Towards Lifted Inference Under Maximum Entropy for Probabilistic Relational FO-PCL Knowledge Bases

DOI10.1007/978-3-319-20807-7_46zbMath1465.68234OpenAlexW2399123559MaRDI QIDQ3451211

Christoph Beierle, Marc Finthammer, Josef Baudisch, Nico Potyka

Publication date: 10 November 2015

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-20807-7_46

Mathematics Subject Classification ID

Logic in artificial intelligence (68T27) Probability and inductive logic (03B48) Knowledge representation (68T30) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Cites Work

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• Probabilistic logic
• Achieving parametric uniformity for knowledge bases in a relational probabilistic conditional logic with maximum entropy semantics
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• First-order probabilistic conditional logic and maximum entropy
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy
• Reasoning about knowledge and probability
• The Uncertain Reasoner's Companion
• Conditionals in nonmonotonic reasoning and belief revision. Considering conditionals as agents

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