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.
翻译:尽管在规划和学习、解决新问题或使现有知识适应新环境方面有许多进展,特别是在环境在部署后可能以不可预测的方式发生变化的情况下,但创新解决问题仍然是智能系统安全和有用整合的一个限制因素。随着日益自主的系统出现,AI代理机构有必要通过创造性处理环境不确定性。为了刺激CPS的进一步研究,我们提出了一个CPS的定义和框架,用于对该领域现有的AI方法进行分类。我们的框架由CPS问题的四个主要部分组成,即:(1) 问题形成,(2) 知识代表,(3) 知识操纵方法,(4) 评价方法。我们用公开的研究问题来结束我们的调查,并提出未来的方向。