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

• URL: http://arxiv.org/abs/2204.10358v1
• Date: Thu, 21 Apr 2022 18:31:44 GMT
• Title: Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework
• Authors: Evana Gizzi, Lakshmi Nair, Sonia Chernova, Jivko Sinapov
• Abstract summary: Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) 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, problem formulation, knowledge representation, method of knowledge manipulation, and method of evaluation.
• Score: 20.51422185398759
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
  Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making. With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems. We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
  arXiv  Detail & Related papers  (2025-04-12T01:27:49Z)
• Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems [132.77459963706437]
  This book provides a comprehensive overview, framing intelligent agents within modular, brain-inspired architectures.<n>It explores self-enhancement and adaptive evolution mechanisms, exploring how agents autonomously refine their capabilities.<n>It also examines the collective intelligence emerging from agent interactions, cooperation, and societal structures.
  arXiv  Detail & Related papers  (2025-03-31T18:00:29Z)
• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks. In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence [19.369216778200034]
  Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems. This review explores the evolution of uncertainty quantification techniques in AI. We examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness.
  arXiv  Detail & Related papers  (2025-01-05T23:14:47Z)
• Metacognition for Unknown Situations and Environments (MUSE) [3.2020845462590697]
  We propose the Metacognition for Unknown Situations and Environments (MUSE) framework. MUSE integrates metacognitive processes--specifically self-awareness and self-regulation--into autonomous agents. Agents show significant improvements in self-awareness and self-regulation.
  arXiv  Detail & Related papers  (2024-11-20T18:41:03Z)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems [2.226040060318401]
  We translate System Theoretic Process Analysis (STPA) for analyzing AI operation and development processes. We focus on systems that rely on machine learning algorithms and conductedA on three case studies. We find that key concepts and steps of conducting anA readily apply, albeit with a few adaptations tailored for AI systems.
  arXiv  Detail & Related papers  (2024-10-29T20:43:18Z)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• MacGyver: Are Large Language Models Creative Problem Solvers? [87.70522322728581]
  We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting. We create MACGYVER, an automatically generated dataset consisting of over 1,600 real-world problems. We present our collection to both LLMs and humans to compare and contrast their problem-solving abilities.
  arXiv  Detail & Related papers  (2023-11-16T08:52:27Z)
• Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems [67.01132165581667]
  We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components. We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
  arXiv  Detail & Related papers  (2023-11-13T21:20:17Z)
• Proceedings of the Robust Artificial Intelligence System Assurance (RAISA) Workshop 2022 [0.0]
  The RAISA workshop will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level.
  arXiv  Detail & Related papers  (2022-02-10T01:15:50Z)
• Problems in AI research and how the SP System may help to solve them [0.0]
  This paper describes problems in AI research and how the SP System may help to solve them. Most of the problems are described by leading researchers in AI in interviews with science writer Martin Ford.
  arXiv  Detail & Related papers  (2020-09-02T11:33:07Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.