"Local Model Checking of Weighted CTL with Upper-Bound Constraints"@en . "85248" . "Proceedings of International SPIN Symposium on Model Checking of Software (SPIN'13)" . "We present a symbolic extension of dependency graphs by Liu and Smolka in order to model-check weighted Kripke structures against the logic CTL with upper-bound weight constraints. Our extension introduces a new type of edges into dependency graphs and lifts the computation of fixed-points from boolean domain to nonnegative integers in order to cope with the weights. We present both global and local algorithms for the fixed-point computation on symbolic dependency graphs and argue for the advantages of our approach compared to the direct encoding of the model checking problem into dependency graphs. We implement all algorithms in a publicly available tool prototype and evaluate them on several experiments. The principal conclusion is that our local algorithm is the most efficient one with an order of magnitude improvement for model checking problems with a high number of \u201Cwitnesses\u201D."@en . "1"^^ . . "New York, USA" . "Springer-Verlag" . "10.1007/978-3-642-39176-7_12" . "9783642391750" . . "Larsen, Kim G." . . . . "Local Model Checking of Weighted CTL with Upper-Bound Constraints" . . "Local Model Checking of Weighted CTL with Upper-Bound Constraints" . "Netherlands" . . . "4"^^ . . . "14330" . "Jensen, Jonas F." . "0302-9743" . . . "Oestergaard, Lars K." . . "Local Model Checking of Weighted CTL with Upper-Bound Constraints"@en . . "RIV/00216224:14330/13:00072717!RIV14-MSM-14330___" . "18"^^ . "2013-01-01+01:00"^^ . . "Srba, Ji\u0159\u00ED" . "I, P(LG13010)" . "[6BEEB2A06A7D]" . . "RIV/00216224:14330/13:00072717" . . . "weigted CTL; model checking; Kripke structure; on-the-fly technique"@en . "We present a symbolic extension of dependency graphs by Liu and Smolka in order to model-check weighted Kripke structures against the logic CTL with upper-bound weight constraints. Our extension introduces a new type of edges into dependency graphs and lifts the computation of fixed-points from boolean domain to nonnegative integers in order to cope with the weights. We present both global and local algorithms for the fixed-point computation on symbolic dependency graphs and argue for the advantages of our approach compared to the direct encoding of the model checking problem into dependency graphs. We implement all algorithms in a publicly available tool prototype and evaluate them on several experiments. The principal conclusion is that our local algorithm is the most efficient one with an order of magnitude improvement for model checking problems with a high number of \u201Cwitnesses\u201D." . .