用于不确定性机器推理的信度赋值网络
Credal Valuation Networks for Machine Reasoning Under Uncertainty
摘要：
当代事业为机器推理和人工智能在以不确定性、敌意和大量数据为特征的情况下的广泛应用提供了无限的机会。本文开发了一个评估网络作为一个图形系统，用于在不确定性下进行更高层次的融合和推理，以支持人类操作员。估值是（不确定的）知识和收集的数据的数学表示，表示为信用集，定义为不精确概率理论框架中的相干区间概率。基本操作与这样的credal集，组合和边缘化，定义为满足公理的赋值代数。一个实际的实施方案的信用评估网络进行了讨论，并证明其效用的一个小规模的例子。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
