Probabilistic Logic Programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables. Here, we introduce a new class of probabilistic logic programs, namely Probabilistic Optimizable Logic Programs, and we provide an effective algorithm to find the best assignment to probabilities of random variables, such that a set of constraints is satisfied and an objective function is optimized. This paper is under consideration for acceptance in Theory and Practice of Logic Programming.
翻译:概率逻辑编程对于具有不确定性的编码问题来说是一种有效的正式形式,其中一些问题可能需要在随机变量概率分布之间受限制的情况下优化概率值。在这里,我们引入了一种新的概率逻辑程序,即概率优化逻辑程序,我们提供了一种有效的算法,以找到随机变量概率的最佳分配,从而满足一系列制约,优化客观功能。本文正在考虑在逻辑编程理论和实践中得到接受。