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Artificial Intelligence Tools for Cyber Attribution pp 47–74Cite as

Belief Revision in DeLP3E

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Abstract

Any artificial intelligence tool designed for cyber-attribution must deal with information coming from different sources that invariably leads to incompleteness, overspecification, or inherently uncertain content. The presence of these varying levels of uncertainty doesn’t mean that the information is worthless—rather, these are hurdles that the knowledge engineer must learn to work with. In this chapter, we continue developing the DeLP3E model introduced in the previous chapter, focusing now on the problem of belief revision in DeLP3E. We first propose a non-prioritized class of revision operators called AFO (Annotation Function-based Operators); then, we go on to argue that in some cases it may be desirable to define revision operators that take quantitative aspects into account (such as how the probabilities of certain literals or formulas of interest change after the revision takes place). As a result, we propose the QAFO (Quantitative Annotation Function-based Operators) class of operators, a subclass of AFO, and study the complexity of several problems related to their specification and application in revising knowledge bases. Finally, we present an algorithm for computing the probability that a literal is warranted in a DeLP3E knowledge base, and discuss how it could be applied towards implementing QAFO-style operators that compute approximations rather than exact operations.

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Notes

  1. 1.

    That is, af ′(x) = af(x) for all x ∈dom(af), and dom(af ′) = dom(af) ∪{f}.

  2. 2.

    Note that this definition can easily be extended to deal with probability intervals as well (i.e., using both nec and poss); here, for simplicity of presentation, we adopt this definition in order to work with point probabilities.

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Authors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    Eric Nunes & Paulo Shakarian

  2. Department of Computer Science and Engineering, Universidad Nacional del Sur (UNS) & Institute for Computer Science and Engineering (UNS-CONICET), Bahia Blanca, Argentina

    Gerardo I. Simari

  3. University of Maryland, College Park, MD, USA

    Andrew Ruef

Authors
  1. Eric Nunes
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  2. Paulo Shakarian
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  3. Gerardo I. Simari
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  4. Andrew Ruef
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Nunes, E., Shakarian, P., Simari, G.I., Ruef, A. (2018). Belief Revision in DeLP3E . In: Artificial Intelligence Tools for Cyber Attribution. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-73788-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-73788-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73787-4

  • Online ISBN: 978-3-319-73788-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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