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Performance Analysis of Probabilistic Action Systems

Performance Analysis of Probabilistic Action Systems
Performance Analysis of Probabilistic Action Systems
Formal notations like B or action systems support a notion of refinement. Refinement relates an abstract specification A to a concrete specification C that is as least as deterministic. Knowing A and C one proves that C refines, or implements, specification A. In this study we consider specification A as given and concern ourselves with a way to find a good candidate for implementation C. To this end we classify all implementations of an abstract specification according to their performance. We distinguish performance from correctness. Concrete systems that do not meet the abstract specification correctly are excluded. Only the remaining correct implementations C are considered with respect to their performance. A good implementation of a specification is identified by having some optimal behaviour in common with it. In other words, a good refinement corresponds to a reduction of non-optimal behaviour. This also means that the abstract specification sets a boundary for the performance of any implementation. We introduce the probabilistic action system formalism which combines refinement with performance. In our current study we measure performance in terms of long-run expected average-cost. Performance is expressed by means of probability and expected costs. Probability is needed to express uncertainty present in physical environments. Expected costs express physical or abstract quantities that describe a system. They encode the performance objective. The behaviour of probabilistic action systems is described by traces of expected costs. A corresponding notion of refinement and simulation-based proof rules are introduced. Probabilistic action systems are based on discrete-time Markov decision processes. Numerical methods solving the optimisation problems posed by Markov decision processes are well-known, and used in a software tool that we have developed. The tool computes an optimal behaviour of a specification A thus assisting in the search for a good implementation C.
313-331
Hallerstede, Stefan
f3ea39f5-26c7-42da-ae5e-7c91209ac20d
Butler, Michael
54b9c2c7-2574-438e-9a36-6842a3d53ed0
Hallerstede, Stefan
f3ea39f5-26c7-42da-ae5e-7c91209ac20d
Butler, Michael
54b9c2c7-2574-438e-9a36-6842a3d53ed0

Hallerstede, Stefan and Butler, Michael (2004) Performance Analysis of Probabilistic Action Systems. Formal Aspects of Computing, 16 (4), 313-331.

Record type: Article

Abstract

Formal notations like B or action systems support a notion of refinement. Refinement relates an abstract specification A to a concrete specification C that is as least as deterministic. Knowing A and C one proves that C refines, or implements, specification A. In this study we consider specification A as given and concern ourselves with a way to find a good candidate for implementation C. To this end we classify all implementations of an abstract specification according to their performance. We distinguish performance from correctness. Concrete systems that do not meet the abstract specification correctly are excluded. Only the remaining correct implementations C are considered with respect to their performance. A good implementation of a specification is identified by having some optimal behaviour in common with it. In other words, a good refinement corresponds to a reduction of non-optimal behaviour. This also means that the abstract specification sets a boundary for the performance of any implementation. We introduce the probabilistic action system formalism which combines refinement with performance. In our current study we measure performance in terms of long-run expected average-cost. Performance is expressed by means of probability and expected costs. Probability is needed to express uncertainty present in physical environments. Expected costs express physical or abstract quantities that describe a system. They encode the performance objective. The behaviour of probabilistic action systems is described by traces of expected costs. A corresponding notion of refinement and simulation-based proof rules are introduced. Probabilistic action systems are based on discrete-time Markov decision processes. Numerical methods solving the optimisation problems posed by Markov decision processes are well-known, and used in a software tool that we have developed. The tool computes an optimal behaviour of a specification A thus assisting in the search for a good implementation C.

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Published date: February 2004
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 258925
URI: http://eprints.soton.ac.uk/id/eprint/258925
PURE UUID: 80abfb5b-b9b8-4ac8-ae4f-861e8b997bc9
ORCID for Michael Butler: ORCID iD orcid.org/0000-0003-4642-5373

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Date deposited: 27 Jan 2005
Last modified: 15 Mar 2024 02:50

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Contributors

Author: Stefan Hallerstede
Author: Michael Butler ORCID iD

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