Monte Carlo Tree Search (MCTS) is an AI technique
that has been successfully applied to many deterministic games
of perfect information, leading to large advances in a number of domains,
such as Go and General Game Playing.
Imperfect information games are less well studied in the field of AI
despite being popular and of significant commercial interest,
for example in the case of computer and mobile adaptations of turn based board and card games.
This is largely because hidden information and uncertainty
leads to a large increase in complexity compared to perfect information games.
In this thesis MCTS is extended to games with hidden information and uncertainty
through the introduction of the Information Set MCTS (ISMCTS) family of algorithms.
It is demonstrated that ISMCTS can handle hidden information and uncertainty
in a variety of complex board and card games.
This is achieved whilst preserving the general applicability of MCTS
and using computational budgets appropriate for use in a commercial game.
The ISMCTS algorithm is shown to outperform the existing approach of Perfect Information Monte Carlo (PIMC) search.
Additionally it is shown that ISMCTS can be used to solve two known issues with PIMC search,
namely strategy fusion and non-locality.
ISMCTS has been integrated into a commercial game, Spades by AI Factory,
with over 2.5 million downloads.
The Information Capture And ReUSe (ICARUS) framework is also introduced in this thesis.
The ICARUS framework generalises MCTS enhancements in terms of information capture (from MCTS simulations)
and reuse (to improve MCTS tree and simulation policies).
The ICARUS framework is used to express existing enhancements,
to provide a tool to design new ones,
and to rigorously define how MCTS enhancements can be combined.
The ICARUS framework is tested across a wide variety of games
