Knowledge acquisition for dialogue agents using reinforcement learning on graph representations

We develop an artificial agent motivated to augment its knowledge base beyond its initial training. The agent actively participates in dialogues with other agents, strategically acquiring new information. The agent models its knowledge as an RDF knowledge graph, integrating new beliefs acquired thro...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Selene Baez Santamaria, Wang, Shihan, Vossen, Piek
Format: Article
Language:English
Subjects:
Agents (artificial intelligence)
Graphical representations
Knowledge acquisition
Knowledge bases (artificial intelligence)
Knowledge representation
Online Access:Get full text
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Summary:We develop an artificial agent motivated to augment its knowledge base beyond its initial training. The agent actively participates in dialogues with other agents, strategically acquiring new information. The agent models its knowledge as an RDF knowledge graph, integrating new beliefs acquired through conversation. Responses in dialogue are generated by identifying graph patterns around these new integrated beliefs. We show that policies can be learned using reinforcement learning to select effective graph patterns during an interaction, without relying on explicit user feedback. Within this context, our study is a proof of concept for leveraging users as effective sources of information.
ISSN:2331-8422