Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework

TL;DR

This survey reviews the field of Creative Problem Solving in AI, proposing a framework to categorize existing methods and highlighting the importance of creativity for autonomous systems to handle environmental uncertainty.

Contribution

It introduces a comprehensive framework for CPS in AI, categorizes existing methods, and outlines open research questions and future directions.

Findings

Proposes a four-component CPS framework.

Categorizes existing AI methods within the framework.

Identifies open challenges and future research directions.

Abstract

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.