Measuring the Complexity of Domains Used to Evaluate AI Systems
This work addresses a foundational issue for AI researchers by providing a tool to assess domain complexity, which could aid in evaluating and comparing AI systems more effectively, though it appears incremental as it builds on existing benchmarking practices.
The paper tackles the problem of lacking an objective measure to compare the complexity of different domains used to evaluate AI systems, proposing a theory for measuring domain complexity and demonstrating its effectiveness through neural network approximations and experimental results.
There is currently a rapid increase in the number of challenge problem, benchmarking datasets and algorithmic optimization tests for evaluating AI systems. However, there does not currently exist an objective measure to determine the complexity between these newly created domains. This lack of cross-domain examination creates an obstacle to effectively research more general AI systems. We propose a theory for measuring the complexity between varied domains. This theory is then evaluated using approximations by a population of neural network based AI systems. The approximations are compared to other well known standards and show it meets intuitions of complexity. An application of this measure is then demonstrated to show its effectiveness as a tool in varied situations. The experimental results show this measure has promise as an effective tool for aiding in the evaluation of AI systems. We propose the future use of such a complexity metric for use in computing an AI system's intelligence.