Weighted MaxSMT enables robust inference of qualitative biological models from uncertain steady-state data by relaxing conflicting observations as soft constraints.
In: Handbook of Satisfiability, FAIA, vol
3 Pith papers cite this work. Polarity classification is still indexing.
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Introduces parallelizable, theory-agnostic methods for complete theory-lemma enumeration in SMT that scale better than classic eager encodings on complex instances.
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Inference of Qualitative Models from Steady-State Data via Weighted MaxSMT
Weighted MaxSMT enables robust inference of qualitative biological models from uncertain steady-state data by relaxing conflicting observations as soft constraints.
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Beyond Eager Encodings: A Theory-Agnostic Approach to Theory-Lemma Enumeration in SMT
Introduces parallelizable, theory-agnostic methods for complete theory-lemma enumeration in SMT that scale better than classic eager encodings on complex instances.
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