论文标题
在不确定性下用于机器推理的信用评估网络
Credal Valuation Networks for Machine Reasoning Under Uncertainty
论文作者
论文摘要
当代企业在以不确定性，敌意和纯粹的数据量为特征的情况下广泛应用机器推理和人工智能提供了无限的机会。该论文开发了一个评估网络，作为在支持人类运营商的不确定性下进行高级融合和推理的图形系统。估值是（不确定）知识和收集数据的数学表示形式，被表示为信用集，定义为不精确概率理论框架中的连贯的间隔概率。具有这种信用集，组合和边缘化的基本操作被定义为满足评估代数的公理。讨论了信用估值网络的实际实施，并在小型示例中证明了其效用。
Contemporary undertakings provide limitless opportunities for widespread application of machine reasoning and artificial intelligence in situations characterised by uncertainty, hostility and sheer volume of data. The paper develops a valuation network as a graphical system for higher-level fusion and reasoning under uncertainty in support of the human operators. Valuations, which are mathematical representation of (uncertain) knowledge and collected data, are expressed as credal sets, defined as coherent interval probabilities in the framework of imprecise probability theory. The basic operations with such credal sets, combination and marginalisation, are defined to satisfy the axioms of a valuation algebra. A practical implementation of the credal valuation network is discussed and its utility demonstrated on a small scale example.
