Counterexample Generation for Infinite-State Chemical Reaction Networks
Mohammad Ahmadi (1), Zhen Zhang (2), Chris Myers (3), Chris Winstead, (2), Hao Zheng (1) ((1) University of South Florida,(2) Utah State, University,(3) University of Colorado Boulder)

TL;DR
This paper introduces a novel SMT-based bounded model checking approach for generating counterexamples in infinite-state chemical reaction networks, effectively identifying critical property-violating paths with high probability.
Contribution
It presents the first method for counterexample generation in infinite-state CRNs using SMT solving, with optimizations for scalability to large models.
Findings
Successfully generated counterexamples for complex CRN models
Scalable approach with divide-and-conquer optimization
Effective identification of high-probability violating paths
Abstract
Counterexample generation is an indispensable part of model checking process. In stochastic model checking, counterexample generation is a challenging problem as it is not enough to find a single trace that violates the given property. Instead, a potentially large set of traces with enough probability to violate the property needs to be found. This paper considers counterexample generation for chemical reaction network (CRN) models with potentially infinite state space. A method based on bounded model checking using SMT solving is developed for counterexample generation for CRNs. It intends to find a small set of property violating paths of a given model such that they collectively have a total probability that is above a given threshold. A unique challenge is due to the highly connected state space of CRNs where a counterexample is only a tiny subset of all property violating paths. To…
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Taxonomy
TopicsMachine Learning in Materials Science · Fuel Cells and Related Materials · Computational Drug Discovery Methods
