Domain Complexity

Domain complexity, in the context of artificial intelligence, focuses on quantifying the challenges posed by different environments to AI systems, aiming to predict AI performance across diverse settings. Current research emphasizes distinguishing between intrinsic complexity (inherent to the environment) and extrinsic complexity (dependent on the AI agent and its task), analyzing factors like dimensionality, sparsity, and data diversity to create domain-independent complexity measures. This work is crucial for improving the robustness and generalizability of AI systems, enabling more accurate predictions of AI performance in real-world applications and facilitating more effective transfer learning across domains.