Abstract
Factor graphs are a very general knowledge representation, subsuming many existing formalisms in AI. Sum-product networks are a more recent representation, inspired by studying cases where factor graphs are tractable. Factor graphs emphasize expressive power, while sum-product networks restrict expressiveness to get strong guarantees on speed of inference. A sum-product network is not simply a restricted factor graph, however. Although the inference algorithms for the two structures are very similar, translating a sum-product network into factor graph representation can result in an exponential slowdown. We propose a formalism which generalizes factor graphs and sum-product networks, such that inference is fast in cases whose structure is close to a sum-product network.
This work was sponsored by the U.S. Army. Statements and opinions expressed may not reflect the position or policy of the United States Government, and no official endorsement should be inferred. Special thanks to Paul Rosenbloom and Łukasz Stafiniak for providing comments on a draft of this paper.
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Demski, A. (2015). Expression Graphs. In: Bieger, J., Goertzel, B., Potapov, A. (eds) Artificial General Intelligence. AGI 2015. Lecture Notes in Computer Science(), vol 9205. Springer, Cham. https://doi.org/10.1007/978-3-319-21365-1_25
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DOI: https://doi.org/10.1007/978-3-319-21365-1_25
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