  Multiple Artificial Intelligence (AI) methods have been proposed over recent
years to create controllers to play multiple video games of different nature
and complexity without revealing the specific mechanics of each of these games
to the AI methods. In recent years, Evolutionary Algorithms (EAs) employing
rolling horizon mechanisms have achieved extraordinary results in these type of
problems. However, some limitations are present in Rolling Horizon EAs making
it a grand challenge of AI. These limitations include the wasteful mechanism of
creating a population and evolving it over a fraction of a second to propose an
action to be executed by the game agent. Another limitation is to use a scalar
value (fitness value) to direct evolutionary search instead of accounting for a
mechanism that informs us how a particular agent behaves during the rolling
horizon simulation. In this work, we address both of these issues. We introduce
the use of a statistical tree that tackles the latter limitation. Furthermore,
we tackle the former limitation by employing a mechanism that allows us to seed
part of the population using Monte Carlo Tree Search, a method that has
dominated multiple General Video Game AI competitions. We show how the proposed
novel mechanism, called Statistical Tree-based Population Seeding, achieves
better results compared to vanilla Rolling Horizon EAs in a set of 20 games,
including 10 stochastic and 10 deterministic games.
