Understanding and Estimating Domain Complexity Across Domains
This work addresses the challenge of AI adaptability in open-world and real-world applications, though it appears incremental as it builds on existing concepts of domain analysis without introducing a new paradigm.
The authors tackled the problem of AI systems struggling in real-world complexities by proposing a general framework to estimate domain complexity across diverse environments, distinguishing between intrinsic and extrinsic factors to enable quantitative predictions of AI difficulty during transitions.
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.