论文标题
解释作为概率逻辑编程中的程序
Explanations as Programs in Probabilistic Logic Programming
论文作者
论文摘要
可理解的解释的产生是现代人工智能系统的重要特征。在这项工作中，我们考虑了概率逻辑编程，这是逻辑编程的扩展，这对于具有关系结构和不确定性的域模型很有用。本质上，一个程序指定了可能的世界（即事实集）的概率分布。解释的概念通常与世界的概念相关联，因此人们经常寻找最可能的世界以及查询是真实的世界。不幸的是，这种解释没有因果结构。特别是，未显示特定预测所需的推论链（以查询为代表）。在本文中，我们提出了一种新颖的方法，其中解释被表示为通过许多不展开的转换从给定查询产生的程序。在这里，证明给定查询的推论链被明确。此外，生成的解释是最小的（即不包含无关的信息），并且可以被参数化W.R.T.可见谓词的规范，以便用户可以从说明中隐藏无趣的细节。
The generation of comprehensible explanations is an essential feature of modern artificial intelligence systems. In this work, we consider probabilistic logic programming, an extension of logic programming which can be useful to model domains with relational structure and uncertainty. Essentially, a program specifies a probability distribution over possible worlds (i.e., sets of facts). The notion of explanation is typically associated with that of a world, so that one often looks for the most probable world as well as for the worlds where the query is true. Unfortunately, such explanations exhibit no causal structure. In particular, the chain of inferences required for a specific prediction (represented by a query) is not shown. In this paper, we propose a novel approach where explanations are represented as programs that are generated from a given query by a number of unfolding-like transformations. Here, the chain of inferences that proves a given query is made explicit. Furthermore, the generated explanations are minimal (i.e., contain no irrelevant information) and can be parameterized w.r.t. a specification of visible predicates, so that the user may hide uninteresting details from explanations.
