On the trade-off between labels and weights in quantitative bisimulation

Reductions for transition systems have been recently introduced as a uniform and principled method for comparing the expressiveness of system models with respect to a range of properties, especially bisimulations. In this paper we study the expressiveness (w.r.t. bisimulations) of models for quantitative computations such as weighted labelled transition systems (WLTSs), uniform labelled transition systems (ULTraSs), and state-to-function transition systems (FuTSs). We prove that there is a trade-off between labels and weights: at one extreme lays the class of “unlabelled” weighted transition systems where information is presented using weights only; at the other lays the class of labelled transition systems (LTSs) where information is shifted on labels. These categories of systems cannot be further reduced in any significant way and subsume all the aforementioned models.

[1]  Marino Miculan,et al.  Weak bisimulations for labelled transition systems weighted over semirings , 2013, ArXiv.

[2]  Bartek Klin,et al.  Structural Operational Semantics for Weighted Transition Systems , 2009, Semantics and Algebraic Specification.

[3]  Peter Aczel,et al.  A Final Coalgebra Theorem , 1989, Category Theory and Computer Science.

[4]  Ana Sokolova,et al.  Generic Trace Semantics via Coinduction , 2007, Log. Methods Comput. Sci..

[5]  Erik P. de Vink,et al.  Bisimulation of Labelled State-to-Function Transition Systems Coalgebraically , 2015, Log. Methods Comput. Sci..

[6]  Marino Miculan,et al.  On the Bisimulation Hierarchy of State-to-Function Transition Systems , 2016, ICTCS.

[7]  James Worrell,et al.  On the final sequence of a finitary set functor , 2005, Theor. Comput. Sci..

[8]  Rocco De Nicola,et al.  A uniform framework for modeling nondeterministic, probabilistic, stochastic, or mixed processes and their behavioral equivalences , 2013, Inf. Comput..

[9]  Marino Miculan,et al.  Behavioural equivalences for coalgebras with unobservable moves , 2014, J. Log. Algebraic Methods Program..

[10]  Nancy A. Lynch,et al.  Probabilistic Simulations for Probabilistic Processes , 1994, Nord. J. Comput..

[11]  Marino Miculan,et al.  Structural operational semantics for non-deterministic processes with quantitative aspects , 2014, Theor. Comput. Sci..

[12]  Jane Hillston,et al.  A compositional approach to performance modelling , 1996 .

[13]  Marco Peressotti,et al.  A Uniform Framework for Timed Automata , 2016, CONCUR.

[14]  Marino Miculan,et al.  Reductions for Transition Systems at Work: Deriving a Logical Characterization of Quantitative Bisimulation , 2017, ArXiv.

[15]  Robin Milner,et al.  Communication and concurrency , 1989, PHI Series in computer science.

[16]  Jan J. M. M. Rutten,et al.  Universal coalgebra: a theory of systems , 2000, Theor. Comput. Sci..

[17]  Sam Staton Relating Coalgebraic Notions of Bisimulation , 2009, CALCO.

[18]  Erik P. de Vink,et al.  A Definition Scheme for Quantitative Bisimulation , 2015, QAPL.

[19]  Marino Miculan,et al.  GSOS for non-deterministic processes with quantitative aspects , 2014, QAPL.

[20]  Bernhard Steffen,et al.  Reactive, Generative and Stratified Models of Probabilistic Processes , 1995, Inf. Comput..

[21]  Diego Latella,et al.  A uniform definition of stochastic process calculi , 2013, CSUR.

[22]  B. Jacobs,et al.  A tutorial on (co)algebras and (co)induction , 1997 .