Identifying Features for Bluff Detection in No-Limit Texas Hold'em

摘要

Abstract Poker is increasingly becoming an area of interest in AI research, partly because of the complex qualities it exhibits which are absent from more traditionally studied games, such as chess. One of the most difficult but also most important aspects of poker is the need to infer information about your opponent while also handling his attempts at disinformation. This problem of "opponent modeling" is a central aspect of poker agent design and has been approached in many different ways. In this paper we focus on one subset of the opponent modelling problem, namely that of bluff detection. We explore the effectiveness of different feature sets towards this task and test the ease with which the bluffs of various poker agents can be detected.