mlsanityData to 14 Oct 2026

On the complexity of Dark Chinese Chess

arXiv 2112.02989 ↗First posted 6 Dec 2021cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

This paper provides a complexity analysis for the game of dark Chinese chess (a.k.a. "JieQi"), a variation of Chinese chess. Dark Chinese chess combines some of the most complicated aspects of board and card games, such as long-term strategy or planning, large state space, stochastic, and imperfect-information, which make it closer to the real world decision-making problem and pose great challenges to game AI. Here we design a self-play program to calculate the game tree complexity and average information set size of the game, and propose an algorithm to calculate the number of information sets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 20 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

5 of 20 references.

References · 5

  1. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  2. 2017DeepStack: Expert-Level Artificial Intelligence in No-Limit Pokercited by 233
  3. 2021DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learningcited by 66
  4. 2020Suphx: Mastering Mahjong with Deep Reinforcement Learningcited by 58
  5. 2018On the Complexity of Reconnaissance Blind Chesscited by 4

Built on by

Not cited yet.