Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling
This addresses the semantic-process gap in game AI architecture for game developers, offering a novel approach to agent behavior modeling.
This paper tackles the problem of rigid algorithmic behavior programming in game AI by proposing Executable Ontologies (EO) as a paradigm shift to semantic world modeling, demonstrating in a survival game scenario that EO enables priority-based task interruption through declarative rules without explicit preemption logic.
This paper examines the application of Executable Ontologies (EO), implemented through the boldsea framework, to game development. We argue that EO represents a paradigm shift: a transition from algorithmic behavior programming to semantic world modeling, where agent behavior emerges naturally from declarative domain rules rather than being explicitly coded. Using a survival game scenario (Winter Feast), we demonstrate how EO achieves prioritybased task interruption through dataflow conditions rather than explicit preemption logic. Comparison with Behavior Trees (BT) and Goal-Oriented Action Planning (GOAP) reveals that while these approaches model what agents should do, EO models when actions become possible - a fundamental difference that addresses the semantic-process gap in game AI architecture. We discuss integration strategies, debugging advantages inherent to temporal event graphs, and the potential for LLM-driven runtime model generation.