Possibilistic Answer Set Programming Revisited
This work addresses the need for more intuitive semantics in PASP, which is important for applications requiring uncertainty handling, but it is incremental as it builds on existing ASP frameworks.
The paper tackled the problem of existing semantics for possibilistic answer set programming (PASP) being poorly motivated and unintuitive, by developing a new semantics based on a characterization of classical ASP in possibilistic logic, which can be implemented using standard ASP solvers.
Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be implemented using standard ASP solvers.