Constraint Programming on Infinite Data Streams

Arnaud Lallouet 1 Yat Chiu Law 2 Jimmy H.M. Lee 2 Charles F.K. Siu 2
1 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Classical constraint satisfaction problems (CSPs) are commonly defined on finite domains. In real life, constrained variables can evolve over time. A variable can actually take an infinite sequence of values over discrete time points. In this paper, we propose constraint programming on infinite data streams, which provides a natural way to model constrained time-varying problems. In our framework, variable domains are specified by ω-regular languages. We introduce special stream operators as basis to form stream expressions and constraints. Stream CSPs have infinite search space. We propose a search procedure that can recognize and avoid infinite search over duplicate search space. The solution set of a stream CSP can be represented by a B¨uchi automaton allowing stream values to be non-periodic. Consistency notions are defined to reduce the search space early. We illustrate the feasibility of the framework by examples and experiments.
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Arnaud Lallouet, Yat Chiu Law, Jimmy H.M. Lee, Charles F.K. Siu. Constraint Programming on Infinite Data Streams. International Joint Conference on Artificial Intelligence, Jul 2011, Barcelone, Spain. pp.597-604. ⟨hal-01009693⟩



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