Understanding and Estimating Domain Complexity Across Domains

• URL: http://arxiv.org/abs/2312.13487v1
• Date: Wed, 20 Dec 2023 23:47:17 GMT
• Title: Understanding and Estimating Domain Complexity Across Domains
• Authors: Katarina Doctor, Mayank Kejriwal, Lawrence Holder, Eric Kildebeck, Emma Resmini, Christopher Pereyda, Robert J. Steininger, Daniel V. Oliven\c{c}a
• Abstract summary: We propose a general framework for estimating domain complexity across diverse environments. By analyzing dimensionality, sparsity, and diversity within these categories, we offer a comprehensive view of domain challenges.
• Score: 2.1613662656419406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) systems, trained in controlled environments, often struggle in real-world complexities. We propose a general framework for estimating domain complexity across diverse environments, like open-world learning and real-world applications. This framework distinguishes between intrinsic complexity (inherent to the domain) and extrinsic complexity (dependent on the AI agent). By analyzing dimensionality, sparsity, and diversity within these categories, we offer a comprehensive view of domain challenges. This approach enables quantitative predictions of AI difficulty during environment transitions, avoids bias in novel situations, and helps navigate the vast search spaces of open-world domains.

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