Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908761275236352 |
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| author | Boldachev, Alexander |
| author_facet | Boldachev, Alexander |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_07964 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling Boldachev, Alexander Artificial Intelligence 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. |
| title | Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.07964 |