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A Framework for Ontological Behavioral Modeling in Domestic Dogs to Predict Aggression

机译：国内狗在内部行为建模框架预测侵略的框架

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There has been a wealth of study in the field of animal behavior; in the case of domestic animals, behavior is often studied in a shelter or laboratory environment [which is likely not predictive of domesticated dogs in the home]. Currently there are no standard methods with scientific merit to determine optimal, reliable, and consistent assessment techniques for domestic dogs and the behaviors that occur as a result of their situational contexts. Accurate behavior analysis is necessary both to reliably predict aggression and to study the effect of human interaction on innate behaviors. In this paper, we propose a contextual ontological framework by which to classify aggressive behaviors in domestic dogs by consulting with a panel of training experts. Further, we propose a statistical methodology for measuring aggression during conspecific [same species] socialization, and lastly we demonstrate this method on a small set of sample cases. Ultimately, we hope to apply this framework to a much larger set of cases to further develop the field and provide groundwork for making scientific the art of animal training.
机译：动物行为领域有丰富的研究;在国内动物的情况下，通常在庇护或实验室环境中进行行为[可能无法预测家庭中的驯养狗]。目前，没有科学优点没有标准方法，以确定国内犬的最佳，可靠和一致的评估技术以及由于其情境范围而发生的行为。准确的行为分析对于可靠预测侵略性，并研究人类互动对先天行为的影响。在本文中，我们提出了一种语境本体论框架，通过与培训专家小组进行咨询来分类家养中的攻击性行为。此外，我们提出了一种统计方法，用于在统一[相同物种]社会化期间测量侵略，最后我们在一小组样本案例上展示了这种方法。最终，我们希望将此框架应用于更大的案例，以进一步发展该领域，并为制作科学艺术的动物训练提供基础。

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