Ensemble Decision Systems for General Video Game Playing
This work addresses the challenge of enhancing algorithm adaptability in video game AI, though it appears incremental as it builds on existing ensemble methods.
The paper tackles the problem of improving decision-making in general video game playing by using Ensemble Decision Systems, which combine multiple algorithms to leverage complementary strengths, resulting in increased generality without significant performance loss.
Ensemble Decision Systems offer a unique form of decision making that allows a collection of algorithms to reason together about a problem. Each individual algorithm has its own inherent strengths and weaknesses, and often it is difficult to overcome the weaknesses while retaining the strengths. Instead of altering the properties of the algorithm, the Ensemble Decision System augments the performance with other algorithms that have complementing strengths. This work outlines different options for building an Ensemble Decision System as well as providing analysis on its performance compared to the individual components of the system with interesting results, showing an increase in the generality of the algorithms without significantly impeding performance.