Abstract
We begin with a brief overview of Probabilistic Logic Networks, distinguish PLN from other approaches to reasoning under uncertainty, and describe some of the main conceptual foundations and goals of PLN. We summarize how knowledge is represented within PLN and describe the four basic truth-value types. We describe a few basic first-order inference rules and formulas, outline PLN’s approach to handling higher-order inference via reduction to first-order rules, and follow this by a brief summary of PLN’s handling of quantifiers.
Since PLN was and continues to be developed as one of several major components of a broader and more general artificial intelligence project, we next describe the OpenCog project and PLN’s roles within the project.
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Iklé, M. (2011). Probabilistic Logic Networks in a Nutshell. In: Benferhat, S., Grant, J. (eds) Scalable Uncertainty Management. SUM 2011. Lecture Notes in Computer Science(), vol 6929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23963-2_5
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DOI: https://doi.org/10.1007/978-3-642-23963-2_5
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