  In many games, moves consist of several decisions made by the player. These
decisions can be viewed as separate moves, which is already a common practice
in multi-action games for efficiency reasons. Such division of a player move
into a sequence of simpler / lower level moves is called \emph{splitting}. So
far, split moves have been applied only in forementioned straightforward cases,
and furthermore, there was almost no study revealing its impact on agents'
playing strength. Taking the knowledge-free perspective, we aim to answer how
to effectively use split moves within Monte-Carlo Tree Search (MCTS) and what
is the practical impact of split design on agents' strength. This paper
proposes a generalization of MCTS that works with arbitrarily split moves. We
design several variations of the algorithm and try to measure the impact of
split moves separately on efficiency, quality of MCTS, simulations, and
action-based heuristics. The tests are carried out on a set of board games and
performed using the Regular Boardgames General Game Playing formalism, where
split strategies of different granularity can be automatically derived based on
an abstract description of the game. The results give an overview of the
behavior of agents using split design in different ways. We conclude that split
design can be greatly beneficial for single- as well as multi-action games.
