Complex computational systems - such as pervasive, adaptive, and self-organising ones - typically rely on simple yet expressive coordination mechanisms: this is why coordination models and languages can be exploited as the sources of the essential abstractions and mechanisms to build such systems. While the features of tuple-based models make them well suited for complex system coordination, they lack the probabilistic mechanisms for modelling the stochastic behaviours typically required by adaptivity and self-organisation. To this end, in this paper we explicitly introduce uniform primitives as a probabilistic specialisation of standard tuple-based coordination primitives, replacing don't know non-determinism with uniform distribution. We define their semantics and discuss their expressiveness and their impact on system predictability.
Tuple-based coordination of stochastic systems with uniform primitives / Mariani, Stefano; Omicini, Andrea. - 1099:(2013), pp. 8-15. (Intervento presentato al convegno 14th Workshop "From Objects to Agents", WOA 2013 - Co-located with the 13th Conference of the Italian Association for Artificial Intelligence, AI*IA 2013 tenutosi a Turin, ita nel 2013).
Tuple-based coordination of stochastic systems with uniform primitives
MARIANI, Stefano;
2013
Abstract
Complex computational systems - such as pervasive, adaptive, and self-organising ones - typically rely on simple yet expressive coordination mechanisms: this is why coordination models and languages can be exploited as the sources of the essential abstractions and mechanisms to build such systems. While the features of tuple-based models make them well suited for complex system coordination, they lack the probabilistic mechanisms for modelling the stochastic behaviours typically required by adaptivity and self-organisation. To this end, in this paper we explicitly introduce uniform primitives as a probabilistic specialisation of standard tuple-based coordination primitives, replacing don't know non-determinism with uniform distribution. We define their semantics and discuss their expressiveness and their impact on system predictability.Pubblicazioni consigliate
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