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Compositional neural logic programming
Citation
Tran, SN, Compositional neural logic programming, Proceedings of the 30th International Joint Conference on Artificial Intelligence, 19-26 August 2021, Virtual Conference, Online (Montreal, Canada), pp. 3059-3066. ISBN 978-0-9992411-9-6 (2021) [Refereed Conference Paper]
Copyright Statement
Copyright 2021 International Joint Conferences on Artificial Intelligence
DOI: doi:10.24963/ijcai.2021/421
Abstract
This paper introduces Compositional Neural Logic Programming (CNLP), a framework that integrates neural networks and logic programming for symbolic and sub-symbolic reasoning. We adopt the idea of compositional neural networks to represent first-order logic predicates and rules. A voting backward-forward chaining algorithm is proposed for inference with both symbolic and sub-symbolic variables in an argument-retrieval style. The framework is highly flexible in that it can be constructed incrementally with new knowledge, and it also supports batch reasoning in certain cases. In the experiments, we demonstrate the advantages of CNLP in discriminative tasks and generative tasks.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | deep learning, reasoning |
| Research Division: | Information and Computing Sciences |
| Research Group: | Artificial intelligence |
| Research Field: | Knowledge representation and reasoning |
| Objective Division: | Information and Communication Services |
| Objective Group: | Information systems, technologies and services |
| Objective Field: | Artificial intelligence |
| UTAS Author: | Tran, SN (Dr Son Tran) |
| ID Code: | 146293 |
| Year Published: | 2021 |
| Deposited By: | Information and Communication Technology |
| Deposited On: | 2021-08-27 |
| Last Modified: | 2022-05-18 |
| Downloads: | 0 |
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