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InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent

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arxiv 2308.01552 v1 pith:KSBXVBCC submitted 2023-08-03 cs.AI cs.CLcs.LG

Po-Lin Chen , Cheng-Shang Chang This is my paper · ORCID
classification cs.AIcs.CLcs.LG
keywords chatgptagentinteractresearchrolestasksaccordingadvancements
verification ladder T0 review T1 audit T2 compute T3 formal
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This research paper delves into the integration of OpenAI's ChatGPT into embodied agent systems, evaluating its influence on interactive decision-making benchmark. Drawing a parallel to the concept of people assuming roles according to their unique strengths, we introduce InterAct. In this approach, we feed ChatGPT with varied prompts, assigning it a numerous roles like a checker and a sorter, then integrating them with the original language model. Our research shows a remarkable success rate of 98% in AlfWorld, which consists of 6 different tasks in a simulated household environment, emphasizing the significance of proficient prompt engineering. The results highlight ChatGPT's competence in comprehending and performing intricate tasks effectively in real-world settings, thus paving the way for further advancements in task planning.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 7 citations worldwide. Full citation record

  1. Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning

    cs.RO 2025-08 reject novelty 4.0 of 10

    A review that categorizes large-model-empowered embodied AI into hierarchical and end-to-end decision-making, imitation and reinforcement learning, and world models.

  2. Brain-inspired AI Agent: The Way Towards AGI

    cs.NE 2024-12 reject novelty 4.0 of 10

    The paper proposes a brain-inspired agent architecture built from cortical-region modules and functional connectivity networks as a conceptual route to AGI, without empirical validation.

  3. AI Agent for Education: von Neumann Multi-Agent System Framework

    cs.MA 2024-12 conditional novelty 2.0 of 10

    A conceptual framework that maps large language model based teaching agents onto a von Neumann computer architecture, organizing techniques like chain-of-thought and agent debate into four operation types.

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