Abstract
Most combination methods for decision procedures known from the area of automated deduction assign to a given “combined” input problem an exponential number of output pairs where the two components represent “pure” decision problems that can be decided with suitable component decision procedures. A mixed input decision problem evaluates to true iff there exists an output pair where both components evaluate to true. We provide a formal framework that explains why in many cases it is impossible to have a polynomial-time optimization of such a combination method, and why often combined decision problems are NP-hard, regardless of the complexity of the component problems. As a first application we consider Oppen’s combination method for algorithms that decide satisfiability of quantifier-free formulae in disjunctive normal form w.r.t. a first-order theory. A collection of first-order theories is given where combined problems are always NP-hard and Oppen’s method does not have a polynomial-time optimization. As a second example, similar results are given for the problem of combining algorithms that decide whether a unification problem has a solution w.r.t. to a given equational theory.
This paper was first presented at the “Workshop on Complexity in Automated Deduction” organized by M. Hermann in Nancy (June 1999).
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Schulz, K.U. (2000). Why Combined Decision Problems Are Often Intractable. In: Kirchner, H., Ringeissen, C. (eds) Frontiers of Combining Systems. FroCoS 2000. Lecture Notes in Computer Science(), vol 1794. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720084_15
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DOI: https://doi.org/10.1007/10720084_15
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