Summary of Itcma: a Generative Agent Based on a Computational Consciousness Structure, by Hanzhong Zhang et al.
ITCMA: A Generative Agent Based on a Computational Consciousness Structure
by Hanzhong Zhang, Jibin Yin, Haoyang Wang, Ziwei Xiang
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Neurons and Cognition (q-bio.NC)
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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 Large Language Models (LLMs) face challenges when understanding implicit instructions and applying common-sense knowledge. This paper introduces the Internal Time-Consciousness Machine (ITCM), a computational structure simulating human consciousness, and proposes the ITCM-based Agent (ITCMA). ITCMA supports action generation and reasoning in open-world settings, enhancing LLMs’ ability to understand implicit instructions and apply common-sense knowledge. Evaluations show that trained ITCMA outperforms state-of-the-art models by 9%, while untrained ITCMA achieves a 96% task completion rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand instructions and make smart decisions. It introduces a new way for computers to think, called the Internal Time-Consciousness Machine (ITCM). This machine lets computers reason and take actions in real-world situations, making them more helpful and reliable. The computer can even solve problems on its own without needing human help. |
