Abstract
A distinguishing feature of Answer Set Programming is its versatility. In addition to satisfiability testing, it offers various forms of model enumeration, intersection or unioning, as well as optimization. Moreover, there is an increasing interest in incremental and reactive solving due to their applicability to dynamic domains. However, so far no comparative studies have been conducted, contrasting the respective modeling capacities and their computational impact. To assess the variety of different forms of ASP solving, we propose Alex Randolph’s board game Ricochet Robots as a transverse benchmark problem that allows us to compare various approaches in a uniform setting. To begin with, we consider alternative ways of encoding ASP planning problems and discuss the underlying modeling techniques. In turn, we conduct an empirical analysis contrasting traditional solving, optimization, incremental, and reactive approaches. In addition, we study the impact of some boosting techniques in the realm of our case study.
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Gebser, M. et al. (2013). Ricochet Robots: A Transverse ASP Benchmark. In: Cabalar, P., Son, T.C. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2013. Lecture Notes in Computer Science(), vol 8148. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40564-8_35
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DOI: https://doi.org/10.1007/978-3-642-40564-8_35
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