Measure-adaptive state-space construction

Measure-adaptive state-space construction is the process of exploiting symm etry in high-level model and performance measure specifications to automati cally construct reduced state-space Markov models that support the evaluati on of the performance measure. This paper describes a new reward variable s pecification technique, which combined with recently developed state-space construction techniques will allow us to build tools capable of measure-ada ptive state-space construction. That is, these tools will automatically ada pt the size of the state space to constraints derived from the system model and the user-specified reward variables. The work described in this paper extends previous work in two directions. First, standard reward variable de finitions are extended to allow symmetry in the reward variable to be ident ified and exploited. Then, symmetric reward variables are further extended to include the set of path-based reward variables described in earlier work . In addition to the theory, several examples are introduced to demonstrate these new techniques. (C) 2001 Elsevier Science B.V. All rights reserved.