Related papers: General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess

General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess

URL: http://arxiv.org/abs/2506.01242v1
Date: Mon, 02 Jun 2025 01:41:27 GMT
Title: General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess
Authors: Brian Hu Zhang, Tuomas Sandholm,
Abstract summary: We present Obscuro, the first superhuman AI for Fog of War chess.<n>It introduces advances to search in imperfect-information games, enabling strong, scalable reasoning.<n>Experiments against the prior state-of-the-art AI and human players show that Obscuro is significantly stronger.
Score: 68.20244032271847
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Since the advent of AI, games have served as progress benchmarks. Meanwhile, imperfect-information variants of chess have existed for over a century, present extreme challenges, and have been the focus of significant AI research. Beyond calculation needed in regular chess, they require reasoning about information gathering, the opponent's knowledge, signaling, etc. The most popular variant, Fog of War (FoW) chess (aka. dark chess) is a recognized challenge problem in AI after superhuman performance was reached in no-limit Texas hold'em poker. We present Obscuro, the first superhuman AI for FoW chess. It introduces advances to search in imperfect-information games, enabling strong, scalable reasoning. Experiments against the prior state-of-the-art AI and human players -- including the world's best -- show that Obscuro is significantly stronger. FoW chess is the largest (by amount of imperfect information) turn-based game in which superhuman performance has been achieved and the largest game in which imperfect-information search has been successfully applied.

Related papers

A Behavior-Based Knowledge Representation Improves Prediction of Players' Moves in Chess by 25% [2.232417329532027]
This paper proposes a novel approach combining expert knowledge with machine learning techniques to predict human players' next moves.<n>By applying feature engineering grounded in domain expertise, we seek to uncover the patterns in the moves of intermediate-level chess players.<n>Our methodology offers a promising framework for anticipating human behavior, advancing both the fields of AI and human-computer interaction.
arXiv Detail & Related papers (2025-04-07T18:49:00Z)
Human-aligned Chess with a Bit of Search [35.16633353273246]
Chess has long been a testbed for AI's quest to match human intelligence. In this paper, we introduce Allie, a chess-playing AI designed to bridge the gap between artificial and human intelligence in this classic game.
arXiv Detail & Related papers (2024-10-04T19:51:03Z)
DanZero+: Dominating the GuanDan Game through Reinforcement Learning [95.90682269990705]
We develop an AI program for an exceptionally complex and popular card game called GuanDan. We first put forward an AI program named DanZero for this game. In order to further enhance the AI's capabilities, we apply policy-based reinforcement learning algorithm to GuanDan.
arXiv Detail & Related papers (2023-12-05T08:07:32Z)
Are AlphaZero-like Agents Robust to Adversarial Perturbations? [73.13944217915089]
AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. We ask whether adversarial states exist for Go AIs that may lead them to play surprisingly wrong actions. We develop the first adversarial attack on Go AIs that can efficiently search for adversarial states by strategically reducing the search space.
arXiv Detail & Related papers (2022-11-07T18:43:25Z)
AI-powered mechanisms as judges: Breaking ties in chess [0.0]
We propose an AI-driven method for an objective tiebreaking mechanism. The method evaluates the quality of players' moves by comparing them to the optimal moves suggested by powerful chess engines. This approach not only enhances the fairness and integrity of the competition but also maintains the game's high standards.
arXiv Detail & Related papers (2022-10-15T13:27:49Z)
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning [86.37438204416435]
Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet mastered. Decisions in Stratego are made over a large number of discrete actions with no obvious link between action and outcome. DeepNash beats existing state-of-the-art AI methods in Stratego and achieved a yearly (2022) and all-time top-3 rank on the Gravon games platform.
arXiv Detail & Related papers (2022-06-30T15:53:19Z)
DecisionHoldem: Safe Depth-Limited Solving With Diverse Opponents for Imperfect-Information Games [31.26667266662521]
DecisionHoldem is a high-level AI for heads-up no-limit Texas hold'em with safe depth-limited subgame solving. We release the source codes and tools of DecisionHoldem to promote AI development in imperfect-information games.
arXiv Detail & Related papers (2022-01-27T15:35:49Z)
TotalBotWar: A New Pseudo Real-time Multi-action Game Challenge and Competition for AI [62.997667081978825]
TotalBotWar is a new pseudo real-time multi-action challenge for game AI. The game is based on the popular TotalWar games series where players manage an army to defeat the opponent's one.
arXiv Detail & Related papers (2020-09-18T09:13:56Z)
Suphx: Mastering Mahjong with Deep Reinforcement Learning [114.68233321904623]
We design an AI for Mahjong, named Suphx, based on deep reinforcement learning with some newly introduced techniques. Suphx has demonstrated stronger performance than most top human players in terms of stable rank. This is the first time that a computer program outperforms most top human players in Mahjong.
arXiv Detail & Related papers (2020-03-30T16:18:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.