A Brain-Inspired Sequence Learning Model based on a Logic

TL;DR

This paper introduces an interpretable sequence learning model inspired by brain logic, capable of handling various difficulty levels and potentially avoiding catastrophic forgetting, demonstrated through synthetic dataset tests.

Contribution

The paper presents a novel brain-inspired, logic-based sequence learning model with a unique three-step mechanism, addressing knowledge limitations and avoiding catastrophic forgetting.

Findings

Model performs well across different difficulty levels

The logical, concept-centered approach may prevent catastrophic forgetting

Synthetic datasets validate the model's effectiveness

Abstract

Sequence learning is an essential aspect of intelligence. In Artificial Intelligence, sequence prediction task is usually used to test a sequence learning model. In this paper, a model of sequence learning, which is interpretable through Non-Axiomatic Logic, is designed and tested. The learning mechanism is composed of three steps, hypothesizing, revising, and recycling, which enable the model to work under the Assumption of Insufficient Knowledge and Resources. Synthetic datasets for sequence prediction task are generated to test the capacity of the model. The results show that the model works well within different levels of difficulty. In addition, since the model adopts concept-centered representation, it theoretically does not suffer from catastrophic forgetting, and the practical results also support this property. This paper shows the potential of learning sequences in a logical way.