Playing the Player: A Heuristic Framework for Adaptive Poker AI

TL;DR

This paper introduces Patrick, a poker AI that focuses on exploiting human opponents' flaws rather than being unexploitable, demonstrating that mastery of human imperfection is key to winning.

Contribution

It presents a novel exploitative AI framework with a prediction-anchored learning method, challenging traditional unexploitable solver approaches in poker.

Findings

Patrick outperforms traditional solvers in human-like play scenarios.

The AI achieves profitable results over 64,267 hands of poker.

Exploiting human irrationality can be more effective than unexploitable strategies.

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.