On The Role of Intentionality in Knowledge Representation: Analyzing Scene Context for Cognitive Agents with a Tiny Language Model

TL;DR

This paper explores how intentionality and context can be modeled in cognitive agents using Promise Theory's Semantic Spacetime, enabling low-cost interpretation of latent intent without extensive training.

Contribution

It introduces a pragmatic approach to identify latent intentionality in data through process coherence and scale separation, applicable to simple agents with limited memory.

Findings

Identifies latent intentionality via anomalies and work assessment.

Uses scale separation to distinguish intended content from ambient context.

Provides a low computational cost method suitable for basic organisms.

Abstract

Since Searle's work deconstructing intent and intentionality in the realm of philosophy, the practical meaning of intent has received little attention in science and technology. Intentionality and context are both central to the scope of Promise Theory's model of Semantic Spacetime, used as an effective Tiny Language Model. One can identify themes and concepts from a text, on a low level (without knowledge of the specific language) by using process coherence as a guide. Any agent process can assess superficially a degree of latent `intentionality' in data by looking for anomalous multi-scale anomalies and assessing the work done to form them. Scale separation can be used to sort parts into `intended' content and `ambient context', using the spacetime coherence as a measure. This offers an elementary but pragmatic interpretation of latent intentionality for very low computational cost, and without reference to extensive training or reasoning capabilities. The process is well within the reach of basic organisms as it does not require large scale artificial probabilistic batch processing. The level of concept formation depends, however, on the memory capacity of the agent.