提出人工智能认知系统的关键挑战，明确进步标准。
Challenges for artificial cognitive systems
- 以从经验中学习并灵活运用知识为核心定义认知系统。
- 提出可衡量进展的挑战框架，而非未来预测。
- 适合研究通用人工智能与认知建模的学者参考。
本文旨在填补认知系统研究中的空白：‘认知系统研究需要定义进展的挑战。这些挑战并非（更多）对未来的技术预测，而是指引目标与进步标准的指南’——此引述出自欧洲认知系统网络（EUCogII）项目描述（http://www.eucognition.org）。因此，我们明确提出人工智能认知系统面临的核心挑战。这些挑战基于对认知系统的定义：能够从经验中学习，并以灵活方式运用其获得的知识（包括陈述性与程序性知识），以实现自身目标。
原文摘要 · Abstract (English)
The declared goal of this paper is to fill this gap: "... cognitive systems research needs questions or challenges that define progress. The challenges are not (yet more) predictions of the future, but a guideline to what are the aims and what would constitute progress." -- the quotation being from the project description of EUCogII, the project for the European Network for Cognitive Systems within which this formulation of the 'challenges' was originally developed (http://www.eucognition.org). So, we stick out our neck and formulate the challenges for artificial cognitive systems. These challenges are articulated in terms of a definition of what a cognitive system is: a system that learns from experience and uses its acquired knowledge (both declarative and practical) in a flexible manner to achieve its own goals.
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