TY - JOUR
T1 - Belief revision in structured probabilistic argumentation
T2 - Model and application to cyber security
AU - Shakarian, Paulo
AU - Simari, Gerardo I.
AU - Moores, Geoffrey
AU - Paulo, Damon
AU - Parsons, Simon
AU - Falappa, Marcelo A.
AU - Aleali, Ashkan
N1 - Publisher Copyright:
© 2015, Springer International Publishing Switzerland.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them.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 these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.
AB - In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them.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 these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.
KW - Argumentation
KW - Belief revision
KW - Cyber security
KW - Probabilistic reasoning
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U2 - 10.1007/s10472-015-9483-5
DO - 10.1007/s10472-015-9483-5
M3 - Article
AN - SCOPUS:84944615359
SN - 1012-2443
VL - 78
SP - 259
EP - 301
JO - Annals of Mathematics and Artificial Intelligence
JF - Annals of Mathematics and Artificial Intelligence
IS - 3-4
ER -