发表机构
Verimag Laboratory（Verimag实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于通用智能体架构的自主系统设计评估框架，强调联结主义与符号主义AI结合，并探讨可信度评估及多智能体协调，指出愿景与现实间存在显著差距。
AI 中文摘要
本文旨在强调自主系统作为AI发展终极阶段的核心地位，阐明其底层技术挑战需要联结主义AI与符号主义AI的结合，并将AI与系统工程相融合。我们基于一个通用智能体架构，提出了自主系统设计与评估的综合框架，该架构将智能体行为表征为围绕长期记忆组织的认知功能的组合，长期记忆包含智能体不断演进的知识。我们解决了实现智能体架构基本特征所面临的挑战，特别是感官数据与存储在记忆中的结构化数据之间的连接、与实现智能体目标及其规划相关的决策制定，以及智能体间的协调以结合个体智能与集体智能。我们解释了智能体的可信度，不同于传统系统，不仅限于行为属性，它还包括与认知属性相关的基本维度，其有效性取决于智能体在决策中如何使用其知识。我们提出了开发智能体可信度评估方法的途径。最后，我们对自主多智能体系统的愿景目标与当前技术水平之间的巨大差距进行了批判性评估。
英文摘要
The purpose of this article is to highlight the central role of autonomous systems as the ultimate stage in the development of AI, to explain the underlying technical challenges that require a combination of connectionist AI and symbolic AI, and to integrate AI and systems engineering. We present a comprehensive framework for the design and evaluation of autonomous systems, based on a generic agent architecture that characterizes their behavior as the composition of cognitive functions organized around a long-term memory containing the agent's evolving knowledge. We address the challenges posed by the implementation of the fundamental features of the agent architecture, in particular the link between sensory data and structured data stored in memory, decision-making related to the achievement of the agent's goals and their planning, as well as the coordination of agents to combine individual and collective intelligence. We explain that agent trustworthiness, unlike that of traditional systems, is not limited to behavioral properties. It includes an essential dimension related to cognitive properties, the validity of which depends on how the agent uses its knowledge in decision-making. We present avenues for the development of methods for evaluating agent trustworthiness. We conclude with a critical assessment of the substantial gap between the aspirational vision of autonomous multi-agent systems and the current state of the art.
