Abstract
Given a knowledge base in Conjunctive Normal Form for use by an intelligent agent, with probabilities assigned to the conjuncts, the probability of any new query sentence can be determined by solving the Probabilistic Satisfiability Problem (PSAT). This involves finding a consistent probability distribution over the atoms (if they are independent) or complete conjunction set of the atoms. We show how this problem can be expressed and solved as a set of nonlinear equations derived from the knowledge base sentences and standard probability of logical sentences. Evidence is given that numerical gradient descent algorithms can be used more effectively then other current methods to find PSAT solutions.
This research supported in part by Dynamic Data Driven Application Systems AFOSR grant FA9550-17-1-0077.
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Henderson, T.C., Simmons, R., Serbinowski, B., Fan, X., Mitiche, A., Cline, M. (2019). Probabilistic Logic for Intelligent Systems. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_11
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DOI: https://doi.org/10.1007/978-3-030-01370-7_11
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