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Process Algebra (2IF45) Analysing Probabilistic systems Dr. Suzana Andova
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1 Probabilistic LTS Process Algebra (2IF45) Basic ingredients of a PLTS: states non-detereministic states set N probabilistic states set P transitions action transitions labelled with actions and t P probabilistic transitions labelled with probabilities and t N For a probabilistic state s, = 1 s ts t s ts t a s ts t
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2 Process Algebra (2IF45) Composing PLTSs 1/2 a b + = 1/3 c d 2/3 1/3 ab 1/6 a 1/3 d c c d b
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3 Strong Probabilistic bisimulation on PLTSs Process Algebra (2IF45) 1 2/3 b 3 2 5 1/3 a 9 c 1 10 c c b 7 6 11 1/3 12 c c a 8 1 13 c b 1 c 2/3 4 1 b 1 a 1 c 1
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4 Process Algebra (2IF45) 1.A chatting philosopher is a person dedicated to two activities: thinking and chatting. A philosopher uses his phone for chatting. He can decide to pick up the phone with probability pi, or stay thinking with probability 1-pi. Once he starts chatting, he end the call with probability ro, or keep chatting with probability 1-ro. 2.There is a switch which allocates connection to a philosopher, and also deallocating a connection. Our switcher is capable of handling only one connection at time. 3.We consider a system of two philosophers and one switcher 4.First, we compute Phil 1 || Phil 2, where Phil i = Think i Chatting Philosophers example (partially)
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5 Analysing PLTSs – main ingredients Process Algebra (2IF45) The set of all paths in x starting in p?! What can we measure on x? Do we need schedulers for it? n p ks 0 x
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6 Example 1 (cont.) Process Algebra (2IF45) Property1: A path has a trace c*a Property2: A path has a trace c*b Property3: A path has a trace (cc)*a Property4: A path has a trace (cc)*b Property5: A path reaches a deadlock state
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7 Example 1 (cont.) Process Algebra (2IF45) Property1: A path has a trace c*a n p ks 0 x p kss 0 p 1/3 1/2 1/6 a b c kss 0 p 1/2 1/6 a b c 1/3......
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8 Example 1 (cont.) Process Algebra (2IF45) Property1: A path has a trace c*a n p ks 0 x p kss 0 p 1/3 1/2 1/6 a b c kss 0 p 1/2 1/6 a b c 1/3...... Prob(SetPaths1) = ?
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9 Example 1 (cont.) Process Algebra (2IF45) Property1: A path has a trace c*a n p ks 0 x p kss 0 p 1/3 1/2 1/6 a b c kss 0 p 1/2 1/6 a b c 1/3...... Prob(SetPaths1) = 1/3 + 1/6x1/3 + (1/6)^2x1/3 + …. = k 0 1/3x(1/6)^k = (1/3)/ (1-1/6) = 2/5
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10 Example 1 (cont.) Process Algebra (2IF45) Property2: A path has a trace c*b n p ks 0 x p kss 0 p 1/3 1/2 1/6 a b c kss 0 p 1/2 1/6 a b c 1/3......
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11 Example 1 (cont.) Process Algebra (2IF45) Property2: A path has a trace c*b n p ks 0 x p kss 0 p 1/3 1/2 1/6 a b c kss 0 p 1/2 1/6 a b c 1/3...... Prob(SetPaths2) = ?
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12 Example 1 (cont.) Process Algebra (2IF45) Property3: A path has a trace (cc)*a or (cc)*b n p ks 0 x p kss 0 p 1/3 1/2 1/6 a b c kss 0 p 1/2 1/6 a b c 1/3...... kss 0 p 1/2 1/6 a b c
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13 Example 1 (cont.) Process Algebra (2IF45) Property3: A path has a trace (cc)*a or (cc)*b n p ks 0 x p kss 0 p 1/3 1/2 1/6 a b c kss 0 p 1/2 1/6 a b c 1/3...... kss 0 p 1/2 1/6 a b c Prob(SetPaths3) = ?
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14 Example 2 Process Algebra (2IF45) The set of all paths in y starting in p? What can we measure on y? Do we need schedulers for it? n p ks 0 y k p kss 0 p 1/3 1/2 1/6 a b c kss 0 p 1/2 1/6 a b c 1/3...... b b
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15 Example 2 (cont.) Process Algebra (2IF45) Property1: A path has a trace c*a? First select a scheduler, then compute this set, and its probability 1.1. Scheduler 1.2. Scheduler 1 1.3. Scheduler 2
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16 Example 2 (cont.) – scheduler Process Algebra (2IF45) Computation tree CT y (p, ) n p ks 0 y k p ksn 0 p 1/3 1/2 1/6 a c ksn 0 p 1/2 1/6 b c 1/3......
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17 Example 2 (cont.) – scheduler Process Algebra (2IF45) Prob (p, trace = c*a) = 12/35 How? n p ks 0 y k p ksn 0 p 1/3 1/2 1/6 a c ksn 0 p 1/2 1/6 b c 1/3.... Property1: A path has a trace c*a
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18 Example 2 (cont.) – scheduler 1 Process Algebra (2IF45) n p ks 0 y k p kss 0 p 1/3 1/2 1/6 a c kss 0 p 1/2 1/6 a b c 1/3...... b Computation tree CT y (p, 1 )
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19 Example 2 (cont.) – scheduler 1 Process Algebra (2IF45) n p ks 0 y k p kss 0 p 1/3 1/2 1/6 a c kss 0 p 1/2 1/6 a b c 1/3...... b Property1: A path has a trace c*a Prob 1 (p, trace = c*a) = 2/5 How? Can we do better?
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Process Algebra (2IF45) Abstraction on PLTSs
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21 Towards probabilistic branching bisimulation Process Algebra (2IF45) We consider again hiding of internal behaviour Again in the style of branching bisimulation, which is: -congruence -easy to axiomatize -rather intuitive -preserve properties
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22 Towards probabilistic branching bisimulation Process Algebra (2IF45) Recall Branching bisimulation on LTss s t s’ ts t’s’ a t’’ a
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23 Towards probabilistic branching bisimulation Process Algebra (2IF45) Recall Branching bisimulation on LTss s t s’ ts t’s’ a t’’ a Recall Strong Probabilistic bisimulation on PLTss s t C1 (eq. class ) s s’ a t’ a t C2 (eq. class ) 11 11 22 22
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24 Towards probabilistic branching bisimulation Process Algebra (2IF45) Recall Branching bisimulation on LTss s t s’ ts t’s’ a t’’ a Recall Strong Probabilistic bisimulation on PLTss s t C1 (eq. class ) s s’ a t’ a t C2 (eq. class ) 11 11 22 22 Combining them into Probabilistic Branching Bisimulation
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25 Missing ingredients: Process Algebra (2IF45) 1.We need a notion of for action transitions, just like in BB on LTSs 2.We need to compute probability to go to next eq. class from a probabilistic state, just like in PSB on PLTSs. 3. And something more…
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26 Missing ingredients Process Algebra (2IF45) s 0 a u 1 k 0 a r 1 n p 1 m q 1 Relate probabilistic and non-deter. states!
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27 Missing ingredients: Process Algebra (2IF45) 1.We need a notion of for action transitions, just like in BB on LTSs OK! Our unobservable paths are now: p0 n1 p1 n2… pk or p0 n1 p1 n2… nk 2.We need to compute probability to go to next eq. class from a probabilistic state, just like in PSB on PLTSs. But also for non-dterministic states. 1 if n C Prob(n, C) = 0 if n C
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28 Probabilistic Branching Bisimulation Process Algebra (2IF45) Definition : An equivalence relation R ⊆ S × S is a probabilistic branching bisimulation iff for every (s, t) ∈ R the following two conditions hold: (i)if s –-> s′ for a ∈ A or a= , then there exist states t0,..., tn, t′ such that t = t0 -------> t1 ------> … tn –-> t’ and (s, ti) ∈ R for all 0 ≤ i ≤ n, and (s′, t′) ∈ R, (ii) for all equivalence classes of states M ∈ S/R, Prob(s,M) = Prob(t,M). States s and t are branching bisimilar, denoted by s ∼ pbb t, if (s, t) ∈ R for some branching bisimulation relation R. a or a
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29 Examples: Probabilistic Branching Bisimulation Process Algebra (2IF45) Distributed page (part 2)
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