{"ID":2832956,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04714","arxiv_id":"2512.04714","title":"Playing the Player: A Heuristic Framework for Adaptive Poker AI","abstract":"For years, the discourse around poker AI has been dominated by the concept of solvers and the pursuit of unexploitable, machine-perfect play. This paper challenges that orthodoxy. It presents Patrick, an AI built on the contrary philosophy: that the path to victory lies not in being unexploitable, but in being maximally exploitative. Patrick's architecture is a purpose-built engine for understanding and attacking the flawed, psychological, and often irrational nature of human opponents. Through detailed analysis of its design, its novel prediction-anchored learning method, and its profitable performance in a 64,267-hand trial, this paper makes the case that the solved myth is a distraction from the real, far more interesting challenge: creating AI that can master the art of human imperfection.","short_abstract":"For years, the discourse around poker AI has been dominated by the concept of solvers and the pursuit of unexploitable, machine-perfect play. This paper challenges that orthodoxy. It presents Patrick, an AI built on the contrary philosophy: that the path to victory lies not in being unexploitable, but in being maximall...","url_abs":"https://arxiv.org/abs/2512.04714","url_pdf":"https://arxiv.org/pdf/2512.04714v1","authors":"[\"Andrew Paterson\",\"Carl Sanders\"]","published":"2025-12-04T12:01:44Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.GT\"]","methods":"[]","has_code":false}