Abstract
Many applications in Artificial Intelligence and Data Bases deal with (domain and/or goal-dependent) temporal patterns that repeat regularly over time ( periodicities). Hence the need for formal languages that allow users to define periodicities. Among proposals in the literature, symbolic languages are sets of operators for compositional and incremental definition. We propose a new methodology for designing symbolic languages, based on a preliminary analysis of the required expressiveness. The analysis is guided by a classification of the periodicities according to expressiveness properties that are mutually independent. Each of the properties will be then paired with a language operator such that the addition of that operator to a language adds the capability of defining all periodicities having the corresponding property. A modular family of languages with well-defined expressiveness is the result of this process. Moreover, in this paper we instantiate the general methodology by identifying a specific set of properties which we also use in order to classify the expressiveness of different symbolic approaches in the literature.
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