A number of generalizations of answer set programming have been proposed in the literature to deal with vagueness, uncertainty, and partial rule satisfaction. We introduce a unifying framework that entails most of the existing approaches to fuzzy answer set programming. In this framework, rule bodies are defined using arbitrary fuzzy connectives with monotone partial mappings. As an approximation of full answer sets, k-answer sets axe introduced to deal with conflicting information, yielding a flexible framework that encompasses, among others, existing work on valued constraint satisfaction and answer set optimization.
['Jeroen Janssen', 'Steven Schockaert', 'Dirk Vermeir', 'Martine De Cock']
General Fuzzy Answer Set Programs