A Brain-Inspired Sequence Learning Model based on a Logic
This work addresses sequence prediction in AI with a brain-inspired, interpretable approach, though it appears incremental as it builds on existing logical frameworks.
The paper tackles sequence learning by designing an interpretable model based on Non-Axiomatic Logic, which performs well on synthetic datasets across difficulty levels and avoids catastrophic forgetting due to its concept-centered representation.
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.