计算机科学
软件部署
分类
背景(考古学)
领域(数学)
创造力
人工智能
知识表示与推理
管理科学
代表(政治)
限制
知识管理
数据科学
软件工程
工程类
机械工程
古生物学
数学
政治
政治学
纯数学
法学
生物
作者
Evana Gizzi,Lakshmi S. Nair,Sonia Chernova,Jivko Sinapov
摘要
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.
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