  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.
