Abstract
Answer Set Programming (ASP) has become a prime paradigm for declarative problem solving due to its combination of an easy yet expressive modeling language with high-performance Boolean constraint solving technology. However, certain applications are more naturally modeled by mixing Boolean with non-Boolean constructs, for instance, accounting for resources, fine timings, or functions over finite domains. The challenge lies in combining the elaborated solving capacities of ASP, like backjumping and conflict-driven learning, with advanced techniques from the area of constraint programming (CP). I therefore developed the solver clingcon, which follows the approach of modern Satisfiability Modulo Theories (SMT). My research shall contribute to bridging the gap between Boolean and Non-Boolean reasoning, in order to bring out the best of both worlds.
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