  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.

Artificial Intelligence