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The features of probabilistic adaptive systems are especially the uncertainty and reconfigurability. The structure of a part of the system may be totally unknown or partially unknown at a particular time. Openness is also an inherent property, as agents may join or leave the system throughout its lifetime. This poses severe challenges for state-based specification. The languages in which probabilistic reconfigurable systems are specified should be clear and intuitive, and thus accessible to generation, inspection and modification by humans. This paper introduces a new approach for specifying adaptive probabilistic discrete event systems. We introduce the semantics of GR-TNCES to optimize the specification of unpredictable timed reconfiguration scenario running under resources constraints. We also apply this approach to specify the requirements of an automotive transport system and we evaluate its benefits.

In a field test at Saarland University, a decision tool named OWLracle has been developed. It supports university managers in comparing benefit and costs of different methods of energy saving. They include building automation as well as economic and psychological incentives. In order to guarantee transferability to other university properties, appropriate typicals are required. To this end, the paper defines common Building Types, Room Types, and User Types, representative of German universities. Furthermore, it introduces a BCVTB-based implementation of OWLracle, integrating Modelica® libraries of Room and User Types and EnergyPlus models of Building Types. 27 combinations of Building, Room and User Type are compared with respect to their total energy consumption.

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