A Computational Learning Theoretic Approach to Birdsong Syntax Analysis

我们提出了一种有效的基于自动机的方法，从动物行为的连续序列数据中提取行为单元和规则。引入原始扩展，我们将两种基本方法- Ngram模型和Angluin的机器学习算法集成到一个行为学数据挖掘框架中。它允许我们获得行为规则的最简单的有限自动机表示，从动物行为的连续数据中接受（或生成）最小的可能行为模式集。利用这种方法，我们演示了如何使用真实的鸟鸣数据进行动物行为学数据挖掘，并使用计算机程序生成的人工鸟鸣数据对该方法进行了实验评估。这些结果表明，通过适当设置参数，我们的动物行为学数据挖掘即使对嘈杂的动物行为学数据也能有效地工作。此外，我们以孟加拉雀的歌声为例进行了案例研究，结果表明我们的方法成功地掌握了歌唱行为的核心结构，如循环和分支。

We propose an efficient automata-based approach to extract behavioral units and rules from continuous sequential data of animal behavior. Introducing original extensions, we integrate two elemental methods-the Ngram model and the Angluin's machine learning algorithm into an ethological data mining framework. It allows us to obtain the simplest finite automaton representation of behavioral rule that accepts (or generates) the smallest set of possible behavioral patterns from sequential data of animal behavior. With this method, we demonstrate how the ethological data mining works using real birdsong data and performs experimental evaluations of this method using artificial birdsong data generated by a computer program. These results suggest that our ethological data mining effectively works even for noisy ethological data by appropriately setting the parameters. In addition, we demonstrate a case study using the Bengalese finch song, showing that our method successfully grasps the core structure of the singing behavior such as loops and branchings.