InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent
Po-Lin Chen Cheng-Shang Chang
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.