|dc.description.abstract||Poker has become the subject of an increasing amount of study in the computational intelligence
community. The element of imperfect information presents new and greater
challenges than those previously posed by games such as checkers and chess. Advances
in computer poker have great potential, since reasoning under conditions of
uncertainty is typical of many real world problems.
To date the focus of computer poker research has centred on the development of
ring game players for limit Texas hold’em. For a computer to compete in the most
prestigious poker events, however, it will be required to play in a tournament setting
with a no-limit betting structure. This thesis is the first academic attempt to investigate
the underlying dynamics of successful no-limit tournament poker play. Professional
players have proffered advice in the non-academic poker literature on correct strategies
for tournament poker play. This study seeks to empirically validate their suggestions
on a simplified no-limit Texas hold’em tournament framework.
Starting by using exhaustive simulations, we first assess the hypothesis that a strategy
including information related to game-specific factors performs better than one
founded on hand strength knowledge alone. Specifically, we demonstrate that the use
of information pertaining to one’s seating position, the opponents’ prior actions, the
stage of the tournament, and one’s chip stack size all contribute towards a statistically
significant improvement in the number of tournaments won.
In extending the research to combine all factors we explain the limitations of the
exhaustive simulation approach, and introduce evolutionary algorithms as a method of
searching the strategy space. We then test the hypothesis that a strategy which combines
information from all the aforementioned factors performs better than one which
employs only a single factor. We show that an evolutionary algorithm is successfully
able to resolve conflicting signals from the specified factors, and that the resulting
strategies are statistically stronger than those previously discovered.
Our research continues with an analysis of the results, as we interpret them in the
context of poker strategy. We compare our findings to poker authors’ recommendations,
and conclude with a discussion on the many possible extensions to this work.||en