Distances for Weighted Transition Systems: Games and Properties

We develop a general framework for reasoning about distances between transition systems with quantitative information. Taking as starting point an arbitrar y distance on system traces, we show how this leads to natural definitions of a linear and a branching d istance on states of such a transition system. We show that our framework generalizes and unifies a l arge variety of previously considered system distances, and we develop some general properties of our distances. We also show that if the trace distance admits a recursive characterization, then t he corresponding branching distance can be obtained as a least fixed point to a similar recursive charact erization. The central tool in our work is a theory of infinite path-building games with quantitative o bjectives.

[1]  Thomas A. Henzinger,et al.  Quantifying Similarities Between Timed Systems , 2005, FORMATS.

[2]  Rob J. van Glabbeek,et al.  The Linear Time - Branching Time Spectrum I , 2001, Handbook of Process Algebra.

[3]  Franck van Breugel,et al.  A Behavioural Pseudometric for Metric Labelled Transition Systems , 2005, CONCUR.

[4]  Pavol Cerný,et al.  Simulation distances , 2010, Theor. Comput. Sci..

[5]  Colin Stirling,et al.  Modal and Temporal Logics for Processes , 1996, Banff Higher Order Workshop.

[6]  Dexter Kozen A Probabilistic PDL , 1985, J. Comput. Syst. Sci..

[7]  Wil M. P. van der Aalst,et al.  Quantifying process equivalence based on observed behavior , 2008, Data Knowl. Eng..

[8]  Scott A. Smolka,et al.  Algebraic Reasoning for Probabilistic Concurrent Systems , 1990, Programming Concepts and Methods.

[9]  Kim G. Larsen,et al.  Metrics for weighted transition systems: Axiomatization and complexity , 2011, Theor. Comput. Sci..

[10]  Kim G. Larsen,et al.  A Quantitative Characterization of Weighted Kripke Structures in Temporal Logic , 2009, MEMICS.

[11]  M. Dufwenberg Game theory. , 2011, Wiley interdisciplinary reviews. Cognitive science.

[12]  Hans L. Bodlaender,et al.  Complexity of Path-Forming Games , 1993, Theor. Comput. Sci..

[13]  Jan Friso Groote,et al.  Structured Operational Semantics and Bisimulation as a Congruence , 1992, Inf. Comput..

[14]  Kim G. Larsen,et al.  Quantitative analysis of weighted transition systems , 2010, J. Log. Algebraic Methods Program..

[15]  Luca Aceto,et al.  2-Nested Simulation Is Not Finitely Equationally Axiomatizable , 2000, STACS.

[16]  Kim G. Larsen,et al.  Infinite Runs in Weighted Timed Automata with Energy Constraints , 2008, FORMATS.

[17]  François Laviolette,et al.  Approximate Analysis of Probabilistic Processes: Logic, Simulation and Games , 2008, 2008 Fifth International Conference on Quantitative Evaluation of Systems.

[18]  Luca de Alfaro,et al.  Linear and Branching System Metrics , 2009, IEEE Transactions on Software Engineering.

[19]  R. V. Glabbeek CHAPTER 1 – The Linear Time - Branching Time Spectrum I.* The Semantics of Concrete, Sequential Processes , 2001 .

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

[21]  Thomas A. Henzinger,et al.  Discounting the Future in Systems Theory , 2003, ICALP.

[22]  Kim C. Border,et al.  Infinite Dimensional Analysis: A Hitchhiker’s Guide , 1994 .

[23]  Krishnendu Chatterjee,et al.  Quantitative languages , 2008, TOCL.

[24]  Thomas A. Henzinger,et al.  The Embedded Systems Design Challenge , 2006, FM.

[25]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[26]  R. N. R. B.,et al.  Geography , 1935, Nature.

[27]  R. J. vanGlabbeek The linear time - branching time spectrum , 1990 .

[28]  Radha Jagadeesan,et al.  Metrics for labelled Markov processes , 2004, Theor. Comput. Sci..