论文标题
估计算法复杂性的方法的综述：选项，挑战和新方向
A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions
论文作者
论文摘要
一些在算法（Kolmogorov）的应用领域中建立了新的技术，目前是第一次共存的，并在这里进行了审查，从统计上的无损压迫等主要的方法到较新的方法到较新的方法，这些方法的发展，补充，构成了新的挑战，并构成了新的挑战，并可能表现出自己的局限性。表明这些不同方法相互补充的证据表明了不同的制度，尽管他们面临许多挑战，但其中一些方法可以更好地以算法信息理论的原则为动机和更好地扎根。这将解释如何在追求数值适用性时探索不同必要和充分条件的不同方法可以探索放松不同的条件，其中一些方法比其他方法更有可能更大的风险，以换取更大的相关性。最后，我们讨论了可能或应该考虑的可能方向发展领域的可能方向，并鼓励方法论创新，但更重要的是，为科学发现做出了贡献。本文还反驳了另一位作者在先前发布的Minireview中提出的索赔，并提供了替代帐户。
Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and may exhibit their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented and despite their many challenges, some of these methods can be better motivated by and better grounded in the principles of algorithmic information theory. It will be explained how different approaches to algorithmic complexity can explore the relaxation of different necessary and sufficient conditions in their pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance. We conclude with a discussion of possible directions that may or should be taken into consideration to advance the field and encourage methodological innovation, but more importantly, to contribute to scientific discovery. This paper also serves as a rebuttal of claims made in a previously published minireview by another author, and offers an alternative account.
