mlsanityData to 7 Oct 2026

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

arXiv 2204.10358 ↗First posted 21 Apr 2022cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 27 of its 119 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

27 of 119 references.

References · 27

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,818
  3. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  4. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 308
  5. 2020Neurosymbolic AI: The 3rd Wavecited by 253
  6. 2017Neural Style Transfer: A Reviewcited by 199
  7. 2019Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutionscited by 182
  8. 2017Neural Task Programming: Learning to Generalize Across Hierarchical Taskscited by 75
  9. 2019Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularitycited by 68
  10. 2019KETO: Learning Keypoint Representations for Tool Manipulationcited by 68
  11. 2019Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Researchcited by 58
  12. 2021Learning Symbolic Operators for Task and Motion Planningcited by 57
15 more
  1. 2018Reinforcement Learning for Improving Agent Designcited by 55
  2. 2018Jointly Learning to Construct and Control Agents using Deep Reinforcement Learningcited by 51
  3. 2019Structured agents for physical constructioncited by 50
  4. 2019Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresightcited by 34
  5. 2020Generalization to New Actions in Reinforcement Learningcited by 25
  6. 2020SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learningcited by 23
  7. 2019Tool Macgyvering: Tool Construction Using Geometric Reasoningcited by 7
  8. 2018Adapting Everyday Manipulation Skills to Varied Scenarioscited by 6
  9. 2020STRIPS Action Discoverycited by 5
  10. 2020Tool Macgyvering: A Novel Framework for Combining Tool Substitution and Constructioncited by 4
  11. 2020Feature Guided Search for Creative Problem Solving Through Tool Constructioncited by 4
  12. 2017Skill Learning by Autonomous Robotic Playing using Active Learning and Creativitycited by 3
  13. 2018Creative Invention Benchmarkcited by 3
  14. 2019Tool Substitution with Shape and Material Reasoning Using Dual Neural Networkscited by 2
  15. 2017Perception-Informed Autonomous Environment Augmentation With Modular Robotscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2025A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomycited by 9
  2. 2024Creative Problem Solving in Large Language and Vision Models -- What Would it Take?cited by 6
  3. 2024Mentigo: An Intelligent Agent for Mentoring Students in the Creative Problem Solving Processcited by 3
  4. 2026Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learningcited by 1