Inductive Learning for Possibilistic Logic Programs Under Stable Models
This work addresses a gap in inductive learning for a variant of answer set programming, but it is incremental as it builds on well-investigated semantics.
The paper tackles the problem of inductive reasoning for possibilistic logic programs under stable models, presenting algorithms to extract programs from examples, and shows that a prototype outperforms a major existing system on randomly generated datasets for ordinary logic programs.
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.