Memory As A Monadic Control Construct In Problem-Solving

TL;DR

This paper introduces a categorical formalization of memory as a control structure in AI inference systems, providing a theoretical framework that captures the interaction between learning and problem-solving.

Contribution

It presents a novel categorical approach to model memory as a control construct, bridging formal language theory and AI system performance analysis.

Findings

Categorical triples effectively model memory interactions in AI systems.

The framework generalizes control mechanisms across different memory representations.

Enhanced understanding of memory's role in inference and problem-solving.

Abstract

Recent advances in programming languages study and design have established a standard way of grounding computational systems representation in category theory. These formal results led to a better understanding of issues of control and side-effects in functional and imperative languages. This framework can be successfully applied to the investigation of the performance of Artificial Intelligence (AI) inference and cognitive systems. In this paper, we delineate a categorical formalisation of memory as a control structure driving performance in inference systems. Abstracting away control mechanisms from three widely used representations of memory in cognitive systems (scripts, production rules and clusters) we explain how categorical triples capture the interaction between learning and problem-solving.