Recent efforts have combined large language models with external resources or internal control flows to create language agents. CoALA proposes a framework organizing agents based on memory components, action spaces, and decision-making processes. Language agents leverage LLMs for reasoning, planning, and managing memory. The paper draws parallels between production systems and LLMs to propose CoALA as a conceptual framework for designing general-purpose language agents.
The paper discusses the history of production systems and cognitive architectures in AI research. It introduces the concept of CoALA as a way to structure language agents with modular memory components, structured action spaces, and decision-making procedures. The proposed framework aims to organize existing work on language agents and guide future developments towards more capable agents.
CoALA organizes language agents along three key dimensions: information storage (working and long-term memories), action space (internal and external actions), and decision-making procedure (interactive loop with planning and execution). The framework aims to express existing agents coherently while identifying unexplored directions for developing new ones.
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by Theodore R. ... at arxiv.org 03-18-2024
https://arxiv.org/pdf/2309.02427.pdfDeeper Inquiries