在西洋棋中,開局決定了棋局未來發展的基礎,棋手在開局階段局勢的好壞,會直接影響到接下來中局的發展,乃至全局的勝負。隨著西洋棋的演進,棋手們在比賽中進行各式各樣的棋步嘗試,發展出眾多經歷實戰考驗的開局,目前西洋棋的開局多達上千種變化,使得棋手在學習西洋棋的過程中,要花上大量的時間從眾多的西洋棋開局變化中,尋找適合自己的開局鑽研與使用。為幫助棋手在此階段的學習,本論文提出西洋棋開局推薦系統,從大數據協助學習的觀點,利用大量棋手們的開局經驗,對棋手做個人化的開局推薦。此系統以風格、棋力相似的棋手們所選用的開局為推薦基礎,並考量棋手習慣使用的下棋模式,推薦棋手善於發揮自身優勢、易於理解,並且投其所好的開局。為此,此西洋棋開局推薦系統包含風格分析、棋力評估、棋形截取,以及混合式推薦等部分。依據棋手過去的對局記錄,風格分析評估棋手下棋偏好冒險或保守的程度;棋力評估將傳統西洋棋棋力轉成可直觀比較棋手棋力程度差異之量表;棋形截取找出棋手習慣使用的下棋模式。最後,混合式推薦綜合考量上述三項因素,推薦出符合棋手棋風、棋力與下棋習慣模式的開局。
本論文以兩個實驗來評估風格分析與開局推薦系統的效果,在風格分析的實驗中,將風格分析方法評估棋手風格的結果與專家判斷的結果做比較;在開局推薦系統的實驗中,以棋手是否將會在比賽使用系統所推薦的開局來評估推薦效果。實驗結果顯示,風格分析對於世界冠軍棋手的風格評估幾乎與專家的判斷相同;開局推薦系統針對開局所設計的混合式推薦方法,推薦效果優於常見的推薦方法。 / The Opening is the fundamental phase of a chess game, and significantly affects the result of a competition. With the evolution of chess, there has been developed thousands of chess openings at present. This makes it difficult and time-consuming for chess players to find and learn the openings suitable for them. For helping players to learn chess in the opening, we provide Opening Recommendation System (OPRS), which considers chess players’ experiences and recommends chess openings that could be understandable and favorite for the players. For personalized recommendation, OPRS analyzes the playing style, translates chess rating, extracts the playing patterns, and then performs hybrid recommendation based on the features obtained.
In the evaluation, the performance of the playing style analysis are demonstrated by comparing with the styles judged by chess experts for world chess championships.
For OPRS, the evaluations are according to the openings the players use in the chess tournaments in the next years. The experiments show that OPRS achieves good accuracies of the playing style analysis and outperforms the competitive methods for chess opening recommendation.
Identifer | oai:union.ndltd.org:CHENGCHI/G0101753012 |
Creators | 楊元翰 |
Publisher | 國立政治大學 |
Source Sets | National Chengchi University Libraries |
Language | 中文 |
Detected Language | English |
Type | text |
Rights | Copyright © nccu library on behalf of the copyright holders |
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