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

arXiv: https://arxiv.org/abs/2601.07964 · HTML: https://mlsanity.com/paper/2601.07964/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 4 of 4)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Subject-Event Ontology Without Global Time: Foundations and Execution Semantics](https://mlsanity.com/paper/2510.18040/) (2025)
- [Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture](https://mlsanity.com/paper/2509.09775/) (2025)
- [Behavior Trees vs Executable Ontologies: a Comparative Analysis of Robot Control Paradigms](https://mlsanity.com/paper/2511.15274/) (2025)

## Abstract (arXiv)

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.