Abstract:
This paper introduces the Integrated Semiotic Framework (ISF), a novel theoretical architecture for understanding and engineering meaningful intelligence. The ISF addresses the foundational “semantic gap” in artificial intelligence by synthesizing two complementary constructs: The Holisticite Framework (THF), which posits an objective, geometric model of reality (the General Reality Manifold or GRM) whose dynamics are governed by homeostatic and predictive principles; and the Sometic Information Framework (SIF), which provides an agent-centric interpretation of interaction within this manifold. The unification of these frameworks is achieved through a rigorous application of Charles Sanders Peirce’s triadic, process-based theory of semiotics. We demonstrate that the semiotic process (semiosis) is not merely analogous to, but can be formally mapped onto, the core computational principle of the GRM: geodesic navigation on a dynamically curved information space. This mapping provides a physically-grounded, non-symbolic resolution to the Symbol Grounding Problem, defining meaning not as a property of symbols, but as an intrinsic geometric property of an agent’s state-space trajectory. We explore the profound implications of the ISF for developing truly context-aware robotics, creating shared reality models for human-robot interaction, and establishing a new basis for machine ethics grounded in the principle of “geodesic integrity.”
Yıldırım, E. (2025). The Integrated Semiotic Framework (ISF): A Geometric Synthesis of Objective Reality and Subjective Experience for Embodied Artificial Intelligence. Zenodo. https://doi.org/10.5281/zenodo.17050397
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