Abstract
We use case injected genetic algorithms to learn how to competently play computer strategy games. Strategic computer games involve long range planning across complex dynamics and imperfect knowledge presented to players requires them to anticipate opponent moves and adapt their strategies accordingly. This work addresses the problem of acquiring and using knowledge from human players for such games. Specifically, we learn general routing information from a human player and use case-injected genetic algorithms to incorporate this acquired knowledge in subsequent planning. Results from a strike planning game show that with an appropriate representation, case injection effectively biases the genetic algorithm toward producing plans that contain important strategic elements used by human players.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
