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Sequence learning is an essential aspect of intelligence. In ArtificialIntelligence, sequence prediction task is usually used to test a sequencelearning model. In this paper, a model of sequence learning, which isinterpretable through Non-Axiomatic Logic, is designed and tested. The learningmechanism is composed of three steps, hypothesizing, revising, and recycling,which enable the model to work under the Assumption of Insufficient Knowledgeand Resources. Synthetic datasets for sequence prediction task are generated totest the capacity of the model. The results show that the model works wellwithin different levels of difficulty. In addition, since the model adoptsconcept-centered representation, it theoretically does not suffer fromcatastrophic forgetting, and the practical results also support this property.This paper shows the potential of learning sequences in a logical way.

A Brain-Inspired Sequence Learning Model based on a Logic