Algorithmic Minimization of Uncertain Continuous-Time Markov Chains

The assumption of perfect knowledge of rate parameters in continuous-time Markov chains (CTMCs) is undermined when confronted with reality, where they may be uncertain due to lack of information or because of measurement noise. Here we consider uncertain CTMCs (UCTMCs), where rates are assumed to va...

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Vydáno v:IEEE transactions on automatic control Ročník 68; číslo 11; s. 1 - 16
Hlavní autoři: Cardelli, Luca, Grosu, Radu, Larsen, Kim G., Tribastone, Mirco, Tschaikowski, Max, Vandin, Andrea
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Benchmark testing
Computational modeling
Decision analysis
Markov analysis
Markov chains
Markov processes
Minimization
Noise measurement
Optimization
Partitioning algorithms
Transient analysis
Uncertainty
ISSN:0018-9286, 1558-2523
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Shrnutí:The assumption of perfect knowledge of rate parameters in continuous-time Markov chains (CTMCs) is undermined when confronted with reality, where they may be uncertain due to lack of information or because of measurement noise. Here we consider uncertain CTMCs (UCTMCs), where rates are assumed to vary non-deterministically with time from bounded continuous intervals. An uncertain CTMC can be therefore seen as a specific type of Markov decision process for which the analysis is computationally difficult. To tackle this, we develop a theory of minimization which generalizes the notion of lumpability for CTMCs. Our first result is a quantitative and logical characterization of minimization. Specifically, we show that the reduced UCTMC model has a macro-state for each block of a partition of the state space, which preserves value functions and logical formulae whenever rewards are equal within each block. The second result is an efficient minimization algorithm for UCTMCs by means of partition refinement. As application, we show that reductions in a number of CTMCbenchmark models are robust with respect to uncertainties in original rates.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2023.3244093