Summary of Combining Cognitive and Generative Ai For Self-explanation in Interactive Ai Agents, by Shalini Sushri et al.
Combining Cognitive and Generative AI for Self-explanation in Interactive AI Agents
by Shalini Sushri, Rahul Dass, Rhea Basappa, Hong Lu, Ashok Goel
First submitted to arxiv on: 25 Jul 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 The Virtual Experimental Research Assistant (VERA) is an inquiry-based learning environment that enables learners to build conceptual models of complex ecological systems and experiment with agent-based simulations. This study combines cognitive AI and generative AI to endow VERA with a functional model of its own design, knowledge, and reasoning. The system uses ChatGPT, LangChain, and Chain-of-Thought to answer user questions based on the Task–Method–Knowledge (TMK) language. The preliminary evaluation of generated explanations in VERA appears promising. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VERA is a special kind of computer program that helps people learn about complex systems like ecosystems. It lets users build models of these systems and test them out virtually. Researchers are trying to make VERA even better by combining two types of artificial intelligence: cognitive AI and generative AI. They’re using these AI technologies to help VERA explain how it works and why it gives certain answers. |
