Constraint answer set programming (CASP) is a family of hybrid approaches integrating answer set programming (ASP) and constraint programming (CP). These hybrid approaches have already proven to be very successful in various domains. In this paper we present first evaluation results for the CASP solver ASCASS, which provides novel methods for defining and exploiting problem-dependent search heuristics. Beyond the possibility of using already built-in problem-independent heuristics, ASCASS allows on the ASP level the definition of problem-dependent variable selection, value selection and pruning strategies, which guide the search of the CP solver. The proof-of-concept evaluation was carried out on benchmark instances of the real world Partner Units Problem (PUP). Due to a sophisticated heuristic, which cannot be represented by other ASP or CASP solvers, ASCASS shows superior performance.
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