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    題名: Toward the optimum performance in the Bulls-and-Cows games
    作者: Liu, Chao-Lin;Chen, Yu-Sheng
    劉昭麟
    貢獻者: 資科系
    日期: 2012-11
    上傳時間: 2016-06-22 17:15:18 (UTC+8)
    摘要: Bulls-and-Cows (BAC) is a popular guessing game worldwide. Players try to find opponents` answers via clues provided by the opponents. Some theoretical analyses indicate that, on average, an optimal strategy takes 5.2131 guesses to find the answers for four-digit games. In this paper, alternative methods were explored to realize this theoretical optimality. The methods include a basic filtering method, different ways to recommend the candidate answers, and a machine-learning based method. The best performing combination of non-deterministic methods identified the correct answers with 5.3135 guesses in 504,000 trials. In addition, we were able to find the answers for five-digit and six-digit BAC games with 5.7800 and 6.3999 guesses.
    關聯: Proceedings of the 2012 Conference on Technologies and Applications of Artificial Intelligence (TAAI`12), 294‒299. Tainan, Taiwan, 16-18 November 2012
    資料類型: conference
    DOI 連結: http://dx.doi.org/10.1109/TAAI.2012.57
    DOI: 10.1109/TAAI.2012.57
    顯示於類別:[資訊科學系] 會議論文

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