Summary of Learning Visually Grounded Domain Ontologies Via Embodied Conversation and Explanation, by Jonghyuk Park et al.
Learning Visually Grounded Domain Ontologies via Embodied Conversation and Explanation
by Jonghyuk Park, Alex Lascarides, Subramanian Ramamoorthy
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper presents a learning framework where an agent’s knowledge gaps are addressed through corrective feedback from a teacher, facilitating the recognition of distinct types of toy trucks in a low-resource visual processing scenario. The agent starts with no prior knowledge about truck types or parts and a deficient model for recognizing those parts from visual input. The teacher provides feedback to the agent’s explanations, addressing its lack of relevant knowledge via generic rules (e.g., “dump trucks have dumpers”) and correcting inaccurate part recognition via deictic statements (e.g., “this is not a dumper”). The learner uses this feedback to update its estimates of domain ontologies and probability distributions over them, as well as to refine its visual interpretation of the scene. Experimental results show that teacher-learner pairs utilizing explanations and corrections are more data-efficient than those without such a faculty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way for machines to learn from mistakes. They taught an artificial agent how to recognize different types of toy trucks by giving it hints and correcting its misunderstandings. The agent started with no idea what kinds of trucks existed or which parts they had, but with the help of these corrections, it was able to improve its knowledge over time. This new method makes machines more efficient at learning from limited data. |
Keywords
» Artificial intelligence » Probability
