AWGS-Core (Adaptive World Generation System Core) is an experimental computational framework designed for dynamic world simulation through ontological modeling, emergent systems, and hybrid semantic processing. The framework transforms natural language inputs into evolving simulated worlds composed of entities, relations, rules, memory states, and narrative outputs. Unlike traditional procedural generation systems that rely primarily on static rule sets or handcrafted pipelines, AWGS-Core explores process-oriented architectures where semantic structures evolve dynamically over time. The system combines symbolic representations, adaptive simulation logic, relational modeling, and hybrid AI-assisted semantic extraction while preserving operational continuity through deterministic fallback systems when external AI services are unavailable. The project was developed as an experimental research prototype with assistance from generative AI tools during parts of the writing, ideation, debugging, and structural refinement processes. However, the conceptual architecture, ontological design principles, simulation logic, and research direction are original contributions of the author. Potential applications include procedural generation, adaptive simulations, symbolic AI systems, game ecosystems, emergent narrative systems, and complex computational environments.
Luis Felipe Martinez Bastidas (Sun,) studied this question.
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