Noname manuscript No. (will be inserted by the editor) Playing a Strategy Game with Knowledge-Based Reinforcement Learning Viktor Voss · Liudmyla Nechepurenko · Dr. Rudi Schaefer · Steffen Bauer Received: date / Accepted: date Abstract 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 pro- posed approach focuses on multi-expert knowledge acquisition, with the reinforce- ment 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 de- liver a strong solution to the complex, multi-strategic problems, and, mainly, to improve the solution with increased experience. Keywords Knowledge-Based Systems · Reinforcement Learning · Multi-Expert Knowledge Base · Conflict Resolution V. Voss arago GmbH, E-mail:
[email protected] L. Nechepurenko arago GmbH, E-mail:
[email protected] Dr. R. Schaefer arXiv:1908.05472v1 [cs.AI] 15 Aug 2019 arago GmbH, E-mail:
[email protected] S.