Associate Prof Ian Watson – The University of Auckland, New Zealand
Successful Performance via Decision Generalisation in No Limit Texas Hold’em
Given a set of data, recorded by observing the decisions of an expert player, we present a case-based framework that allows the successful generalisation of those decisions in the game of no limit Texas Hold’em. The transition from a limit betting structure to a no limit betting structure offers challenging problems that are not faced in the limit domain. In particular, we address the problems of determining a suitable action abstraction and the resulting state translation that is required to map real-value bet amounts into a discrete set of abstract actions. We also detail the similarity metrics used in order to identify similar scenarios, without which no generalisation of playing decisions would be possible. We show that we were able to successfully generalise no limit betting decisions from recorded data via our agent, SartreNL, which achieved a 2nd place finish at the 2010 Annual Computer Poker Competition.
Associate Professor Ian Watson, Dept of Computer Science, The University of Auckland and SICSA Distinguished Visiting Fellow hosted by RGU.
Ian Watson has a PhD in Computer Science from Liverpool University. His career has involved the practical application of many areas of Artificial Intelligence research, including Knowledge Engineering and Expert Systems. His recent interests are in Case-Based Reasoning and Game AI.
School of Computing Science & Digital Media, Robert Gordon University, Riverside East, Garthdee, Aberdeen, Conference Room N204, 13:00 – 14:30.