Summary of Knowledge Acquisition For Dialogue Agents Using Reinforcement Learning on Graph Representations, by Selene Baez Santamaria et al.
Knowledge acquisition for dialogue agents using reinforcement learning on graph representations
by Selene Baez Santamaria, Shihan Wang, Piek Vossen
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop an artificial agent capable of augmenting its knowledge base through conversations with other agents. The agent represents its knowledge as a graph and uses patterns around new beliefs to generate responses. To learn policies for selecting effective graph patterns during interactions, the team employs reinforcement learning without relying on explicit user feedback. This study demonstrates the potential of leveraging users as sources of information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how artificial agents can improve their knowledge by talking with other agents and people. The agent uses a special kind of map called an RDF graph to organize its ideas and learn new things from conversations. When it responds, it looks for patterns around these new ideas. The researchers used a type of learning called reinforcement learning to help the agent make good choices during interactions without needing feedback from others. This is an interesting idea that could help agents become smarter by talking with people. |
Keywords
» Artificial intelligence » Knowledge base » Reinforcement learning
