Anthony Monnet

Anthony Monnet
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Main area of research
Natural language & AI

Anthony Monnet is a NLU Engineer in the R&D Mobility Team with research interests in the area of Natural Language & AI. Anthony currently works on the natural language understanding of French for various mobility voice assistant projects. Before joining Nuance, Anthony earned a computer science M.Sc at the Université Paul-Verlaine of Metz (France) and a computer science Ph.D. at the Université du Québec à Montréal (Canada). His thesis was focused on algorithmic variations of the DPLL algorithm for the resolution of the satisfiability problem on propositional logic formulas.

Selected articles

Efficient partial order CDCL using assertion level choice heuristics

We previously designed Partial Order Conflict Driven Clause Learning (PO-CDCL), a variation of the satisfiability solving CDCL algorithm with a partial order on decision levels,

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CDCL with less destructive backtracking through partial ordering

Conflict-driven clause learning is currently the most efficient complete algorithm for satisfiability solving. However, a conflict-directed backtrack deletes potentially large portions of the current assignment

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Scalable formula decomposition for propositional satisfiability

Propositional satisfiability solving, or SAT, is an important reasoning task arising in numerous applications, such as circuit design, formal verification, planning, scheduling or probabilistic reasoning.

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JaCk-SAT: A new parallel scheme to solve the satisfiability problem (SAT) based on join-and-check

This paper presents and investigates for the first time a new trail for parallel solving of the Satisfiability problem based on a simple and efficient

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