【英文】CoALA: 通向AGI的LLM智能体认知架构框架

CoALA builds on the history of cognitive architectures and production systems in artificial intelligence, which are systems that learn and reason using rules and logic. The paper discusses the analogy between these systems and large language models (LLMs), which are trained on massive datasets of text and can generate human-like text. By incorporating LLMs into a cognitive architecture, CoALA suggests that language agents can be designed with modular memory components, a structured action space for interacting with both internal memory and external environments, and a generalized decision-making process to choose actions. The paper uses CoALA to analyze existing language agents and outlines potential directions for future research.