Automatic Rule Ordering for Dynamic Scripting

Abstract

The goal of adaptive game AI is to enhance computer-controlled game-playing agents with (1) the ability to self-correct mistakes, and (2) creativity in responding to new situations. Dynamic scripting is a reinforcement learning technique that realises fast and reliable online adaptation of game AI. It employs knowledge bases which contain rules that can be included in game scripts. To be successful, dynamic scripting requires a mechanism to order the rules that are selected for scripts. So far, rule ordering was achieved by a manually-tuned priority value for each rule. In the present research, we propose three mechanisms to order rules automatically for dynamic scripting. We performed experiments in which we let dynamic scripting, using each of the three mechanisms, play against manually-designed tactics. Our results show that dynamic scripting with automatic rule ordering generates game AI that is at least as effective as dynamic scripting with manually-tuned priority values. Moreover, it has the ability to generate novel game AI with significantly increased effectiveness. The costs are a slight decrease in learning efficiency. So, we may conclude that automatic rule ordering is a valuable enhancement for dynamic scripting.