Conceptual Framework for Autonomous Cognitive Entities
- URL: http://arxiv.org/abs/2310.06775v2
- Date: Wed, 1 Nov 2023 16:28:29 GMT
- Title: Conceptual Framework for Autonomous Cognitive Entities
- Authors: David Shapiro, Wangfan Li, Manuel Delaflor, Carlos Toxtli
- Abstract summary: This paper introduces the Autonomous Cognitive Entity model, a novel framework for a cognitive architecture.
The model is designed to harness the capabilities of the latest generative AI technologies, including large language models (LLMs) and multimodal generative models (MMMs)
The ACE framework also incorporates mechanisms for handling failures and adapting actions, thereby enhancing the robustness and flexibility of autonomous agents.
- Score: 0.9285295512807729
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid development and adoption of Generative AI (GAI) technology in the
form of chatbots such as ChatGPT and Claude has greatly increased interest in
agentic machines. This paper introduces the Autonomous Cognitive Entity (ACE)
model, a novel framework for a cognitive architecture, enabling machines and
software agents to operate more independently. Drawing inspiration from the OSI
model, the ACE framework presents layers of abstraction to conceptualize
artificial cognitive architectures. The model is designed to harness the
capabilities of the latest generative AI technologies, including large language
models (LLMs) and multimodal generative models (MMMs), to build autonomous,
agentic systems. The ACE framework comprises six layers: the Aspirational
Layer, Global Strategy, Agent Model, Executive Function, Cognitive Control, and
Task Prosecution. Each layer plays a distinct role, ranging from setting the
moral compass and strategic thinking to task selection and execution. The ACE
framework also incorporates mechanisms for handling failures and adapting
actions, thereby enhancing the robustness and flexibility of autonomous agents.
This paper introduces the conceptual framework and proposes implementation
strategies that have been tested and observed in industry. The goal of this
paper is to formalize this framework so as to be more accessible.
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