ITCMA: A Generative Agent Based on a Computational Consciousness Structure
<|reference_start|>ITCMA: A Generative Agent Based on a Computational Consciousness Structure: Large Language Models (LLMs) still face challenges in tasks requiring understanding implicit instructions and applying common-sense knowledge. In such scenarios, LLMs may require multiple attempts to achieve human-level performance, potentially leading to inaccurate responses or inferences in practical environments, affecting their long-term consistency and behavior. This paper introduces the Internal Time-Consciousness Machine (ITCM), a computational consciousness structure to simulate the process of human consciousness. We further propose the ITCM-based Agent (ITCMA), which supports action generation and reasoning in open-world settings, and can independently complete tasks. ITCMA enhances LLMs' ability to understand implicit instructions and apply common-sense knowledge by considering agents' interaction and reasoning with the environment. Evaluations in the Alfworld environment show that trained ITCMA outperforms the state-of-the-art (SOTA) by 9% on the seen set. Even untrained ITCMA achieves a 96% task completion rate on the seen set, 5% higher than SOTA, indicating its superiority over traditional intelligent agents in utility and generalization. In real-world tasks with quadruped robots, the untrained ITCMA achieves an 85% task completion rate, which is close to its performance in the unseen set, demonstrating its comparable utility and universality in real-world settings.<|reference_end|>
@article{zhang2024itcma:,
    title={ITCMA: A Generative Agent Based on a Computational Consciousness
  Structure},
    author={Hanzhong Zhang, Jibin Yin, Haoyang Wang, Ziwei Xiang},
    journal={arXiv preprint arXiv:2403.20097},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.20097},
    primaryClass={cs.AI cs.HC q-bio.NC}
}