Action is the primary key: a categorical framework for episodic memories and logical reasoning

TL;DR

This paper introduces cognitive-logs, a relational and graph database format for episodic memory that supports logical reasoning and inference, aiming to enhance AI's cognitive capabilities with a human-like understanding.

Contribution

It proposes a novel data format based on category theory for episodic memories, enabling rigorous logical reasoning and inference in AI systems.

Findings

Cognitive-logs effectively model episodic memories as graph networks.

Operations enable complex reasoning like planning and story abstraction.

Supports large-scale knowledge storage surpassing neural networks.

Abstract

This study presents data format of episodic memory for artificial intelligence and cognitive science. The data format, named cognitive-logs, enables rigour and flexible logical reasoning. Cognitive-logs consist of a set of relational and graph databases. Cognitive-logs store an episodic memory as a graphical network that consist of "actions" represented by verbs in natural languages and "participants" who perform the actions. These objects are connected by arrows (morphisms) that bind each action to its participant and bind causes and effects. The design principle of cognitive-logs refers cognitive sciences especially in cognitive linguistics. Logical reasoning is the processes of comparing causal chains in episodic memories with known rules which are also recorded in the cognitive-logs. Operations based on category theory enable such comparisons between episodic memories or scenarios. These operations represent various inferences including planning, comprehensions, and hierarchical abstractions of stories. The goal of this study is to develop a database-driven artificial intelligence that thinks like a human but possesses the accuracy and rigour of a machine. The vast capacities of databases (up to petabyte scales in current technologies) enable the artificial intelligence to store a greater volume of knowledge than neural-network based artificial intelligences. Cognitive-logs also serve as a model of human cognition mind activities.