Playing a Strategy Game with Knowledge-Based Reinforcement Learning
  This paper presents Knowledge-Based Reinforcement Learning (KB-RL) as a
method that combines a knowledge-based approach and a reinforcement learning
(RL) technique into one method for intelligent problem solving. The proposed
approach focuses on multi-expert knowledge acquisition, with the reinforcement
learning being applied as a conflict resolution strategy aimed at integrating
the knowledge of multiple exerts into one knowledge base.
  The article describes the KB-RL approach in detail and applies the reported
method to one of the most challenging problems of current Artificial
Intelligence (AI) research, namely playing a strategy game. The results show
that the KB-RL system is able to play and complete the full FreeCiv game, and
to win against the computer players in various game settings. Moreover, with
more games played, the system improves the gameplay by shortening the number of
rounds that it takes to win the game.
  Overall, the reported experiment supports the idea that, based on human
knowledge and empowered by reinforcement learning, the KB-RL system can deliver
a strong solution to the complex, multi-strategic problems, and, mainly, to
improve the solution with increased experience.
