In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its li...In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its literals.The current WMC solvers work on Conjunctive Normal Form(CNF)formulas.However,CNF is not a natural representation for human-being in many applications.Motivated by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB formulas.Based on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool PBCounter.We compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF formula.The experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.展开更多
Bushnell and the author proposed the neural networks for NOT, AND, OR, NAND, NOR, XOR and XNOR gates. Using these neural networks, the neural networks of any logic circuits can be constructd. From this, the consistent...Bushnell and the author proposed the neural networks for NOT, AND, OR, NAND, NOR, XOR and XNOR gates. Using these neural networks, the neural networks of any logic circuits can be constructd. From this, the consistent signals in the logic circuits will be transformed into the global minimal points of a quadratic pseudo Boolean function. Thus the neural network application in the field of circuit modeling and automatic test pattern generation can be widened.展开更多
基金supported by NSFC(Grant Nos.61976050,61972384)the Jilin Province Science and Technology Department project(Nos.20240101378JC,20240602005RC,YDZJ202201ZYTS415)Jilin Education Department Project(No.JJKH20231319KJ)。
文摘In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its literals.The current WMC solvers work on Conjunctive Normal Form(CNF)formulas.However,CNF is not a natural representation for human-being in many applications.Motivated by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB formulas.Based on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool PBCounter.We compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF formula.The experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.
文摘Bushnell and the author proposed the neural networks for NOT, AND, OR, NAND, NOR, XOR and XNOR gates. Using these neural networks, the neural networks of any logic circuits can be constructd. From this, the consistent signals in the logic circuits will be transformed into the global minimal points of a quadratic pseudo Boolean function. Thus the neural network application in the field of circuit modeling and automatic test pattern generation can be widened.
