8 citations · 19 across the 8 of their papers we have counts for
8 papers
d-DNNF Modulo Theories: A General Framework for Polytime SMT Queries
Gabriele Masina, Emanuele Civini, Massimo Michelutti +2
In Knowledge Compilation (KC) a propositional knowledge base is compiled off-line into some target form, typically into deterministic decomposable negation normal form (d-DNNF) or…
The Theory and Practice of MAP Inference over Non-Convex Constraints
Leander Kurscheidt, Gabriele Masina, Roberto Sebastiani +1
In many safety-critical settings, probabilistic ML systems have to make predictions subject to algebraic constraints, e.g., predicting the most likely trajectory that does not cros…
Beyond Eager Encodings: A Theory-Agnostic Approach to Theory-Lemma Enumeration in SMT
Emanuele Civini, Gabriele Masina, Giuseppe Spallitta +1
Lifting Boolean-reasoning techniques to the SMT level most often requires producing theory lemmas that rule out theory-inconsistent truth assignments. With standard SMT solving, it…
Exploiting Partial-Assignment Enumeration in Optimization Modulo Theories
Gabriele Masina, Roberto Sebastiani
Optimization Modulo Theories (OMT) extends Satisfiability Modulo Theories (SMT) with the task of optimizing some objective function(s). In OMT solvers, a CDCL-based SMT solver enum…
Canonical Decision Diagrams Modulo Theories
Massimo Michelutti, Gabriele Masina, Giuseppe Spallitta +1
Decision diagrams (DDs) are powerful tools to represent effectively propositional formulas, which are largely used in many domains, in particular in formal verification and in know…
On CNF Conversion for SAT and SMT Enumeration
Gabriele Masina, Giuseppe Spallitta, Roberto Sebastiani
Modern SAT and SMT solvers are designed to handle problems expressed in Conjunctive Normal Form (CNF) so that non-CNF problems must be CNF-ized upfront, typically by using variants…
Enhancing SMT-based Weighted Model Integration by Structure Awareness
Giuseppe Spallitta, Gabriele Masina, Paolo Morettin +2
The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research. Whereas substantial progress…
SMT-based Weighted Model Integration with Structure Awareness
Giuseppe Spallitta, Gabriele Masina, Paolo Morettin +2
Weighted Model Integration (WMI) is a popular formalism aimed at unifying approaches for probabilistic inference in hybrid domains, involving logical and algebraic constraints. Des…