面向集成持续学习、审议性行为与可理解模型的智能体 AI
Agential AI for Integrated Continual Learning, Deliberative Behavior, and Comprehensible Models
AI总结：
提出智能体 AI (AAI) 系统设计，通过具有完整性保证的持续学习方法对时间动态建模，并结合规划行为算法，克服了机器学习缺乏规划集成、不可理解和无法持续学习的局限。
摘要：
当代机器学习范式在统计分析方面表现卓越，解决了经典 AI 无法解决的问题。然而，它面临一些关键局限，如缺乏与规划集成、内部结构难以理解以及无法持续学习。我们提出了一种 AI 系统的初步设计，即 Agential AI (AAI)，它原则上可独立运行或基于统计方法运行，旨在克服这些问题。AAI 的核心是一种学习方法，通过组件级变异和选择来学习环境结构，以完整性、最小性和持续学习的保证对时间动态进行建模。它将此与一种行为算法集成，该算法在学习的模型上进行规划并封装高层行为模式。在简单环境上的初步实验展示了 AAI 的有效性和潜力。
英文摘要：
Contemporary machine learning paradigm excels in statistical data analysis, solving problems that classical AI couldn't. However, it faces key limitations, such as a lack of integration with planning, incomprehensible internal structure, and inability to learn continually. We present the initial design for an AI system, Agential AI (AAI), in principle operating independently or on top of statistical methods, designed to overcome these issues. AAI's core is a learning method that models temporal dynamics with guarantees of completeness, minimality, and continual learning, using component-level variation and selection to learn the structure of the environment. It integrates this with a behavior algorithm that plans on a learned model and encapsulates high-level behavior patterns. Preliminary experiments on a simple environment show AAI's effectiveness and potential.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
