Bringing AI to Autonomous Systems — From Cognition to Collective Intelligence

将人工智能引入自主系统——从认知到群体智能

Abstract: 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.

摘要： 本文旨在强调自主系统作为人工智能发展终极阶段的核心作用，阐述了需要结合联结主义人工智能（Connectionist AI）与符号人工智能（Symbolic AI）的底层技术挑战，并探讨了人工智能与系统工程的融合。

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.

我们提出了一个用于自主系统设计与评估的综合框架，该框架基于一种通用的智能体架构。该架构将智能体的行为描述为围绕长期记忆（包含智能体不断演进的知识）组织的认知功能的组合。

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.

我们探讨了在实现该智能体架构基本特征时所面临的挑战，特别是感官数据与存储在记忆中的结构化数据之间的关联、与实现智能体目标及其规划相关的决策制定，以及为结合个体智能与群体智能而进行的智能体协作。

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.

我们解释了智能体的可信度（Trustworthiness）与传统系统不同，它不仅限于行为属性，还包含了一个与认知属性相关的重要维度，其有效性取决于智能体如何在决策中使用其知识。我们提出了开发评估智能体可信度方法的途径。

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.

最后，我们对自主多智能体系统的愿景与当前技术水平之间的巨大差距进行了批判性评估。