Reasoning with Ontologies for Non-player Character’s Decision-Making in Games


In most games, the decision-making of non-player characters (NPCs) is usually constructed using variants of state machines, behaviour trees, utility-based AI or planning. These methods are relatively simple to implement, but have drawbacks in that it can be difficult to create complex non-hardcoded behaviour for many agents and to maintain the algorithms, especially when scaling up. We show that logic programming can be used to overcome these limitations. This new approach is intuitive because game designers usually think of their games with rules that closely resemble logic rules. A methodology is introduced to design both gen eral and modular behaviour using hierarchical and scalable ontologies. Moreover, we show that this approach can be seamlessly combined with the well-founded semantics (WFS) to solve the problem of representation and reasoning despite the lack of NPC knowledge.

Data Scientist & PhD Student

My interests include Artificial Intelligence, Data Science, Machine Learning and Games.