Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework
This addresses the problem of limited adaptability in autonomous systems for AI researchers, but it is incremental as it surveys and organizes existing work rather than introducing new methods.
The paper tackles the challenge of enabling AI agents to solve novel or anomalous problems in unpredictable environments by presenting a definition and framework for Creative Problem Solving (CPS), categorizing existing methods to stimulate further research.
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.