Quantified Boolean formulas are a powerful representation that have been used to capture and solve a variety of problems in Artificial Intelligence. While most research has focused...
Horn⊃ is a logic programming language which extends usual Horn clauses by adding intuitionistic implication in goals and clause bodies. This extension can be seen as a form of s...
Probabilistic databases hold promise of being a viable means for large-scale uncertainty management, increasingly needed in a number of real world applications domains. However, q...
A -model is a satisfying assignment of a Boolean formula for which any small alteration, such as a single bit flip, can be repaired by flips to some small number of other bits, yi...
In this paper we show how to do symbolic model checking using Boolean Expression Diagrams (BEDs), a non-canonical representation for Boolean formulas, instead of Binary Decision Di...
Poul Frederick Williams, Armin Biere, Edmund M. Cl...
We discuss a Probably Approximate Correct (PAC) learning paradigm for Boolean formulas, which we call PAC meditation, where the class of formulas to be learnt is not known in advan...
Bruno Apolloni, Andrea Brega, Dario Malchiodi, Gio...
Abstract. We describe a novel decision procedure for Quantified Boolean Formulas (QBFs) which aims to unleash the hidden potential of quantified reasoning in applications. The Sk...
Abstract. Quantified Boolean formulas (QBFs) play an important role in artificial intelligence subjects, specially in planning, knowledge representation and reasoning [20]. In th...
Mohammad GhasemZadeh, Volker Klotz, Christoph Mein...
We give a randomized algorithm for testing satisfiability of Boolean formulas in conjunctive normal form with no restriction on clause length. Its running time is at most 2n(1−1...
Current algorithms for bounded model checking (BMC) use SAT methods for checking satisfiability of Boolean formulas. These BMC methods suffer from a potential memory explosion prob...