Bisimulation and Logical Preservation for ContinuousTime Markov Decision Processes


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1 Bisimulation and Logical Preservation for ContinuousTime Markov Decision Processes Martin R. Neuhäußer 1,2 JoostPieter Katoen 1,2 1 RWTH Aachen University, Germany 2 University of Twente, The Netherlands CONCUR 2007, Lisbon, Portugal Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
2 Repairman s Roulette Introduction Motivation Maintenance of a safety critical system: System is operational in state up. Failures are exponentially distributed: Mean delay to next failure 1 µ. Probability for a failure within time t: ε rep 1 up µ off rep 2 A failure moves the system from up to off. Two types of repairmen: 1 cautious: slow and reliable via α or 2 aggressive: unreliable but fast via β. ε α, 4λ rep 1 λ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
3 Repairman s Roulette Introduction Motivation Maintenance of a safety critical system: System is operational in state up. Failures are exponentially distributed: Mean delay to next failure 1 µ. Probability for a failure within time t: ε rep 1 up µ off rep 2 A failure moves the system from up to off. Two types of repairmen: 1 cautious: slow and reliable via α or 2 aggressive: unreliable but fast via β. ε α, 4λ rep 1 λ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
4 Repairman s Roulette Introduction Motivation Maintenance of a safety critical system: System is operational in state up. Failures are exponentially distributed: Mean delay to next failure 1 µ. Probability for a failure within time t: ε rep 1 up µ off rep 2 A failure moves the system from up to off. Two types of repairmen: 1 cautious: slow and reliable via α or 2 aggressive: unreliable but fast via β. ε α, 4λ rep 1 λ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
5 Repairman s Roulette Introduction Motivation Maintenance of a safety critical system: System is operational in state up. Failures are exponentially distributed: Mean delay to next failure 1 µ. Probability for a failure within time t: ε rep 1 up µ off rep 2 A failure moves the system from up to off. Two types of repairmen: 1 cautious: slow and reliable via α or 2 aggressive: unreliable but fast via β. ε α, 4λ rep 1 λ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
6 Repairman s Roulette Introduction Motivation Maintenance of a safety critical system: System is operational in state up. Failures are exponentially distributed: Mean delay to next failure 1 µ. Probability for a failure within time t: ε rep 1 up µ off rep 2 A failure moves the system from up to off. Two types of repairmen: 1 cautious: slow and reliable via α or 2 aggressive: unreliable but fast via β. ε α, 4λ rep 1 λ Measure of interest: Probability to return to up in t time units after a failure. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
7 Introduction Motivation Why ContinuousTime Markov Decision Processes? 1 CTMDPs are an important model in stochastic control theory stochastic scheduling [Qiu et al.] [Feinberg et al.] 2 CTMDPs provide the semantic basis for nonwellspecified stochastic activity networks [Sanders et al.] generalised stochastic Petri nets with confusion [Chiola et al.] Markovian process algebras [Hermanns et al., Hillston et al.] In this talk: 1 Preliminary definitions. 2 Strong bisimulation on CTMDPs. 3 Adaptation of Continuous Stochastic Logic (cf. CTL) to CTMDPs. 4 Preservation of CSL under strong bisimulation. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
8 Introduction Motivation Why ContinuousTime Markov Decision Processes? 1 CTMDPs are an important model in stochastic control theory stochastic scheduling [Qiu et al.] [Feinberg et al.] 2 CTMDPs provide the semantic basis for nonwellspecified stochastic activity networks [Sanders et al.] generalised stochastic Petri nets with confusion [Chiola et al.] Markovian process algebras [Hermanns et al., Hillston et al.] In this talk: 1 Preliminary definitions. 2 Strong bisimulation on CTMDPs. 3 Adaptation of Continuous Stochastic Logic (cf. CTL) to CTMDPs. 4 Preservation of CSL under strong bisimulation. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
9 Introduction Preliminaries Definition (Continuous Time Markov Decision Process) A CTMDP (S, Act,R, AP, L) has a finite set of states S and propositions AP. L : S 2 AP is its state labelling. Further Act {α, β,... } is a finite set of actions and R : S Act S Ê 0 is a transition rate matrix such that R(s, α, s ) λ is the rate of a negative exponential distribution ( λ e λ t if t 0 f X(t) and E[X] 1 0 otherwise λ Example such that Act(s) {α Act s S. R(s, α, s ) > 0} for all s S. E(s, α) P s S R(s, α, s ) is the exit rate of s under α. 1 Choose action in Act(s 1 ) {α, β} nondeterministically: say α 2 Race condition between αtransitions: 1 Mean delay: Probability to move to s 3: 1 R(s 1,α,s 2 )+R(s 1,α,s 3 ) 20 R(s 1,α,s 3 ) 1 E(s,α) 4 Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
10 Introduction Preliminaries Definition (Continuous Time Markov Decision Process) A CTMDP (S, Act,R, AP, L) has a finite set of states S and propositions AP. L : S 2 AP is its state labelling. Further Act {α, β,... } is a finite set of actions and R : S Act S Ê 0 is a transition rate matrix such that R(s, α, s ) λ is the rate of a negative exponential distribution ( λ e λ t if t 0 f X(t) and E[X] 1 0 otherwise λ such that Act(s) {α Act s S. R(s, α, s ) > 0} for all s S. E(s, α) P s S R(s, α, s ) is the exit rate of s under α. Example 1 Choose action in Act(s 1 ) {α, β} nondeterministically: say α 2 Race condition between αtransitions: 1 Mean delay: Probability to move to s 3: 1 R(s 1,α,s 2 )+R(s 1,α,s 3 ) 20 R(s 1,α,s 3 ) 1 E(s,α) 4 β, 0.1 s 0 β, 0.5 s 1 α, 5 α, 15 s 2 β, 0.5 β, 0.1 β, 1 s 3 Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
11 Introduction Preliminaries Transitions and paths Definition (Paths) 1 Finite paths of length n Æare denoted α 0,t 0 α 1,t 1 α 2,t 2 α n 1,t n 1 π s 0 s1 s2 sn Paths n is the set of paths of length n and π s n is the last state of π. 2 Paths ω is the corresponding set of infinite paths is the state occupied at time t on path π. Definition (Combined transition) A combined transition m (α, t, s ) α is the action (chosen externally), t is the transition s firing time and s the transition s successor state. Ω : Act Ê 0 S is the set of combined transitions. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
12 Introduction Preliminaries Transitions and paths Definition (Paths) 1 Finite paths of length n Æare denoted α 0,t 0 α 1,t 1 α 2,t 2 α n 1,t n 1 π s 0 s1 s2 sn Paths n is the set of paths of length n and π s n is the last state of π. 2 Paths ω is the corresponding set of infinite paths is the state occupied at time t on path π. Definition (Combined transition) A combined transition m (α, t, s ) α is the action (chosen externally), t is the transition s firing time and s the transition s successor state. Ω : Act Ê 0 S is the set of combined transitions. π π α, t s Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
13 Introduction Measure Space Construction Definition (Measurable spaces) Probability measures are defined on σfields 1 F of sets of combined transitions: Ω : Act Ê 0 S F : σ ( F Act B(Ê 0) F S ) 2 F Paths n of sets of paths of length n: B(Ê 0) : Borel σfield forê 0 F Paths n : σ ({S 0 M 1 M n S 0 F S, M i F}) 3 F Paths ω of sets of infinite paths: Cylinder set construction: Any C n F Paths n defines a cylinder base (of finite length) C n : {π Paths ω π[0..n] C n } is a cylinder (extension to infinity). The σfield F Paths ω is then F Paths ω : σ ( {C n C n F Paths n} ) n0 Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
14 Introduction Measure Space Construction Definition (Measurable spaces) Probability measures are defined on σfields 1 F of sets of combined transitions: Ω : Act Ê 0 S F : σ ( F Act B(Ê 0) F S ) 2 F Paths n of sets of paths of length n: B(Ê 0) : Borel σfield forê 0 F Paths n : σ ({S 0 M 1 M n S 0 F S, M i F}) 3 F Paths ω of sets of infinite paths: Cylinder set construction: Any C n F Paths n defines a cylinder base (of finite length) C n : {π Paths ω π[0..n] C n } is a cylinder (extension to infinity). The σfield F Paths ω is then F Paths ω : σ ( {C n C n F Paths n} ) n0 Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
15 Introduction Measure Space Construction Definition (Measurable spaces) Probability measures are defined on σfields 1 F of sets of combined transitions: Ω : Act Ê 0 S F : σ ( F Act B(Ê 0) F S ) 2 F Paths n of sets of paths of length n: B(Ê 0) : Borel σfield forê 0 F Paths n : σ ({S 0 M 1 M n S 0 F S, M i F}) 3 F Paths ω of sets of infinite paths: Cylinder set construction: Any C n F Paths n defines a cylinder base (of finite length) C n : {π Paths ω π[0..n] C n } is a cylinder (extension to infinity). The σfield F Paths ω is then F Paths ω : σ ( {C n C n F Paths n} ) Cn n0 Paths ω C n Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
16 Introduction Resolving Nondeterminism by Schedulers Defining probability measures Schedulers resolve nondeterministic choices between actions. Classes are 1 either deterministic or randomized and 2 depending on available history. Definition (Measurable scheduler) A measurable scheduler [WJ, 2006] is a mapping D : Paths F Act [0, 1] where: 1 D(π, ) Distr(Act(π )) for π Paths 2 D(, A) are measurable for A F Act. Example (Why such schedulers?) Stationary schedulers are not optimal: t t 0 : choose β to maximize probability t > t 0 : now start choosing α Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
17 Introduction Resolving Nondeterminism by Schedulers Defining probability measures Schedulers resolve nondeterministic choices between actions. Classes are 1 either deterministic or randomized and 2 depending on available history. Definition (Measurable scheduler) A measurable scheduler [WJ, 2006] is a mapping D : Paths F Act [0, 1] where: 1 D(π, ) Distr(Act(π )) for π Paths 2 D(, A) are measurable for A F Act. Example (Why such schedulers?) Stationary schedulers are not optimal: t t 0 : choose β to maximize probability t > t 0 : now start choosing α Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
18 Introduction Resolving Nondeterminism by Schedulers Defining probability measures Schedulers resolve nondeterministic choices between actions. Classes are 1 either deterministic or randomized and 2 depending on available history. τ, ε rep 1 Definition (Measurable scheduler) A measurable scheduler [WJ, 2006] is a mapping up τ, µ off rep 2 D : Paths F Act [0, 1] where: 1 D(π, ) Distr(Act(π )) for π Paths 2 D(, A) are measurable for A F Act. τ, ε α, 4λ τ, λ rep 1 Example (Why such schedulers?) Stationary schedulers are not optimal: t t 0 : choose β to maximize probability t > t 0 : now start choosing α t0 Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
19 Introduction Probability Measures Example (Onestep probabilities) The event to go from off to rep 1 in 2 time units: M {β} [0, 2] {rep 1 } F. Its probability µ D (π, M) depends on: 1 the probability D(π, {β}) of choosing β 2 the exp. distr. residence time in off : η E(off,β) (t) E(off, β) e E(off,β) t 2ε e 2ε t 3 the race between rep 1 and rep 2 : π rep 1 off α, 4λ rep 1 rep 2 P(off, β,rep 1 ) R(off, β,rep 1 ) E(off, β) ε 2ε 0.5. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
20 Introduction Probability Measures Example (Onestep probabilities) The event to go from off to rep 1 in 2 time units: M {β} [0, 2] {rep 1 } F. Its probability µ D (π, M) depends on: 1 the probability D(π, {β}) of choosing β 2 the exp. distr. residence time in off : η E(off,β) (t) E(off, β) e E(off,β) t 2ε e 2ε t 3 the race between rep 1 and rep 2 : π rep 1 off α, 4λ rep 1 rep 2 P(off, β,rep 1 ) R(off, β,rep 1 ) E(off, β) ε 2ε 0.5. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
21 Introduction Probability Measures Example (Onestep probabilities) The event to go from off to rep 1 in 2 time units: M {β} [0, 2] {rep 1 } F. Its probability µ D (π, M) depends on: 1 the probability D(π, {β}) of choosing β 2 the exp. distr. residence time in off : η E(off,β) (t) E(off, β) e E(off,β) t 2ε e 2ε t 3 the race between rep 1 and rep 2 : π rep 1 off α, 4λ rep 1 rep 2 P(off, β,rep 1 ) R(off, β,rep 1 ) E(off, β) ε 2ε 0.5. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
22 Introduction Probability Measures Example (Onestep probabilities) The event to go from off to rep 1 in 2 time units: M {β} [0, 2] {rep 1 } F. Its probability µ D (π, M) depends on: 1 the probability D(π, {β}) of choosing β 2 the exp. distr. residence time in off : η E(off,β) (t) E(off, β) e E(off,β) t 2ε e 2ε t 3 the race between rep 1 and rep 2 : P(off, β,rep 1 ) R(off, β,rep 1 ) E(off, β) ε 2ε 0.5. π rep 1 off α, 4λ rep 1 rep 2 Definition (Measuring sets of combined transitions) For history π Paths and D THR define probability measure µ D (π, ) on F: µ D (π, M) : D(π, dα) η E(π,α) (dt) I M (α, t, s) P(π, α, ds). Act Ê 0 S Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
23 Introduction Definition (Measuring sets of paths) Probability Measures 1 Initial distribution ν: The probability to start in state s is ν({s}) ν(s). on sets of finite paths: Let ν Distr(S) and D THR. Define inductively: 2 Pr n ν,d 3 Pr ω ν,d Pr 0 ν,d(π) : s Π ν(s) Pr n+1 ν,d Paths (Π) : Pr n ν,d (dπ) I Π (π m) µ D (π, dm) n Ω on sets of infinite paths: A cylinder base is a measurable set C n F Paths n C n defines cylinder C n {π Paths ω π[0..n] C n } The probability of cylinder C n is that of its base C n : Pr ω ν,d(c n) Pr n ν,d(c n ). This extends to F Paths ω by IonescuTulcea. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
24 Introduction Definition (Measuring sets of paths) Probability Measures 1 Initial distribution ν: The probability to start in state s is ν({s}) ν(s). on sets of finite paths: Let ν Distr(S) and D THR. Define inductively: 2 Pr n ν,d 3 Pr ω ν,d Pr 0 ν,d(π) : s Π ν(s) Pr n+1 ν,d Paths (Π) : Pr n ν,d (dπ) I Π (π m) µ D (π, dm) n Ω on sets of infinite paths: A cylinder base is a measurable set C n F Paths n C n defines cylinder C n {π Paths ω π[0..n] C n } The probability of cylinder C n is that of its base C n : Pr ω ν,d(c n) Pr n ν,d(c n ). This extends to F Paths ω by IonescuTulcea. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
25 Introduction Definition (Measuring sets of paths) Probability Measures 1 Initial distribution ν: The probability to start in state s is ν({s}) ν(s). on sets of finite paths: Let ν Distr(S) and D THR. Define inductively: 2 Pr n ν,d 3 Pr ω ν,d Pr 0 ν,d(π) : s Π ν(s) Pr n+1 ν,d Paths (Π) : Pr n ν,d (dπ) I Π (π m) µ D (π, dm) n Ω on sets of infinite paths: A cylinder base is a measurable set C n F Paths n C n defines cylinder C n {π Paths ω π[0..n] C n } The probability of cylinder C n is that of its base C n : Pr ω ν,d(c n) Pr n ν,d(c n ). This extends to F Paths ω by IonescuTulcea. C n ω Paths C n Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
26 Strong Bisimulation on CTMDPs Definition (Strong bisimulation relation) Equivalence R S S is a strong bisimulation relation iff for all (u, v) R and all α Act: 1 L(u) L(v) and 2 C S R. R(u, α, C) R(v, α, C). R(u, α, C) : P u C R(u, α, u ) u, v are strongly bisimilar (u v) iff str. bisim. relation R with (u, v) R. Definition (Quotient under ) For C (S, Act,R, AP, L), its quotient under is C : ( S, Act, R, AP, L): L ([s]) : L(s) S : [s] s S R([s], α, C) : R(s, α, C) Example Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
27 Strong Bisimulation on CTMDPs Definition (Strong bisimulation relation) Equivalence R S S is a strong bisimulation relation iff for all (u, v) R and all α Act: 1 L(u) L(v) and 2 C S R. R(u, α, C) R(v, α, C). R(u, α, C) : P u C R(u, α, u ) u, v are strongly bisimilar (u v) iff str. bisim. relation R with (u, v) R. Definition (Quotient under ) For C (S, Act,R, AP, L), its quotient under is C : ( S, Act, R, AP, L): L ([s]) : L(s) S : [s] s S R([s], α, C) : R(s, α, C) Example Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
28 Strong Bisimulation on CTMDPs Definition (Strong bisimulation relation) Equivalence R S S is a strong bisimulation relation iff for all (u, v) R and all α Act: 1 L(u) L(v) and 2 C S R. R(u, α, C) R(v, α, C). R(u, α, C) : P u C R(u, α, u ) u, v are strongly bisimilar (u v) iff str. bisim. relation R with (u, v) R. Definition (Quotient under ) For C (S, Act,R, AP, L), its quotient under is C : ( S, Act, R, AP, L): L ([s]) : L(s) S : [s] s S R([s], α, C) : R(s, α, C) Example τ, ε r1 τ, ε [r1] α, 4λ up τ, µ off r2 [up] τ, µ [off] [r2] α, 4λ τ, λ τ, λ τ, ε r 1 Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
29 A Logic for CTMDPs Continuous Stochastic Logic [Aziz et al. 2000, Baier et al. 2003] Example (Transient state formula) In state off, the probability to reach the up state within 20 time units exceeds 0.5 under any scheduling decision: Φ off >0.5 [0,20] up ε rep 1 up µ off rep 2 Example (Longrun average [de Alfaro, LICS 98]) For any scheduler, the system on average is not operational for less than 1% of its execution time: ε α, 4λ rep 1 λ Ψ L <0.01 up Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
30 A Logic for CTMDPs Syntax and Semantics of CSL Definition (Syntax of CSL) For a AP, p [0, 1], I Ê 0 a nonempty interval and {<,,, >}, state formulas and path formulas are: Φ :: a Φ Φ Φ p ϕ L p Φ ϕ :: X I Φ ΦU I Φ. Definition (Semantics of path formulas) α 0,t 0 α 1,t 1 For CTMDP C and infinite path π s 0 s1 define: π X I Φ π[1] Φ t 0 I π ΦU I Ψ t I. Ψ ( t [0, t). Φ)). Example Let ϕ ΦU [1,2] Ψ and π Paths ω as follows: Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
31 A Logic for CTMDPs Syntax and Semantics of CSL Definition (Syntax of CSL) For a AP, p [0, 1], I Ê 0 a nonempty interval and {<,,, >}, state formulas and path formulas are: Φ :: a Φ Φ Φ p ϕ L p Φ ϕ :: X I Φ ΦU I Φ. Definition (Semantics of path formulas) α 0,t 0 α 1,t 1 For CTMDP C and infinite path π s 0 s1 define: π X I Φ π[1] Φ t 0 I π ΦU I Ψ t I. Ψ ( t [0, t). Φ)). Example Let ϕ ΦU [1,2] Ψ and π Paths ω as follows: Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
32 A Logic for CTMDPs Syntax and Semantics of CSL Definition (Syntax of CSL) For a AP, p [0, 1], I Ê 0 a nonempty interval and {<,,, >}, state formulas and path formulas are: Φ :: a Φ Φ Φ p ϕ L p Φ ϕ :: X I Φ ΦU I Φ. Definition (Semantics of path formulas) α 0,t 0 α 1,t 1 For CTMDP C and infinite path π s 0 s1 define: π X I Φ π[1] Φ t 0 I π ΦU I Ψ t I. Ψ ( t [0, t). Φ)). Example Let ϕ ΦU [1,2] Ψ and π Paths ω as follows: π s Φ Ψ s Φ Ψ s Φ Ψ s Φ Ψ s Ψ s (Φ Ψ) Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
33 A Logic for CTMDPs Syntax and Semantics of CSL Average residence time For state formula Φ, path π s 0 α 0,t 0 s1 α 1,t 1 avg(φ, t, π) : 1 t t 0 I Sat(Φ) ) dt. s2 and time point t: I Sat(Φ) (s) : Example The average time spent in Sat(Φ)states up to time t 8: ( 1 if s Sat(Φ) 0 otherwise avg(φ, 8, π) Definition (Semantics of state formulas) Let p [0, 1], {, <,, >} and Φ, ϕ state and path formulas: s L p Φ D THR. lim avg(φ, t, π) Pr t Paths ω ν s,d(dπ) p ω s p ϕ D THR. Pr ω ν s,d {π Paths ω π ϕ} p Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
34 A Logic for CTMDPs Syntax and Semantics of CSL Average residence time For state formula Φ, path π s 0 α 0,t 0 s1 α 1,t 1 avg(φ, t, π) : 1 t t 0 I Sat(Φ) ) dt. s2 and time point t: I Sat(Φ) (s) : Example The average time spent in Sat(Φ)states up to time t 8: π t s Φ s Φ s Φ s Φ s Φ s Φ Definition (Semantics of state formulas) Let p [0, 1], {, <,, >} and Φ, ϕ state and path formulas: ( 1 if s Sat(Φ) 0 otherwise avg(φ, 8, π) s L p Φ D THR. lim avg(φ, t, π) Pr t Paths ω ν s,d(dπ) p ω s p ϕ D THR. Pr ω ν s,d {π Paths ω π ϕ} p Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
35 A Logic for CTMDPs Syntax and Semantics of CSL Average residence time For state formula Φ, path π s 0 α 0,t 0 s1 α 1,t 1 avg(φ, t, π) : 1 t t 0 I Sat(Φ) ) dt. s2 and time point t: I Sat(Φ) (s) : Example The average time spent in Sat(Φ)states up to time t 8: π t s Φ s Φ s Φ s Φ s Φ s Φ Definition (Semantics of state formulas) Let p [0, 1], {, <,, >} and Φ, ϕ state and path formulas: ( 1 if s Sat(Φ) 0 otherwise avg(φ, 8, π) s L p Φ D THR. lim avg(φ, t, π) Pr t Paths ω ν s,d(dπ) p ω s p ϕ D THR. Pr ω ν s,d {π Paths ω π ϕ} p Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
36 Preservation of CSL properties Preservation Theorem Strong bisimilarity preserves CSL properties Theorem Let (S, Act,R, AP, L) be a CTMDP. For all states u, v S it holds: Proof by structural induction: 1 a,, omitted u v Φ CSL. (u Φ v Φ). 2 p ϕ sketch in this talk 3 L p ϕ straightforward, relies on p ϕ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
37 Preservation of CSL properties Preservation Theorem The reverse conjecture does not hold: Counterexample [Segala et al., Nordic J. of Comp. 1995, Baier 1998] u 1 v 1 α, 6 α, 6 β, 4 s δ, 3 δ, 9 β, 8 u 3 v 3 u 1 v 1 α, 6 α, 6 s δ, 3 δ, 9 u 3 v 3 u 2 v 2 1 s s as R(s, β, [u 2 ]) 4 whereas R(s, β, ) 0. 2 For all Φ it holds: s Φ s Φ : CSL can only express extreme probability bounds. The choice of action β does not contribute an infimum or supremum. Some examples: Φ 1 3 X [0, ) yellow: choose δ Φ 1 3 X [0, ) yellow: choose α p ϕ p ϕ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
38 Preservation of CSL properties Preservation Theorem The reverse conjecture does not hold: Counterexample [Segala et al., Nordic J. of Comp. 1995, Baier 1998] u 1 v 1 α, 6 α, 6 β, 4 s δ, 3 δ, 9 β, 8 u 3 v 3 u 1 v 1 α, 6 α, 6 s δ, 3 δ, 9 u 3 v 3 u 2 v 2 1 s s as R(s, β, [u 2 ]) 4 whereas R(s, β, ) 0. 2 For all Φ it holds: s Φ s Φ : CSL can only express extreme probability bounds. The choice of action β does not contribute an infimum or supremum. Some examples: Φ 1 3 X [0, ) yellow: choose δ Φ 1 3 X [0, ) yellow: choose α p ϕ p ϕ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
39 Preservation of CSL properties Preservation Theorem The reverse conjecture does not hold: Counterexample [Segala et al., Nordic J. of Comp. 1995, Baier 1998] u 1 v 1 α, 6 α, 6 β, 4 s δ, 3 δ, 9 β, 8 u 3 v 3 u 1 v 1 α, 6 α, 6 s δ, 3 δ, 9 u 3 v 3 u 2 v 2 1 s s as R(s, β, [u 2 ]) 4 whereas R(s, β, ) 0. 2 For all Φ it holds: s Φ s Φ : CSL can only express extreme probability bounds. The choice of action β does not contribute an infimum or supremum. Some examples: Φ 1 3 X [0, ) yellow: choose δ Φ 1 3 X [0, ) yellow: choose α p ϕ p ϕ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
40 Preservation of CSL properties Proof sketch Preservation of CSL formulas Proof idea for transient state formulas Assume u v and u p ϕ. To prove: v p ϕ, i.e. V THR. Pr ω ν v,v{ π Paths ω π ϕ } p. (1) Let V THR be a scheduler (w.r.t. ν v ). From V, construct U (w.r.t. ν u ) such that Pr ω ν u,u{ π Paths ω π ϕ } Pr ω ν v,v{ π Paths ω π ϕ }. (2) Since u p ϕ, we obtain Pr ω ν v,v{ π Paths ω π ϕ } p. How to prove equation (2) For a specific subclass of sets of paths: lift the argument to the quotient space Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
41 Preservation of CSL properties Proof sketch Preservation of CSL formulas Proof idea for transient state formulas Assume u v and u p ϕ. To prove: v p ϕ, i.e. V THR. Pr ω ν v,v{ π Paths ω π ϕ } p. (1) Let V THR be a scheduler (w.r.t. ν v ). From V, construct U (w.r.t. ν u ) such that Pr ω ν u,u{ π Paths ω π ϕ } Pr ω ν v,v{ π Paths ω π ϕ }. (2) Since u p ϕ, we obtain Pr ω ν v,v{ π Paths ω π ϕ } p. How to prove equation (2) For a specific subclass of sets of paths: lift the argument to the quotient space Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
42 Preservation of CSL properties Proof sketch Preservation of CSL formulas Proof idea for transient state formulas Assume u v and u p ϕ. To prove: v p ϕ, i.e. V THR. Pr ω ν v,v{ π Paths ω π ϕ } p. (1) Let V THR be a scheduler (w.r.t. ν v ). From V, construct U (w.r.t. ν u ) such that Pr ω ν u,u{ π Paths ω π ϕ } Pr ω ν v,v{ π Paths ω π ϕ }. (2) Since u p ϕ, we obtain Pr ω ν v,v{ π Paths ω π ϕ } p. How to prove equation (2) For a specific subclass of sets of paths: lift the argument to the quotient space Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
43 Preservation of CSL properties Proof sketch Preservation of CSL formulas Proof idea for transient state formulas Assume u v and u p ϕ. To prove: v p ϕ, i.e. V THR. Pr ω ν v,v{ π Paths ω π ϕ } p. (1) Let V THR be a scheduler (w.r.t. ν v ). From V, construct U (w.r.t. ν u ) such that Pr ω ν u,u{ π Paths ω π ϕ } Pr ω ν v,v{ π Paths ω π ϕ }. (2) Since u p ϕ, we obtain Pr ω ν v,v{ π Paths ω π ϕ } p. How to prove equation (2) For a specific subclass of sets of paths: lift the argument to the quotient space Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
44 Preservation of CSL properties Proof sketch Preservation of CSL formulas Proof idea for transient state formulas Assume u v and u p ϕ. To prove: v p ϕ, i.e. V THR. Pr ω ν v,v{ π Paths ω π ϕ } p. (1) Let V THR be a scheduler (w.r.t. ν v ). From V, construct U (w.r.t. ν u ) such that Pr ω ν u,u{ π Paths ω π ϕ } Pr ω ν v,v{ π Paths ω π ϕ }. (2) Since u p ϕ, we obtain Pr ω ν v,v{ π Paths ω π ϕ } p. How to prove equation (2) For a specific subclass of sets of paths: lift the argument to the quotient space C : ν v, V C : ν v, V νv! defines prove ν u, U ν u, U νu Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
45 Preliminaries Preservation of CSL properties Quotient scheduler Definition (Simple bisimulation closed sets of paths) A measurable set of paths of the form Π [s 0 ] A 0 T 0 [s 1 ] A n 1 T n 1 [s n ] is called simple bisimulation closed. Π corresponds to the set Π on C: Π {[s 0 ]} A 0 T 0 {[s 1 ]} A n 1 T n 1 {[s n ]} Preliminary goal For initial distribution ν Distr(S) and scheduler D THR: Provide a measure preserving scheduler D ν on C such that Pr ω ) ν,d( Π Pr ω ν,d ν ( Π) for simple bisimulation closed sets of paths Π. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
46 Preliminaries Preservation of CSL properties Quotient scheduler Definition (Simple bisimulation closed sets of paths) A measurable set of paths of the form Π [s 0 ] A 0 T 0 [s 1 ] A n 1 T n 1 [s n ] is called simple bisimulation closed. Π corresponds to the set Π on C: Π {[s 0 ]} A 0 T 0 {[s 1 ]} A n 1 T n 1 {[s n ]} Preliminary goal For initial distribution ν Distr(S) and scheduler D THR: Provide a measure preserving scheduler D ν on C such that Pr ω ) ν,d( Π Pr ω ν,d ν ( Π) for simple bisimulation closed sets of paths Π. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
47 Example Preservation of CSL properties Quotient scheduler 1 4 α, s 0 s 1 β, 0.5 α, 0.5 α, 1.0 s 2 s 3 α, α, 0.5 α, 2.0 β, 0.5 Let the first decision of D be as follows: [s 0 ] α, 3.0 α, 0.5 β, 0.5 α, 2.0 [s 2 ] [s 3 ] 1 12 α, 1.0 α, 1.0 Intuitively, the quotient scheduler D ν then decides in [s 0 ] as follows: P D ν ([s s [s 0], {α}) 0 ] ν(s) D(s, {α}) 1 4 Ps [s 0 ] ν(s) P D([s ν s [s 0 ], {β}) 0 ] ν(s) D(s, {β}) Ps [s 0 ] ν(s) Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
48 Example Preservation of CSL properties Quotient scheduler 1 4 α, s 0 s 1 β, 0.5 α, 0.5 α, 1.0 s 2 s 3 α, α, 0.5 α, 2.0 β, 0.5 Let the first decision of D be as follows: [s 0 ] α, 3.0 α, 0.5 β, 0.5 α, 2.0 [s 2 ] [s 3 ] 1 12 α, 1.0 α, 1.0 D(s 0, {α}) 2 3 D(s 0, {β}) 1 3 D(s 1, {α}) 1 4 D(s 1, {β}) 3 4 Intuitively, the quotient scheduler D ν then decides in [s 0 ] as follows: P D ν ([s s [s 0], {α}) 0 ] ν(s) D(s, {α}) 1 4 Ps [s 0 ] ν(s) P D([s ν s [s 0 ], {β}) 0 ] ν(s) D(s, {β}) Ps [s 0 ] ν(s) Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
49 Example Preservation of CSL properties Quotient scheduler 1 4 α, s 0 s 1 β, 0.5 α, 0.5 α, 1.0 s 2 s 3 α, α, 0.5 α, 2.0 β, 0.5 Let the first decision of D be as follows: [s 0 ] α, 3.0 α, 0.5 β, 0.5 α, 2.0 [s 2 ] [s 3 ] 1 12 α, 1.0 α, 1.0 D(s 0, {α}) 2 3 D(s 0, {β}) 1 3 D(s 1, {α}) 1 4 D(s 1, {β}) 3 4 Intuitively, the quotient scheduler D ν then decides in [s 0 ] as follows: P D ν ([s s [s 0], {α}) 0 ] ν(s) D(s, {α}) 1 4 Ps [s 0 ] ν(s) P D([s ν s [s 0 ], {β}) 0 ] ν(s) D(s, {β}) Ps [s 0 ] ν(s) Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
50 Quotient scheduler Preservation of CSL properties Quotient scheduler Definition (History weight) Given CTMDP C, ν Distr(S) and D THR. Define the weight of history π Paths inductively: hw 0 (ν, D, π) : ν(π) if π Paths 0 S and hw n+1 (ν, D, π αn,tn s n+1 ) : hw n (ν, D, π) D(π, {α n }) P(π, α n, s n+1 ). Definition (Quotient scheduler) For any history π [s 0 ] α0,t0 [s 1 ] α1,t1 αn 1,tn 1 [s n ], the quotient scheduler D ν is defined by: D( π, ν {αn } ) π Π : hw n(ν, D, π) D(π, {α n }) π Π hw n(ν, D, π) where Π [s 0 ] {α 0 } {t 0 } {α n 1 } {t n 1 } [s n ]. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
51 Quotient scheduler Preservation of CSL properties Quotient scheduler Definition (History weight) Given CTMDP C, ν Distr(S) and D THR. Define the weight of history π Paths inductively: hw 0 (ν, D, π) : ν(π) if π Paths 0 S and hw n+1 (ν, D, π αn,tn s n+1 ) : hw n (ν, D, π) D(π, {α n }) P(π, α n, s n+1 ). Definition (Quotient scheduler) For any history π [s 0 ] α0,t0 [s 1 ] α1,t1 αn 1,tn 1 [s n ], the quotient scheduler D ν is defined by: D( π, ν {αn } ) π Π : hw n(ν, D, π) D(π, {α n }) π Π hw n(ν, D, π) where Π [s 0 ] {α 0 } {t 0 } {α n 1 } {t n 1 } [s n ]. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
52 Proof sketch I Preservation of CSL properties Proof Details Lemma 1 For simple bisimulation closed sets of paths Π: Pr ω ν,d (Π) Prω ν,d ν ( Π). 2 For any path formula ϕ there exists a family {Π k } k Æsuch that nπ Paths ω π ϕ o and Π k is simple bisimulation closed. What have we gained? For any Π k {π Paths ω π ϕ}: Pr ω ν,d(π k ) Pr ω ν,d ν ( Πk ). ] Π k Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
53 Proof sketch I Preservation of CSL properties Proof Details Lemma 1 For simple bisimulation closed sets of paths Π: Pr ω ν,d (Π) Prω ν,d ν ( Π). 2 For any path formula ϕ there exists a family {Π k } k Æsuch that nπ Paths ω π ϕ o and Π k is simple bisimulation closed. ] Π k What have we gained? For any Π k {π Paths ω π ϕ}: C : ν v, V! defines ν u, U Pr ω ν,d(π k ) Pr ω ν,d ν ( Πk ). C : ν v, V νv prove ν u, U νu Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
54 Proof sketch II Preservation of CSL properties Proof Details Sketch of the proof Let Π { π Paths ω π ϕ }. To show: Pr ω ν u,u(π) Pr ω ν v,v(π): 1 Define scheduler U to mimic V νv on the quotient: a U s 0,t 0 0 an 1,t n 1 s n : V νv a [s 0 ] 0,t 0 2 Then U νu Now we obtain the proof: Pr ω ν v,v ( Π ) Pr ω ν u,u νu V νv and ν u ν v : P U νu ( π, αn) Pr ω ν v,v ( Πk ) π Π hwn(νu, U, π) Vνv ` π, αn P π Π hwn(νu, U, π) Pr ω ν ) ( Πk v,v νv ) ( Πk Pr ω ) ) ν u,u( Πk Pr ω νu,u( Π. a n 1,t n 1 [s n]. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
55 Proof sketch II Preservation of CSL properties Proof Details Sketch of the proof Let Π { π Paths ω π ϕ }. To show: Pr ω ν u,u(π) Pr ω ν v,v(π): 1 Define scheduler U to mimic V νv on the quotient: a U s 0,t 0 0 an 1,t n 1 s n : V νv a [s 0 ] 0,t 0 2 Then U νu Now we obtain the proof: Pr ω ν v,v ( Π ) Pr ω ν u,u νu V νv and ν u ν v : P U νu ( π, αn) Pr ω ν v,v ( Πk ) π Π hwn(νu, U, π) Vνv ` π, αn P π Π hwn(νu, U, π) Pr ω ν ) ( Πk v,v νv ) ( Πk Pr ω ) ) ν u,u( Πk Pr ω νu,u( Π. a n 1,t n 1 [s n]. Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
56 Proof sketch II Preservation of CSL properties Proof Details Sketch of the proof Let Π { π Paths ω π ϕ }. To show: Pr ω ν u,u(π) Pr ω ν v,v(π): 1 Define scheduler U to mimic V νv on the quotient: a U s 0,t 0 0 an 1,t n 1 s n : V νv a [s 0 ] 0,t 0 2 Then U νu Now we obtain the proof: V νv and ν u ν v : P U νu ( π, αn) π Π hwn(νu, U, π) Vνv ` π, αn P π Π hwn(νu, U, π) a n 1,t n 1 [s n]. Pr ω ν v,v ( Π ) Pr ω ν u,u νu Pr ω ν v,v ( Πk ) Pr ω ν ) ( Πk v,v νv ) ( Πk Pr ω ) ) ν u,u( Πk Pr ω νu,u( Π. C : C : ν v, V ν v, V νv! ν u, U ν u, U νu Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
57 Proof sketch II Preservation of CSL properties Proof Details Sketch of the proof Let Π { π Paths ω π ϕ }. To show: Pr ω ν u,u(π) Pr ω ν v,v(π): 1 Define scheduler U to mimic V νv on the quotient: a U s 0,t 0 0 an 1,t n 1 s n : V νv a [s 0 ] 0,t 0 2 Then U νu Now we obtain the proof: V νv and ν u ν v : P U νu ( π, αn) π Π hwn(νu, U, π) Vνv ` π, αn P π Π hwn(νu, U, π) a n 1,t n 1 [s n]. Pr ω ν v,v ( Π ) Pr ω ν u,u νu Pr ω ν v,v ( Πk ) Pr ω ν ) ( Πk v,v νv ) ( Πk Pr ω ) ) ν u,u( Πk Pr ω νu,u( Π. C : C : ν v, V ν v, V νv! ν u, U ν u, U νu Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
58 Proof sketch II Preservation of CSL properties Proof Details Sketch of the proof Let Π { π Paths ω π ϕ }. To show: Pr ω ν u,u(π) Pr ω ν v,v(π): 1 Define scheduler U to mimic V νv on the quotient: a U s 0,t 0 0 an 1,t n 1 s n : V νv a [s 0 ] 0,t 0 2 Then U νu Now we obtain the proof: V νv and ν u ν v : P U νu ( π, αn) π Π hwn(νu, U, π) Vνv ` π, αn P π Π hwn(νu, U, π) a n 1,t n 1 [s n]. Pr ω ν v,v ( Π ) Pr ω ν u,u νu Pr ω ν v,v ( Πk ) Pr ω ν ) ( Πk v,v νv ) ( Πk Pr ω ) ) ν u,u( Πk Pr ω νu,u( Π. C : C : ν v, V ν v, V νv! ν u, U ν u, U νu Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
59 Proof sketch II Preservation of CSL properties Proof Details Sketch of the proof Let Π { π Paths ω π ϕ }. To show: Pr ω ν u,u(π) Pr ω ν v,v(π): 1 Define scheduler U to mimic V νv on the quotient: a U s 0,t 0 0 an 1,t n 1 s n : V νv a [s 0 ] 0,t 0 2 Then U νu Now we obtain the proof: V νv and ν u ν v : P U νu ( π, αn) π Π hwn(νu, U, π) Vνv ` π, αn P π Π hwn(νu, U, π) a n 1,t n 1 [s n]. Pr ω ν v,v ( Π ) Pr ω ν u,u νu Pr ω ν v,v ( Πk ) Pr ω ν ) ( Πk v,v νv ) ( Πk Pr ω ) ) ν u,u( Πk Pr ω νu,u( Π. C : C : ν v, V ν v, V νv! ν u, U ν u, U νu Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
60 Proof sketch II Preservation of CSL properties Proof Details Sketch of the proof Let Π { π Paths ω π ϕ }. To show: Pr ω ν u,u(π) Pr ω ν v,v(π): 1 Define scheduler U to mimic V νv on the quotient: a U s 0,t 0 0 an 1,t n 1 s n : V νv a [s 0 ] 0,t 0 2 Then U νu Now we obtain the proof: V νv and ν u ν v : P U νu ( π, αn) π Π hwn(νu, U, π) Vνv ` π, αn P π Π hwn(νu, U, π) a n 1,t n 1 [s n]. Pr ω ν v,v ( Π ) Pr ω ν u,u νu Pr ω ν v,v ( Πk ) Pr ω ν ) ( Πk v,v νv ) ( Πk Pr ω ) ) ν u,u( Πk Pr ω νu,u( Π. C : C : ν v, V ν v, V νv! ν u, U ν u, U νu Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
61 Proof sketch II Preservation of CSL properties Proof Details Sketch of the proof Let Π { π Paths ω π ϕ }. To show: Pr ω ν u,u(π) Pr ω ν v,v(π): 1 Define scheduler U to mimic V νv on the quotient: a U s 0,t 0 0 an 1,t n 1 s n : V νv a [s 0 ] 0,t 0 2 Then U νu Now we obtain the proof: V νv and ν u ν v : P U νu ( π, αn) π Π hwn(νu, U, π) Vνv ` π, αn P π Π hwn(νu, U, π) a n 1,t n 1 [s n]. Pr ω ν v,v ( Π ) Pr ω ν u,u νu Pr ω ν v,v ( Πk ) Pr ω ν ) ( Πk v,v νv ) ( Πk Pr ω ) ) ν u,u( Πk Pr ω νu,u( Π. C : C : ν v, V ν v, V νv! ν u, U ν u, U νu Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
62 Proof sketch II Preservation of CSL properties Proof Details Sketch of the proof Let Π { π Paths ω π ϕ }. To show: Pr ω ν u,u(π) Pr ω ν v,v(π): 1 Define scheduler U to mimic V νv on the quotient: a U s 0,t 0 0 an 1,t n 1 s n : V νv a [s 0 ] 0,t 0 2 Then U νu Now we obtain the proof: V νv and ν u ν v : P U νu ( π, αn) π Π hwn(νu, U, π) Vνv ` π, αn P π Π hwn(νu, U, π) a n 1,t n 1 [s n]. Pr ω ν v,v ( Π ) Pr ω ν u,u νu Pr ω ν v,v ( Πk ) Pr ω ν ) ( Πk v,v νv ) ( Πk Pr ω ) ) ν u,u( Πk Pr ω νu,u( Π. C : C : ν v, V ν v, V νv! ν u, U ν u, U νu Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
63 Summary Related Work Related Work Measure theoretic basis of THR schedulers [Wolovick et al., FORMATS 06] Probabilistic branching time logics [Baier et al., Distr. Comp. 98] Long run average behaviour [de Alfaro, LICS 1998] Timebounded reachability in uniform CTMDPs [Baier et al., TCS 05] Model Checking of prob. and nondet. systems [Bianco et al., FSTTCS 95] Abstraction for continuoustime Markov chains [Katoen et al., CAV 07] Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
64 Summary Summary Summary What did we do? 1 provided the measuretheoretic foundations of CSL on CTMDPs, 2 defined strong bisimulation on CTMDPs and 3 proved the preservation of CSL under strong bisimilarity Example (Model minimization) Bisimulation minimization preserves transient and steadystate measures: Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
65 Summary Summary Summary What did we do? 1 provided the measuretheoretic foundations of CSL on CTMDPs, 2 defined strong bisimulation on CTMDPs and 3 proved the preservation of CSL under strong bisimilarity Example (Model minimization) Bisimulation minimization preserves transient and steadystate measures: τ, ε r 1 τ, ε [r 1 ] α, 4λ up τ, µ off r 2 [up] τ, µ [off] [r 2 ] τ, ε α, 4λ τ, λ r 1 τ, λ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
66 Summary Summary Summary What did we do? 1 provided the measuretheoretic foundations of CSL on CTMDPs, 2 defined strong bisimulation on CTMDPs and 3 proved the preservation of CSL under strong bisimilarity Example (Model minimization) Bisimulation minimization preserves transient and steadystate measures: τ, ε r 1 τ, ε [r 1 ] α, 4λ up τ, µ off r 2 [up] τ, µ [off] [r 2 ] τ, ε α, 4λ τ, λ r 1 Thank you for your attention! τ, λ Neuhäußer, Katoen (RWTH Aachen) Bisimulation and Logical Preservation for CTMDPs CONCUR / 23
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