%0 Generic %1 shapiro2023conceptual %A Shapiro, David %A Li, Wangfan %A Delaflor, Manuel %A Toxtli, Carlos %D 2023 %K %T Conceptual Framework for Autonomous Cognitive Entities %U http://arxiv.org/abs/2310.06775 %X 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.

@misc{shapiro2023conceptual, 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.}, added-at = {2023-10-12T05:06:21.000+0200}, author = {Shapiro, David and Li, Wangfan and Delaflor, Manuel and Toxtli, Carlos}, biburl = {https://www.bibsonomy.org/bibtex/2b0e5f8ac5b5d4fc789940c2db52e217b/carlostoxtli}, interhash = {44bf025cdd3ff06596db102f7be68a9d}, intrahash = {b0e5f8ac5b5d4fc789940c2db52e217b}, keywords = {}, note = {cite arxiv:2310.06775Comment: 34 pages, 12 figures}, timestamp = {2023-10-12T05:06:21.000+0200}, title = {Conceptual Framework for Autonomous Cognitive Entities}, url = {http://arxiv.org/abs/2310.06775}, year = 2023 }