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

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Evana Gizzi , Lakshmi S. Nair , Sonia Chernova , Jivko Sinapov ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2204.10358

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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Computer Science

Creativity

Artificial Intelligence

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Software deployment Computer science Categorization Context (archaeology) Field (mathematics) Creativity Knowledge representation and reasoning Artificial intelligence Management science Representation (politics) Knowledge management Data science Software engineering Engineering Psychology Mathematics Political science Social psychology Pure mathematics Law Politics Biology Paleontology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2204.10358
PDF: https://arxiv.org/pdf/2204.10358
OA Status: green
Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2204.10358

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

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-04-21

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Authors: Evana Gizzi, Lakshmi S. Nair, Sonia Chernova, Jivko Sinapov

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Landing page: https://arxiv.org/abs/2204.10358

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Concepts: Software deployment, Computer science, Categorization, Context (archaeology), Field (mathematics), Creativity, Knowledge representation and reasoning, Artificial intelligence, Management science, Representation (politics), Knowledge management, Data science, Software engineering, Engineering, Psychology, Mathematics, Political science, Social psychology, Pure mathematics, Law, Politics, Biology, Paleontology

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Cited by: 1

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abstract_inverted_index.increasingly	71
abstract_inverted_index.off-nominal,	17
abstract_inverted_index.environmental	83
abstract_inverted_index.manipulation,	135
abstract_inverted_index.unpredictable	51
abstract_inverted_index.representation,	130
cited_by_percentile_year.max	95
cited_by_percentile_year.min	91
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile.value	0.50545011
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False