论文标题
测量用于评估AI系统的域的复杂性
Measuring the Complexity of Domains Used to Evaluate AI Systems
论文作者
论文摘要
目前，挑战问题的数量迅速增加，基准测试数据集和用于评估AI系统的算法优化测试。但是，目前尚未存在一个客观的度量来确定这些新创建的域之间的复杂性。缺乏跨域检查为有效研究更通用的AI系统带来了障碍。我们提出了一种测量各个领域之间复杂性的理论。然后，使用基于神经网络的AI系统的群体对该理论进行评估。将近似值与其他众所周知的标准进行比较，并表明它符合复杂性的直觉。然后，证明了此措施的应用以显示其作为不同情况下的工具的有效性。实验结果表明，该措施有望作为帮助评估AI系统的有效工具。我们建议将这种复杂度度量的未来使用用于计算AI系统的智能。
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.
