论文标题
部分可观测时空混沌系统的无模型预测
Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework
论文作者
论文摘要
创造性问题解决（CPS）是人工智能（AI）中的一个子区域，重点是解决自主系统中新月式或异常问题的方法。尽管在计划和学习方面取得了许多进步，解决新的问题或将现有知识调整到新的环境中，尤其是在部署后环境可能会发生不可预测的方式变化的情况下，仍然是智能系统安全有用的整合的限制因素。越来越多的自主系统的出现决定了AI代理通过创造力来应对环境不确定性的必要性。为了刺激CPS的进一步研究，我们提出了CP的定义和框架，我们采用了该领域的现有AI方法。我们的框架由CPS问题的四个主要组成部分组成，即1）问题制定，2）知识表示，3）知识操纵方法和4）评估方法。我们以开放的研究问题为您的调查结束，并建议未来的指示。
Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways post deployment, remains a limiting factor in the safe and useful integration of intelligent systems. The emergence of increasingly autonomous systems dictates the necessity for AI agents to deal with environmental uncertainty through creativity. To stimulate further research in CPS, we present a definition and a framework of CPS, which we adopt to categorize existing AI methods in this field. Our framework consists of four main components of a CPS problem, namely, 1) problem formulation, 2) knowledge representation, 3) method of knowledge manipulation, and 4) method of evaluation. We conclude our survey with open research questions, and suggested directions for the future.
