Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling

TL;DR

This paper explores how Executable Ontologies (EO) can revolutionize game development by shifting from explicit behavior coding to semantic world modeling, enabling more natural agent behaviors and improved AI architecture.

Contribution

It introduces the boldsea framework for EO in games, demonstrating its advantages over traditional methods like BT and GOAP, and discusses integration, debugging, and future LLM-driven model generation.

Findings

EO enables priority-based task interruption via dataflow conditions.

EO models the potential for actions, not just their execution.

Comparison shows EO offers a semantic-process approach to game AI.

Abstract

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.