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Combining Cognitive and Generative AI for Self-explanation in Interactive AI Agents

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arxiv 2407.18335 v1 pith:MVXIZ2FN submitted 2024-07-25 cs.AI

Shalini Sushri , Rahul Dass , Rhea Basappa , Hong Lu , Ashok Goel This is my paper · ORCID
classification cs.AI
keywords veracognitivegenerativeagentsexplanationsinteractivemodelmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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The Virtual Experimental Research Assistant (VERA) is an inquiry-based learning environment that empowers a learner to build conceptual models of complex ecological systems and experiment with agent-based simulations of the models. This study investigates the convergence of cognitive AI and generative AI for self-explanation in interactive AI agents such as VERA. From a cognitive AI viewpoint, we endow VERA with a functional model of its own design, knowledge, and reasoning represented in the Task--Method--Knowledge (TMK) language. From the perspective of generative AI, we use ChatGPT, LangChain, and Chain-of-Thought to answer user questions based on the VERA TMK model. Thus, we combine cognitive and generative AI to generate explanations about how VERA works and produces its answers. The preliminary evaluation of the generation of explanations in VERA on a bank of 66 questions derived from earlier work appears promising.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimizing Web-Based AI Query Retrieval with GPT Integration in LangChain A CoT-Enhanced Prompt Engineering Approach

    cs.HC 2025-06 reject novelty 3.0 of 10

    A LangChain plus GPT-4o retrieval pipeline with chain-of-thought prompting is claimed to beat GPT-4o on two QA benchmarks, but the headline numbers are inconsistent with the stated dataset size.

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