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Algorithms for the Generation of State-Level Representations of Stochastic Activity Networks with General Reward Structures

Lookup NU author(s): Professor Aad van Moorsel



Stochastic Petri nets (SPNs) and extensions are a popular method for evaluating a wide variety of systems. In most cases, their numerical solution requires generating a state-level stochastic process, which captures the behavior of the SPN with respect to a set of specified performance measures. These measures are commonly defined at the net level by means of a reward variable. In this paper, we discuss issues regarding the generation of state-level reward models for systems specified as stochastic activity networks (SANs) with “step-based reward structures”. Step-based reward structures are a generalization of previously proposed reward structures for SPNs and can represent all reward variables that can be defined on the marking behavior of a net. While discussing issues related to the generation of the underlying state-level reward model, we provide an algorithm to determine whether a given SAN is “well-specified” A SAN is well-specified if choices about which instantaneous activity completes among multiple simultaneously-enabled instantaneous activities do not matter, with respect to the probability of reaching next possible stable markings and the distribution of reward obtained upon completion of a timed activity. The fact that a SAN is well specified is both a necessary and sufficient condition for its behavior to be completely probabilistically specified, and hence is an important property to determine.

Publication metadata

Author(s): Qureshi MA, Sanders WH, van Moorsel A, German R

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Software Engineering

Year: 1996

Volume: 22

Issue: 9

Pages: 603-614

Date deposited: 28/09/2010

ISSN (print): 0098-5589

ISSN (electronic): 1939-3520

Publisher: IEEE Computer Society


DOI: 10.1109/32.541432


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