Related papers: Cognitive Architectures for Language Agents

Cognitive Architectures for Language Agents

URL: http://arxiv.org/abs/2309.02427v3
Date: Fri, 15 Mar 2024 15:44:11 GMT
Title: Cognitive Architectures for Language Agents
Authors: Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths,
Abstract summary: We propose Cognitive Architectures for Language Agents (CoALA) CoALA describes a language agent with modular memory components, a structured action space to interact with internal memory and external environments, and a generalized decision-making process to choose actions. We use CoALA to retrospectively survey and organize a large body of recent work, and prospectively identify actionable directions towards more capable agents.
Score: 44.89258267600489
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Recent efforts have augmented large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning, leading to a new class of language agents. While these agents have achieved substantial empirical success, we lack a systematic framework to organize existing agents and plan future developments. In this paper, we draw on the rich history of cognitive science and symbolic artificial intelligence to propose Cognitive Architectures for Language Agents (CoALA). CoALA describes a language agent with modular memory components, a structured action space to interact with internal memory and external environments, and a generalized decision-making process to choose actions. We use CoALA to retrospectively survey and organize a large body of recent work, and prospectively identify actionable directions towards more capable agents. Taken together, CoALA contextualizes today's language agents within the broader history of AI and outlines a path towards language-based general intelligence.

Related papers

Symbolic Learning Enables Self-Evolving Agents [55.625275970720374]
We introduce agent symbolic learning, a systematic framework that enables language agents to optimize themselves on their own. Agent symbolic learning is designed to optimize the symbolic network within language agents by mimicking two fundamental algorithms in connectionist learning. We conduct proof-of-concept experiments on both standard benchmarks and complex real-world tasks.
arXiv Detail & Related papers (2024-06-26T17:59:18Z)
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents [54.09074527006576]
Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges. This inadequacy primarily stems from the lack of built-in action knowledge in language agents. We introduce KnowAgent, a novel approach designed to enhance the planning capabilities of LLMs by incorporating explicit action knowledge.
arXiv Detail & Related papers (2024-03-05T16:39:12Z)
Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects [32.91556128291915]
This paper surveys current research to provide an in-depth overview of intelligent agents within single and multi-agent systems. It covers their definitions, research frameworks, and foundational components such as their composition, cognitive and planning methods, tool utilization, and responses to environmental feedback. We conclude by envisioning prospects for LLM-based agents, considering the evolving landscape of AI and natural language processing.
arXiv Detail & Related papers (2024-01-07T09:08:24Z)
Agents: An Open-source Framework for Autonomous Language Agents [98.91085725608917]
We consider language agents as a promising direction towards artificial general intelligence. We release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience.
arXiv Detail & Related papers (2023-09-14T17:18:25Z)
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization [103.70896967077294]
This paper introduces a principled framework for reinforcing large language agents by learning a retrospective model. Our proposed agent architecture learns from rewards across multiple environments and tasks, for fine-tuning a pre-trained language model. Experimental results on various tasks demonstrate that the language agents improve over time.
arXiv Detail & Related papers (2023-08-04T06:14:23Z)
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning [56.07190845063208]
We ask: can embodied reinforcement learning (RL) agents indirectly learn language from non-language tasks? We design an office navigation environment, where the agent's goal is to find a particular office, and office locations differ in different buildings (i.e., tasks) We find RL agents indeed are able to indirectly learn language. Agents trained with current meta-RL algorithms successfully generalize to reading floor plans with held-out layouts and language phrases.
arXiv Detail & Related papers (2023-06-14T09:48:48Z)
Knowledge Engineering in the Long Game of Artificial Intelligence: The Case of Speech Acts [0.6445605125467572]
This paper describes principles and practices of knowledge engineering that enable the development of holistic language-endowed intelligent agents. We focus on dialog act modeling, a task that has been widely pursued in linguistics, cognitive modeling, and statistical natural language processing.
arXiv Detail & Related papers (2022-02-02T14:05:12Z)
Language Generation for Broad-Coverage, Explainable Cognitive Systems [0.0]
This paper describes recent progress on natural language generation for language-endowed intelligent agents (LEIAs) developed within the OntoAgent cognitive architecture. It uses the same knowledge bases, theory of computational linguistics, agent architecture, and methodology of developing broad-coverage capabilities over time while still supporting near-term applications.
arXiv Detail & Related papers (2022-01-25T16:09:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.