A more effective use of information in search for quantified Boolean formulae
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The solving of Quantified Boolean Formulae (QBFs) has recently become of great interest. QBFs are an extension of the Satisfiability problem (SAT), which has been studied in depth. Many QBF techniques are built as extensions to SAT techniques. While this can be useful, it also means that QBF specific techniques have not received as much attention as they could. The contributions of this thesis are: Introduce new data structures for QBF which use the information available to the QBF solver more effectively. This reduces the amount of time taken to update the data structures. Description of a new method of using a SAT solver within a QBF solver. This does not ignore the results of the SAT solver as is done with previous techniques. The use of an incomplete SAT solver in QBF search is also discussed, which gives rise to the first incomplete QBF solver. A detailed analysis of solution-directed backjumping. This is shown to be less effective than was previously thought. New techniques are developed to build better solution sets that result in improved operation of solution-directed backjumping. A new technique for solution learning is developed. This increases the amount of information learned for each solution found without a large increase in the space required. An experimental analysis shows that this results in a reduced number of backtracks on many problems compared to other solution learning techniques. Overall, the better use of information is shown to lead to improvements in QBF solving techniques
Thesis, PhD Doctor of Philosopy
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