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.

Related papers

• LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Integration of Multi Active/Passive Core-Agents [0.0]
  We propose a novel LLM-based Agent Unified Modeling Framework (LLM-Agent-UMF) Our framework distinguishes between the different components of an LLM-based agent, setting LLMs and tools apart from a new element, the core-agent. We evaluate our framework by applying it to thirteen state-of-the-art agents, thereby demonstrating its alignment with their functionalities.
  arXiv  Detail & Related papers  (2024-09-17T17:54:17Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• An Interactive Agent Foundation Model [49.77861810045509]
  We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
  arXiv  Detail & Related papers  (2024-02-08T18:58:02Z)
• Levels of AGI for Operationalizing Progress on the Path to AGI [64.59151650272477]
  We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors. This framework introduces levels of AGI performance, generality, and autonomy, providing a common language to compare models, assess risks, and measure progress along the path to AGI.
  arXiv  Detail & Related papers  (2023-11-04T17:44:58Z)
• Balancing Autonomy and Alignment: A Multi-Dimensional Taxonomy for Autonomous LLM-powered Multi-Agent Architectures [0.0]
  Large language models (LLMs) have revolutionized the field of artificial intelligence, endowing it with sophisticated language understanding and generation capabilities. This paper proposes a comprehensive multi-dimensional taxonomy to analyze how autonomous LLM-powered multi-agent systems balance the dynamic interplay between autonomy and alignment.
  arXiv  Detail & Related papers  (2023-10-05T16:37:29Z)
• CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection [0.07499722271664144]
  We propose a novel framework for automation of High Level Context (HLC) reasoning across intelligent systems at scale. The design of the framework supports the sharing and inter context among intelligent systems and the components for handling CSMs and the management of hierarchy, relationship, and transition. The implementation of the framework experiments on the HLC reasoning into vector and matrix computing and presents the potential to reach next level of automation.
  arXiv  Detail & Related papers  (2023-08-21T22:21:15Z)
• Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis [12.9222727028798]
  This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs) We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems.
  arXiv  Detail & Related papers  (2023-08-18T21:42:47Z)
• Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents [0.0]
  We present a novel framework for enhancing the capabilities of large language models (LLMs) by leveraging the power of multi-agent systems. Our framework introduces a collaborative environment where multiple intelligent agent components, each with distinctive attributes and roles, work together to handle complex tasks more efficiently and effectively.
  arXiv  Detail & Related papers  (2023-06-05T23:55:37Z)
• Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review [77.34726150561087]
  This review considers the existing methods for determining intrinsic motivation based on the world model obtained by the agent. The proposed unified framework describes the architecture of agents using a world model and intrinsic motivation to improve learning.
  arXiv  Detail & Related papers  (2023-01-24T15:13:02Z)
• On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective [54.38373782121503]
  A Foundation Decision Model (FDM) can be developed by formulating diverse decision-making tasks as sequence decoding tasks. We present a case study demonstrating our FDM implementation, DigitalBrain (DB1) with 1.3 billion parameters, achieving human-level performance in 870 tasks.
  arXiv  Detail & Related papers  (2022-12-24T06:16:45Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.