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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.12486 |
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| _version_ | 1866910914492497920 |
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| author | Xu, Bowen |
| author_facet | Xu, Bowen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_12486 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Brain-Inspired Sequence Learning Model based on a Logic Xu, Bowen Artificial Intelligence 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. |
| title | A Brain-Inspired Sequence Learning Model based on a Logic |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2308.12486 |