Using probability triples as Loki-2's betting strategy and as the reweighting factor in its opponent modeling module represents a significant improvement in Loki-2's play against previous versions of Loki in self-play experiments and against human opponents on IRC. Selective sampling simulations show impressive results in self-play experiments. Against human opponents on IRC, the best results were obtained when all three enhancements were used. In self-play experiments, the playing style of the computer players certainly matches the opponents' actions generated inside the simulations. Thus, the simulation-based betting strategy successfully exploits all the weaknesses in the computer opponents' play. In the more realistic environment on IRC, the less predictable approach of the simulation-based Loki-2 paid dividends by making it more difficult for regular opponents to form a correct model of Loki-2's play.
Developing Loki is an iterative process. The work concentrates on improving an aspect of the program until it becomes apparent that another aspect is the main performance bottleneck. That problem is then addressed until it is no longer the limiting factor, and new weaknesses in the program's play are revealed. Loki-1's deterministic betting strategy was its limiting factor. This bottleneck was overcome in two ways. Probability triples provide as a probabilistic representation of betting decisions to increase unpredictability. Simulations add dynamic functionality to static betting strategies. The PT-generation function also supports better use of the information available to the Opponent Modeler, and is more tolerant of the uncertainty in the opponents' actions. However, the opponent modeling still needs to be refined. In fact, it seems that further performance gains will depend on perfecting the opponent modeling module together with improvements to the simulation-based betting strategy.
This thesis presents the first steps in using a simulation-based betting strategy and improving the reweighting process in the Opponent Modeler. These are the initial steps and there are still many to take. Some avenues to explore in Loki-2's future development are:
As experimental results point out, Loki-2 wins more money (plays better) than last-year's Loki. However, does the program play world-class level poker? It is not there yet, but many improvements are being made to its performance and there are still lots of ideas to try.