Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
复制标题
自适应马尔可夫逻辑网络：学习具有动态参数的统计关系模型
DOI：
10.3233/978-1-60750-606-5-937
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
M. Beetz
中科院分区：
文献类型：
--
作者：
Dominik Jain;A. Barthels;M. Beetz
统计关系模型，如马尔可夫逻辑网络，试图通过表示属于特定类的对象的一般原则来简洁地描述关系域的属性。模型的目的是独立于可以应用这些原则的对象集，并且假设这些原则可以很好地推广到任意对象集。本文指出了试图用一组固定参数来表示相应原理的模型的局限性，并讨论了固定参数的健全性确实值得怀疑的条件。我们提出了一种新的表示形式，称为自适应马尔可夫逻辑网络，以允许更灵活的关系域表示，其中包括动态调整参数以适应实例化的属性，方法是将模型的参数作为手边实例化属性的函数。我们在一个简单但动机良好的示例域上实证地证明了我们的学习和表示系统的价值。
Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects belonging to particular classes. Models are intended to be independent of the set of objects to which these principles can be applied, and it is assumed that the principles will soundly generalize across arbitrary sets of objects. In this paper, we point out limitations of models that seek to represent the corresponding principles with a fixed set of parameters and discuss the conditions under which the soundness of fixed parameters is indeed questionable. We propose a novel representation formalism called adaptive Markov logic networks to allow more flexible representations of relational domains, which involve parameters that are dynamically adjusted to fit the properties of an instantiation by phrasing the model's parameters as functions over attributes of the instantiation at hand. We empirically demonstrate the value of our learning and representation system on a simple but well-motivated example domain.