TY - GEN
T1 - Belief revision in structured probabilistic argumentation
AU - Shakarian, Paulo
AU - Simari, Gerardo I.
AU - Falappa, Marcelo A.
PY - 2014
Y1 - 2014
N2 - In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates - based on well-known ones developed for classical knowledge bases - that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates.
AB - In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates - based on well-known ones developed for classical knowledge bases - that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates.
UR - http://www.scopus.com/inward/record.url?scp=84898061138&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84898061138&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-04939-7_16
DO - 10.1007/978-3-319-04939-7_16
M3 - Conference contribution
AN - SCOPUS:84898061138
SN - 9783319049380
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 324
EP - 343
BT - Foundations of Information and Knowledge Systems - 8th International Symposium, FoIKS 2014, Proceedings
T2 - 8th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2014
Y2 - 3 March 2014 through 7 March 2014
ER -