Knowledge acquisition for dialogue agents using reinforcement learning on graph representations
This work addresses the challenge of knowledge acquisition for dialogue agents, but it is incremental as it builds on existing graph-based and reinforcement learning methods.
The paper tackles the problem of enabling dialogue agents to autonomously expand their knowledge base through conversations, using reinforcement learning on graph representations to learn policies for selecting effective graph patterns, resulting in a proof-of-concept demonstration without relying on explicit user feedback.
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.