从稳定模型中学习可能性逻辑程序，解决未知规则提取问题。
Inductive Learning for Possibilistic Logic Programs Under Stable Models
- 定义归纳任务并提出两种求解算法。
- 在随机生成数据集上性能优于现有系统。
- 适合逻辑编程与归纳学习交叉研究者。
基于稳定模型的可能性逻辑程序（poss-programs）是答案集编程（ASP）的重要变体。尽管其语义（可能性稳定模型）和性质已得到充分研究，但归纳推理问题尚未被探索。本文提出一种从背景程序和示例（目标可能性稳定模型的片段）中提取poss-programs的方法。首先形式化定义了归纳任务，并研究其性质，随后提出两种计算归纳解的算法ilpsm和ilpsmmin。实现了ilpsmmin的原型，并在随机生成的数据集上进行实验，结果表明当输入为普通逻辑程序时，该原型在性能上超过一个主流的基于稳定模型的正则逻辑程序归纳学习系统。
原文摘要 · Abstract (English)
Possibilistic logic programs (poss-programs) under stable models are a major variant of answer set programming (ASP). While its semantics (possibilistic stable models) and properties have been well investigated, the problem of inductive reasoning has not been investigated yet. This paper presents an approach to extracting poss-programs from a background program and examples (parts of intended possibilistic stable models). To this end, the notion of induction tasks is first formally defined, its properties are investigated and two algorithms ilpsm and ilpsmmin for computing induction solutions are presented. An implementation of ilpsmmin is also provided and experimental results show that when inputs are ordinary logic programs, the prototype outperforms a major inductive learning system for normal logic programs from stable models on the datasets that are randomly generated.
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