The paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to develop mixed networks stems from the desire to fully exploit the deterministic information (constraints) that is often present in graphical models. Several concepts and algorithms specific to belief networks and constraint networks are combined, achieving computational efficiency, semantic coherence and user-interface convenience. We define the semantics and graphical representation of mixed networks, and discuss the two main types of algorithms for processing them: inference-based and search-based. A preliminary experimental evaluation shows the benefits of the new model.

中文翻译：

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混合确定性和概率网络

该论文介绍了混合网络，这是一种新的图形模型框架，用于表达和推理概率和确定性信息。开发混合网络的动机源于充分利用图形模型中经常出现的确定性信息（约束）的愿望。结合了信念网络和约束网络特有的几个概念和算法，实现了计算效率、语义一致性和用户界面的便利性。我们定义了混合网络的语义和图形表示，并讨论了处理它们的两种主要算法类型：基于推理和基于搜索。初步实验评估显示了新模型的好处。