Studies of Winning Tactics for the Chinese Chess <tt> KN <u>P</u> kgm </tt> Endgame

Endgames do help improve the playing strength of computer Chinese chess programs. However, due to their large size, human players benefit minorly. Distance-to-mate (DTM) or distance-to-conversion (DTC) values computed for endgames do not capture the idea of human playing style. Typically, human players develop a high-level strategy during a game, which consists of a sequence of captures or piece exchanges. The capture process may not follow DTC or DTM values but aims at simplifying the game without creating risks. The process of piece exchanges is not recorded in any endgame databases. We propose a systematic method for finding important winning tactics in endgame studies, which may be useful in studying other endgames. As an illustration, we study the Chinese chess KN P kgm endgame and find winning tactics that are beneficial for human players. Although many winning tactics are known for RED to win, such as exchanging the red pawn for the black guard or minister, human players only need to focus on two tactics, namely to capture the black guard or minister to ensure a win when playing this endgame.

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Publication Details

Journal
ICGA Journal
Published
2025-04-27
DOI
https://doi.org/10.1177/13896911251334960
Primary Topic
Artificial Intelligence in Games
Type
article
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article

Studies of Winning Tactics for the Chinese Chess <tt> KN <u>P</u> kgm </tt> Endgame

T. C. Hsu, Chih-Lin Chen, Jr-Chang Chen
ICGA Journal
Artificial Intelligence in Games
article

Studies of Winning Tactics for the Chinese Chess <tt> KN <u>P</u> kgm </tt> Endgame

T. C. Hsu, Chih-Lin Chen, Jr-Chang Chen
article en

Abstract

Endgames do help improve the playing strength of computer Chinese chess programs. However, due to their large size, human players benefit minorly. Distance-to-mate (DTM) or distance-to-conversion (DTC) values computed for endgames do not capture the idea of human playing style. Typically, human players develop a high-level strategy during a game, which consists of a sequence of captures or piece exchanges. The capture process may not follow DTC or DTM values but aims at simplifying the game without creating risks. The process of piece exchanges is not recorded in any endgame databases. We propose a systematic method for finding important winning tactics in endgame studies, which may be useful in studying other endgames. As an illustration, we study the Chinese chess KN P kgm endgame and find winning tactics that are beneficial for human players. Although many winning tactics are known for RED to win, such as exchanging the red pawn for the black guard or minister, human players only need to focus on two tactics, namely to capture the black guard or minister to ensure a win when playing this endgame.

ICGA JournalVol. 47(2)
Institute of Information Science, Academia Sinica (TW), National Taipei University (TW)
Peace, Justice and strong institutions
Openalex Percentile: Top 17%
Artificial Intelligence in Games
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