Paper Info
Title
Possibilistic Well-Founded Semantics
Abstract
Recently, a good set of logic programming semantics has been defined for capturing possibilistic logic program. Practically all of them follow a credulous reasoning approach. This means that given a possibilistic logic program one can infer a set of possibilistic models. However, sometimes it is desirable to associate just one possibilistic model to a given possibilistic logic program. One of the main implications of having just one model associated to a possibilistic logic program is that one can perform queries directly to a possibilistic program and answering these queries in accordance with this model. In this paper, we introduce an extension of the Well-Founded Semantics, which represents a sceptical reasoning approach, in order to capture possibilistic logic programs. We will show that our new semantics can be considered as an approximation of the possibilistic semantics based on the answer set semantics and the pstable semantic. A relevant feature of the introduced semantics is that it is polynomial time computable.
Year
DOI
Venue
2009
10.1007/978-3-642-05258-3_2
Mexican International Conference on Artificial Intelligence
Keywords
Field
DocType
possibilistic well-founded semantics,logic programming semantics,sceptical reasoning approach,possibilistic semantics,possibilistic model,credulous reasoning approach,good set,answer set semantics,possibilistic program,possibilistic logic program,new semantics,polynomial time
Data mining,Computer science,Theoretical computer science,Artificial intelligence,Stable model semantics,Logic programming,Possibilistic logic,Time complexity,Well-founded semantics,Semantics,Machine learning,Semantics of logic
Conference
Volume
ISSN
Citations 
5845
0302-9743
2
PageRank 
References 
Authors
0.36
8
2
Name
Order
Citations
PageRank
Mauricio Osorio143652.82
Juan Carlos Nieves222135.66