CompoSAT: Specification-Guided Coverage for Model Finding

2018 ◽  
pp. 568-587 ◽  
Author(s):  
Sorawee Porncharoenwase ◽  
Tim Nelson ◽  
Shriram Krishnamurthi
Keyword(s):  
Model Finding

scholarly journals Lightweight string reasoning in model finding

2013 ◽  
Vol 14 (1) ◽  
pp. 413-427
Author(s):  
Fabian Büttner ◽  
Jordi Cabot
Keyword(s):  
Model Finding

HYBRID INCREMENTAL ALGORITHMS FOR BOOLEAN SATISFIABILITY

2012 ◽  
Vol 21 (06) ◽  
pp. 1250025 ◽  
Author(s):  
FLORIAN LETOMBE ◽  
JOAO MARQUES-SILVA
Keyword(s):  
Boolean Satisfiability ◽  
Equivalence Checking ◽  
Satisfiability Modulo Theory ◽  
Practical Applications ◽  
Sat Solvers ◽  
Hybrid Approaches ◽  
Software Model ◽  
Wide Range ◽  
Model Finding ◽  
Problem Instances

Boolean Satisfiability (SAT) solvers have been successfully applied to a wide range of practical applications, including hardware model checking, software model finding, equivalence checking, and planning, among many others. SAT solvers are also the building block of more sophisticated decision procedures, including Satisfiability Modulo Theory (SMT) solvers. The large number of applications of SAT yields ever more challenging problem instances, and motivate the development of more efficient algorithms. Recent work studied hybrid approaches for SAT, which involves integrating incomplete and complete SAT solvers. This paper proposes a number of improvements to hybrid SAT solvers. Experimental results demonstrate that the proposed optimizations are effective. The resulting algorithms in general perform better and, more importantly, are significantly more robust.

scholarly journals Symmetry Avoidance in MACE-Style Finite Model Finding

2019 ◽  
pp. 3-21 ◽  
Author(s):  
Giles Reger ◽  
Martin Riener ◽  
Martin Suda
Keyword(s):  
Finite Model ◽  
Model Finding

scholarly journals Black Box Models and Sociological Explanations: Predicting GPA Using Neural Networks

2017 ◽  
Author(s):  
Thomas Davidson
Keyword(s):  
Network Models ◽  
Predictive Performance ◽  
Black Box ◽  
Neural Network Models ◽  
Grade Point ◽  
Predictive Variables ◽  
Box Models ◽  
Model Finding ◽  
Basic Network ◽  
Predicting Gpa

The Fragile Families Challenge provided an opportunity to empirically assess the applicability of black box machine learning models to sociological questions and the extent to which interpretable explanations can be extracted from these models. In this paper I use neural network models to predict high school grade-point average and examine how variations of basic network parameters affect predictive performance. Using a recently proposed technique, I identify the most important predictive variables used by the best-performing model, finding that they relate to parenting and the child’s cognitive and behavioral development, consistent with prior work. I conclude by discussing the implications of these findings for the relationship between prediction and explanation in sociological analyses.

scholarly journals Model Finding in the EMF Ecosystem.

2020 ◽  
Vol 19 (2) ◽  
pp. 10:1
Author(s):  
Jesús Sánchez Cuadrado ◽  
Martin Gogolla
Keyword(s):  
Model Finding

MODEL FINDING IN SEMANTICALLY GUIDED INSTANCE-BASED THEOREM PROVING

1994 ◽  
Vol 21 (3) ◽  
pp. 221-235 ◽  
Author(s):  
Heng Chu ◽  
David A. Plaisted
Keyword(s):  
Theorem Proving ◽  
Model Finding

Genetic algorithms used in model finding and fitting for neutron reflection experiments

1994 ◽  
Vol 198 (1-3) ◽  
pp. 24-26 ◽  
Author(s):  
V.-O. de Haan ◽  
G.G. Drijkoningen
Keyword(s):  
Genetic Algorithms ◽  
Neutron Reflection ◽  
Model Finding
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