Explanatory Dialogues with Active Learning for Rule-based Expertise

Yao, Yao, González-Vélez, Horacio and Croitoru, Madalina (2024) Explanatory Dialogues with Active Learning for Rule-based Expertise. In: Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning 2024 (RuleML+RR 2024). CEUR-WS, Bucharest, Romania, pp. 1-15.

Preview

PDF Download (853kB) | Preview

Abstract

Contemporary language models have enhanced the interaction capabilities of AI with users. The improved understanding and processing abilities of AI with respect to the provided data, thanks to these language models, have simplified related knowledge engineering tasks. In this research, we embed LLMs in computational agents to reinforce the interaction between the system and expert users to improve knowledge engineering processes. By combining explanatory dialogue and active learning into knowledge engineering pipelines, we provide a framework that can help experts validate rule-based expertise in a specific domain. This validated expertise can be represented in RuleML format and is available to support knowledge-driven AI applications in domain-specific tasks. Our initial test indicates that such an integration is feasible and improves the overall usability of knowledge engineering processes, using curriculum development scenarios from DIGITAL4Business, a four-year EU-funded project to deliver a new European Master’s programme on the practical application of advanced digital skills within European SMEs and companies.

Item Type:	Book Section
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords:	Multi-agent Systems; Active Learning; RuleML; Explanatory Dialogues; LLMs; DIGITAL4Business
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	07 Feb 2025 16:40
Last Modified:	07 Feb 2025 16:40
URI:	https://norma.ncirl.ie/id/eprint/7324

Actions (login required)

View Item