{
  "annotation_id": "2e936f13-eb39-443b-87b0-5e5781aa5019",
  "date_created": "2026-02-17T05:53:15.048000Z",
  "date_modified": "2026-02-17T05:53:15.048000Z",
  "file_hash": "835daf1f6213f347b8ab02a68d426602e9a551176f8121f7b1a636bb295ffdb9",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.07964",
    "authors": [
      "Alexander Boldachev"
    ],
    "categories": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "title": "Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling",
    "url": "https://arxiv.org/abs/2601.07964",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c43f9900-8b93-4129-bfa3-07178489611c",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}