Intelligent online case-based planning agent model for real-time strategy games

Abstract

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 RTS 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.