  General Video Game Playing (GVGP) is a field of Artificial Intelligence where
agents play a variety of real-time video games that are unknown in advance.
This limits the use of domain-specific heuristics. Monte-Carlo Tree Search
(MCTS) is a search technique for game playing that does not rely on
domain-specific knowledge. This paper discusses eight enhancements for MCTS in
GVGP; Progressive History, N-Gram Selection Technique, Tree Reuse,
Breadth-First Tree Initialization, Loss Avoidance, Novelty-Based Pruning,
Knowledge-Based Evaluations, and Deterministic Game Detection. Some of these
are known from existing literature, and are either extended or introduced in
the context of GVGP, and some are novel enhancements for MCTS. Most
enhancements are shown to provide statistically significant increases in win
percentages when applied individually. When combined, they increase the average
win percentage over sixty different games from 31.0% to 48.4% in comparison to
a vanilla MCTS implementation, approaching a level that is competitive with the
best agents of the GVG-AI competition in 2015.
