-player games is flexible, but there are only limited techniques for pruning max n game trees.