This episode explores the Internal Time-Consciousness Machine (ITCM), a new framework for generative agents designed to enhance Large Language Model (LLM)-based agents. The ITCM draws inspiration from human consciousness to improve agents' understanding of implicit instructions and common-sense reasoning, while maintaining long-term consistency.
Key points include:
* ITCM introduces a computational consciousness structure, integrating phenomenal and perceptual fields to simulate a stream of consciousness.
* The model uses retention, primal impression, and protention to manage past, present, and future experiences.
* The ITCM framework incorporates drive and emotions to guide agent behavior, using the PAD model (Pleasure, Arousal, Dominance) to influence decision-making.
* The ITCM-based Agent (ITCMA) outperformed existing models in tests, showcasing its utility in both simulated and real-world environments.
The episode highlights how this novel framework advances AI by incorporating concepts from consciousness research to create more intelligent, human-like generative agents.
https://arxiv.org/pdf/2403.20097v1