Greetings ! I wanted to share with you my first AI-related paper which will be published soon. I might (or maybe not) upload the whole paper in another post later to gain your reviews, but for now, I’m showing the abstract and keywords.
Research in learning and planning in real-time strategy (RTS) games is very interesting in several industries such as military industry, robotics, and most importantly game industry. A Recent published work on online case-based planning in RTS Games does not include the capability of online learning from experience, so the knowledge certainty remains constant, which leads to inefficient decisions. In this paper, an intelligent agent model based on both online case-based planning (OLCBP) and reinforcement learning (RL) techniques is proposed. In addition, the proposed model has been evaluated using empirical simulation on Wargus (an open-source clone of the well known Real-Time Strategy Game Warcraft 2). This evaluation shows that the proposed model increases the certainty of the case base by learning from experience, and hence the process of decision making for selecting more efficient, effective and successful plans.