A Distribution Semantics for Probabilistic Term Rewriting
Germ\'an Vidal

TL;DR
This paper introduces a new distribution semantics for probabilistic term rewriting systems, enabling the modeling and efficient computation of reduction probabilities, with potential for enhanced expressive power.
Contribution
It proposes a novel distribution semantics for probabilistic rewriting systems and methods to compute reduction probabilities efficiently.
Findings
Defined a distribution semantics for probabilistic term rewriting
Developed a method to compute explanations for reductions
Illustrated the approach with multiple examples
Abstract
Probabilistic programming is becoming increasingly popular thanks to its ability to specify problems with a certain degree of uncertainty. In this work, we focus on term rewriting, a well-known computational formalism. In particular, we consider systems that combine traditional rewriting rules with probabilities. Then, we define a novel "distribution semantics" for such systems that can be used to model the probability of reducing a term to some value. We also show how to compute a set of "explanations" for a given reduction, which can be used to compute its probability in a more efficient way. Finally, we illustrate our approach with several examples and outline a couple of extensions that may prove useful to improve the expressive power of probabilistic rewrite systems.
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Taxonomy
TopicsNatural Language Processing Techniques · Semantic Web and Ontologies · Topic Modeling
MethodsSparse Evolutionary Training · Focus
