Good old days: Observing trees in JCMB · Testing theory pCSP Testing power Simulations Equations...

Testing theory pCSP Testing power Simulations Equations

Good old days: Observing trees in JCMB

For fun: add probabilitiesDGHMZ (Nicta) Probabilistic testing gdp festschrift 1 / 32

Good old days: Observing trees in JCMB

For fun: add probabilitiesDGHMZ (Nicta) Probabilistic testing gdp festschrift 1 / 32

Some remarks on testing probabilistic processes

Matthew Hennessy

and Yuxin Deng, Rob van Glabbeek, Carroll Morgan, Chenyi ZhangNicta

gdp festschrift

DGHMZ (Nicta) Probabilistic testing gdp festschrift 2 / 32

Outline

Testing theory

Probabilistic CSP

The power of testing

Simulations

Equational theories

Outline

Testing theory

Probabilistic CSP

Simulations

Equational theories

Outline

Testing theory

Probabilistic CSP

Simulations

Equational theories

Outline

Testing theory

Probabilistic CSP

Simulations

Equational theories

Outline

Testing theory

Probabilistic CSP

Simulations

Equational theories

Outline

Testing theory

Probabilistic CSP

Simulations

Equational theories

Testing scenario

◮ a set of processes Proc◮ a set of tests T

◮ a set of outcomes O

◮ Apply : T × Proc → P+

fin(O) – the non-empty finite set of possibleresults of applying a test to a process

Comparing sets of outcomes:

◮ O1 ⊑Ho O2 if for every o1 ∈ O1 there exists some o2 ∈ O2 suchthat o1 ≤ o2

◮ O1 ⊑Sm O2 if for every o2 ∈ O2 there exists some o1 ∈ O1 suchthat o1 ≤ o2

o1 ≤ o2 : means o2 is as least as good as o1

Testing scenario

◮ a set of processes Proc◮ a set of tests T

◮ a set of outcomes O

◮ Apply : T × Proc → P+

fin(O) – the non-empty finite set of possibleresults of applying a test to a process

Comparing sets of outcomes:

◮ O1 ⊑Ho O2 if for every o1 ∈ O1 there exists some o2 ∈ O2 suchthat o1 ≤ o2

◮ O1 ⊑Sm O2 if for every o2 ∈ O2 there exists some o1 ∈ O1 suchthat o1 ≤ o2

o1 ≤ o2 : means o2 is as least as good as o1

Testing preorders◮ P ⊑may Q if Apply(T , P)⊑Ho Apply(T , Q) for every test T◮ P ⊑must Q if Apply(T , P) ⊑Sm Apply(T , Q) for every test T

Standard testing:Use as outcomes O = {⊤, ⊥} with ⊥ ≤ ⊤

Probabilistic testing:Use as O the unit interval [0, 1]Intuition: with 0 ≤ p ≤ q ≤ 1, passing a test with probability q betterthn passing with probability p

TheoremFor O1, O2 ∈ P

fin(Oprob) we have◮ O1 ⊑Ho O2 if and only if max(O1) ≤ max(O2)

◮ O1 ⊑Sm O2 if and only if min(O1) ≤ min(O2)

Outline

Testing theory

Probabilistic CSP

Simulations

Equational theories

Finite probabilistic CSP: pCSP

◮ 0 Do nothing◮ a.P Perform a then act as P◮ P |A Q Run P and Q in parallel . . .◮ P � Q External nondeterministic choice between P and Q -

environment chooses◮ P ⊓ Q Internal nondeterministic choice between P and Q◮ P p⊕ Q Probabilistic choice: act like P with probability p, as Q

with probability (1 − p)

CSP: the sublanguage without probabilistic choice

Operational semanticsLTS:a triple 〈S, Actτ ,→〉, with

◮ S a set of states◮ Actτ a set of actions Act, with extra τ

◮ → ⊆ S × Actτ × S - the effect of performing actions.

pLTS, probabilistic LTS:a triple 〈S, Actτ ,→〉, with

◮ → ⊆ S × Actτ× D(S) - the effect of performing actions

(finite) Distributions D(S):Mappings ∆ : S → [0, 1] with

∑s∈S ∆(s) = 1DGHMZ (Nicta) Probabilistic testing gdp festschrift 9 / 32

Operational semantics

∑s∈S ∆(s) = 1

◮ ⌈∆⌉ := { s ∈ S | ∆(s) > 0 } is finite the support of ∆

∑s∈S ∆(s) = 1

◮ ⌈∆⌉ := { s ∈ S | ∆(s) > 0 } is finite the support of ∆

CSP as an LTS:

◮ States S are all terms from CSP◮ Actions s α−→ s′ defined inductively

pCSP as a pLTS:

◮ States S are subset Sp of terms from pCSP◮ Actions s α−→ s′ defined inductively◮ Terms P in pCSP interpreted as distributions [P℄ over S

pCSP as a pLTSStates Sp:

◮ 0 ∈ Sp

◮ a.P ∈ Sp

◮ P ⊓ Q ∈ Sp

◮ s1, s2 ∈ Sp implies s1 � s2 ∈ Sp

◮ s1, s2 ∈ Sp implies s1 |A s2 ∈ Sp.

Distributions [P℄:◮ [s℄ = s one point distribution s → 1

◮ [P p⊕ Q℄ = p · [P℄ + (1−p) · [Q℄◮ [P � Q℄ = [P℄ � [Q℄◮ [P |A Q℄ = [P℄ |A [Q℄

pCSP as a probabilistic LTS

Defining arrows:Spα−→ D(Sp)

(action)

a.P a−→ [P℄ (int.l)

P ⊓ Q τ−→ [P℄(ext.i.l)

s1τ−→ ∆

s1 � s2τ−→ ∆ � s2

(ext.l)

s1a−→ ∆

s1 � s2a−→ ∆

(par.l)

s1α−→ ∆

s1 |A s2α−→ ∆ |A s2

α 6∈ A

(par.i)

s1a−→ ∆1, s2

a−→ ∆2

s1 |A s2τ−→ ∆1 |A ∆2

a ∈ A

and symmetric rules

Very similar to standard rules

pCSP as a probabilistic LTS

Defining arrows:Spα−→ D(Sp)

(action)

a.P a−→ [P℄ (int.l)

P ⊓ Q τ−→ [P℄(ext.i.l)

s1τ−→ ∆

s1 � s2τ−→ ∆ � s2

(ext.l)

s1a−→ ∆

s1 � s2a−→ ∆

(par.l)

s1α−→ ∆

s1 |A s2α−→ ∆ |A s2

α 6∈ A

(par.i)

s1a−→ ∆1, s2

a−→ ∆2

s1 |A s2τ−→ ∆1 |A ∆2

a ∈ A

and symmetric rules

Very similar to standard rules

Example pLTSs

abbrev. to b

b. 0 ⊓ c. 0

◮ b states◮ bc distributions

Example pLTSs

(b ⊓ c) � (d 12⊕ a) bc

a 13⊕ ((b ⊓ c) � (d 1

2⊕ a)) bc

Example pLTSs

(b ⊓ c) � (d 12⊕ a) bc

a 13⊕ ((b ⊓ c) � (d 1

2⊕ a)) bc

Testing pCSP processes

Tests:Any process which may contain new report success action ω

a.ω 14⊕ (b � c.ω):

◮ 25% of time requests an a action◮ 75% requests a c action◮ 75% requires that b is not possible in a must test

Applying test T to process P:

◮ Run the combined process T |Act P◮ Calculate chances of success

Note: T |Act P can only perform τ or report success

Running a test

Test: T = a.ω 14⊕ (b � c.ω) Process P = b � c � d

(a.ω 14⊕ (b � c.ω)) |Act (b � c � d)

a.ω |Act (b � c � d)

(b � c.ω) |Act (b � c � d)

0 |Act 0

ω |Act 0

0 |Act 0

Calculating chances of success

Test: T = a.ω 14⊕ (b � c.ω) Process P = b � c � d

Calculate success probabilities:

Apply(T , P) = 14 · {0} + 3

4 · {0, 1} = {0,34}

Outline

Testing theory

Probabilistic CSP

Simulations

Equational theories

Example: probabilistic choice and prefixingR1 = a.(b 1

2⊕ c) R2 = a.b 1

2⊕ a.c T = a.b.ω ⊓ a.c.ω

Apply(T ,R1)={ 12} Apply(T ,R2)={0,

12 ,1}= 1

2 ·{0,1}+ 12 ·{0,1}

So R2 6⊑pmay R1 and R1 6⊑pmust R2

2⊕ c) R2 = a.b 1

2⊕ a.c T = a.b.ω ⊓ a.c.ω

12 ,1}= 1

2 ·{0,1}+ 12 ·{0,1}

2⊕ c) R2 = a.b 1

2⊕ a.c T = a.b.ω ⊓ a.c.ω

12 ,1}= 1

2 ·{0,1}+ 12 ·{0,1}

A beloved testing axiom

R1 = a.(b ⊓ c) R2 = a.b ⊓ a.c T = a.(b.ω 12⊕ c.ω)

Apply(T ,R1)={0,

12 ,1} Apply(T ,R2)={ 1

So action prefix does not distribute over internal choice

A beloved testing axiom

R1 = a.(b ⊓ c) R2 = a.b ⊓ a.c T = a.(b.ω 12⊕ c.ω)

Apply(T ,R1)={0,

12 ,1} Apply(T ,R2)={ 1

So action prefix does not distribute over internal choice

Power of internal choice in tests

◮ R1 = a 12⊕ (b � c) R2 = (a � b) 1

2⊕ (a � c)

◮ T = a.(ω 12⊕ 0) ⊓ (b.ω 1

2⊕ c.ω)

◮ Apply(T , R1) = {0,14 ,

◮ Apply(T , R2) = {12}

Therefore R1 6⊑pmay R2

Without internal choice in tests, R1 ⊑may R2

◮ R1 = a 12⊕ (b � c) R2 = (a � b) 1

2⊕ (a � c)

◮ T = a.(ω 12⊕ 0) ⊓ (b.ω 1

2⊕ c.ω)

◮ Apply(T , R1) = {0,14 ,

34} = 1

2 · {0,12} + 1

2 · {0, 1}

◮ Apply(T , R2) = {12} = 1

2 · {12} + 1

2 · {12}

◮ R1 = a 12⊕ (b � c) R2 = (a � b) 1

2⊕ (a � c)

◮ T = a.(ω 12⊕ 0) ⊓ (b.ω 1

2⊕ c.ω)

◮ Apply(T , R1) = {0,14 ,

◮ Apply(T , R2) = {12}

◮ R1 = a 12⊕ (b � c) R2 = (a � b) 1

2⊕ (a � c)

◮ T = a.(ω 12⊕ 0) ⊓ (b.ω 1

2⊕ c.ω)

◮ Apply(T , R1) = {0,14 ,

◮ Apply(T , R2) = {12}

Massacre of the axioms

a.(P ⊓ Q) 6= a.P ⊓ a.Q

P 6= P � P

P � (Q ⊓ R) 6= (P � Q) ⊓ (P � R)

P ⊓ (Q � R) 6= (P ⊓ Q) ⊓ (P ⊓ R)

Hopes dashed:

P p⊕ (Q � R) 6= (P p⊕ Q) � (P p⊕ R)

P ⊓ (Q p⊕ R) 6= (P ⊓ Q) p⊕ (P ⊓ R)

P p⊕ (Q � R) 6= (P p⊕ Q) � (P p⊕ R)

All is not lost:

a.P � a.Q = a.P ⊓ a.Q

P � (Q p⊕ R) = (P � Q) p⊕ (P p⊕ R)

. . . = . . .

a.(P ⊓ Q) 6= a.P ⊓ a.Q

P 6= P � P

P � (Q ⊓ R) 6= (P � Q) ⊓ (P � R)

P ⊓ (Q � R) 6= (P ⊓ Q) ⊓ (P ⊓ R)

Hopes dashed:

P p⊕ (Q � R) 6= (P p⊕ Q) � (P p⊕ R)

P ⊓ (Q p⊕ R) 6= (P ⊓ Q) p⊕ (P ⊓ R)

P p⊕ (Q � R) 6= (P p⊕ Q) � (P p⊕ R)

All is not lost:

a.P � a.Q = a.P ⊓ a.Q

P � (Q p⊕ R) = (P � Q) p⊕ (P p⊕ R)

. . . = . . .

a.(P ⊓ Q) 6= a.P ⊓ a.Q

P 6= P � P

P � (Q ⊓ R) 6= (P � Q) ⊓ (P � R)

P ⊓ (Q � R) 6= (P ⊓ Q) ⊓ (P ⊓ R)

Hopes dashed:

P p⊕ (Q � R) 6= (P p⊕ Q) � (P p⊕ R)

P ⊓ (Q p⊕ R) 6= (P ⊓ Q) p⊕ (P ⊓ R)

P p⊕ (Q � R) 6= (P p⊕ Q) � (P p⊕ R)

All is not lost:

a.P � a.Q = a.P ⊓ a.Q

P � (Q p⊕ R) = (P � Q) p⊕ (P p⊕ R)

. . . = . . .

Outline

Testing theory

Probabilistic CSP

Simulations

Equational theories

Simulations: for may testing

Simulations relate states with distributions

Definition (Segala)

◮ R ⊆ Sp ×D(Sp) is a simulation ifs R ∆ and s α−→ Θ implies there exists some ∆′

Θ R ∆′ and ∆α

=⇒ ∆′

Requirements:

◮ Lift Sp R D(Sp) to D(Sp) R D(Sp)

◮ Generalise s α−→ ∆′ to ∆α

=⇒ ∆′

=⇒ means τ−→∗ a−→ τ−→∗

=⇒ means τ−→∗

Definition (Segala)

=⇒ ∆′

Requirements:

=⇒ ∆′

=⇒ means τ−→∗ a−→ τ−→∗

Definition (Segala)

=⇒ ∆′

Requirements:

=⇒ ∆′

=⇒ means τ−→∗ a−→ τ−→∗

Definition (Segala)

=⇒ ∆′

Requirements:

=⇒ ∆′

=⇒ means τ−→∗ a−→ τ−→∗

Lifting relation: S R D(S)

∆1 R ∆2 if◮ source ∆1 can be decomposed:

∆1 =∑

pi · si

◮ each decomposed state si can be related:

si R Φi , for some Φi

◮ related Φi yield the target:

∆2 =∑

pi · Φi

Example liftings: actions(a.b � a.c) 1

2⊕ a.d a−→ b 1

2⊕ d

because (a.b � a.c) a−→ b a.d a−→ d(a.b � a.c) 1

2⊕ a.d a−→ (b 1

2⊕ c) 1

2⊕ d

because◮ source is 1

4 · (a.b � a.c) + 14 · (a.b � a.c) + 1

2 · a.d

◮ a.b � a.c a−→ b a.b � a.c a−→ c a.d a−→ d

◮ target is 14 · b + 1

4 · c + 12 · d

2⊕ a.d a−→ b 1

2⊕ d

2⊕ a.d a−→ (b 1

2⊕ c) 1

2⊕ d

4 · (a.b � a.c) + 14 · (a.b � a.c) + 1

2 · a.d

4 · c + 12 · d

2⊕ a.d a−→ b 1

2⊕ d

2⊕ a.d a−→ (b 1

2⊕ c) 1

2⊕ d

4 · (a.b � a.c) + 14 · (a.b � a.c) + 1

2 · a.d

4 · c + 12 · d

2⊕ a.d a−→ b 1

2⊕ d

2⊕ a.d a−→ (b 1

2⊕ c) 1

2⊕ d

4 · (a.b � a.c) + 14 · (a.b � a.c) + 1

2 · a.d

4 · c + 12 · d

Lifting actions: 0 or 1 internal moves: τ−→

(a ⊓ b) 12⊕ (a ⊓ c) τ−→ a 1

2⊕ (a ⊓ b 1

2⊕ c)

4 · (a ⊓ b) + 14 · (a ⊓ b) + 1

4 · (a ⊓ c) + 14 · (a ⊓ c)

(a ⊓ b) τ−→ a (a ⊓ b) τ−→ (a ⊓ b)

(a ⊓ c) τ−→ a (a ⊓ c) τ−→ c

◮ target is 14 · a + 1

4 · (a ⊓ b) + 14 · a + 1

4 · c

(a ⊓ b) 12⊕ (a ⊓ c) τ−→ a 1

2⊕ (a ⊓ b 1

2⊕ c)

4 · (a ⊓ b) + 14 · (a ⊓ b) + 1

4 · (a ⊓ c) + 14 · (a ⊓ c)

(a ⊓ b) τ−→ a (a ⊓ b) τ−→ (a ⊓ b)

(a ⊓ c) τ−→ a (a ⊓ c) τ−→ c

4 · (a ⊓ b) + 14 · a + 1

4 · c

(a ⊓ b) 12⊕ (a ⊓ c) τ−→ a 1

2⊕ (a ⊓ b 1

2⊕ c)

4 · (a ⊓ b) + 14 · (a ⊓ b) + 1

4 · (a ⊓ c) + 14 · (a ⊓ c)

(a ⊓ b) τ−→ a (a ⊓ b) τ−→ (a ⊓ b)

(a ⊓ c) τ−→ a (a ⊓ c) τ−→ c

4 · (a ⊓ b) + 14 · a + 1

4 · c

(a ⊓ b) 12⊕ (a ⊓ c) τ−→ a 1

2⊕ (a ⊓ b 1

2⊕ c)

4 · (a ⊓ b) + 14 · (a ⊓ b) + 1

4 · (a ⊓ c) + 14 · (a ⊓ c)

(a ⊓ b) τ−→ a (a ⊓ b) τ−→ (a ⊓ b)

(a ⊓ c) τ−→ a (a ⊓ c) τ−→ c

4 · (a ⊓ b) + 14 · a + 1

4 · c

Results on simulations

P ⊑S Q - lifting of simulations to pCSP processes

◮ ⊑S is a precongruence for pCSP◮ For pCSP, P ⊑S Q implies P ⊑pmay Q◮ For CSP, P ⊑S Q iff P ⊑pmay Q

◮ P ⊑S Q can be equationally characterised, over pCSP◮ P ⊑S Q can be equationally characterised, over CSP

Conjecture:◮ For pCSP, P ⊑S Q iff P ⊑pmay Q

Results on simulations

P ⊑S Q - lifting of simulations to pCSP processes

◮ ⊑S is a precongruence for pCSP◮ For pCSP, P ⊑S Q implies P ⊑pmay Q◮ For CSP, P ⊑S Q iff P ⊑pmay Q

◮ P ⊑S Q can be equationally characterised, over pCSP◮ P ⊑S Q can be equationally characterised, over CSP

Conjecture:◮ For pCSP, P ⊑S Q iff P ⊑pmay Q

Outline

Testing theory

Probabilistic CSP

Simulations

Equational theories

Equations for CSPtests contain probability choice, processes not

A selection:a.P � a.Q = a.P ⊓ a.Q

a.P � (Q ⊓ R) = (a.P � Q) ⊓ (a.P � R)P � Q = (P1 � Q) ⊓ (P2 � Q) ⊓ (P � Q1) ⊓ (P � Q2),

provided P = P1 ⊓ P2, Q = Q1 ⊓ Q2

For completeness add: P ⊑ P ⊓ Q P ⊓ Q = P � Q standard

Interesting derived equations:a.P ⊓ a.Q ⊑ a.(P ⊓ Q)

(P � Q) ⊓ (P � R) ⊑ P � (Q ⊓ R)P ⊓ (Q � R) ⊑ (P ⊓ Q) � (P ⊓ R)

Equations for pCSPprocesses and tests contain probability choice

Standard: P p⊕ P = PP p⊕ Q = Q 1−p⊕ P

(P p⊕ Q) q⊕ R = P p·q⊕ (Q (1−p)·q1−p·q

⊕ R)

Add: a.(P p⊕ Q) ⊑ a.P p⊕ a.QP � (Q p⊕ R) = (P � Q) p⊕ (P � R)

Interesting derived equations:(P p⊕ Q) ⊓ (P p⊕ R) ⊑ P p⊕ (Q ⊓ R)

P ⊓ (Q p⊕ R) ⊑ (P ⊓ Q) p⊕ (P ⊓ R)a.(P p⊕ Q) ⊑ a.P � a.Q

(P p⊕ Q) q⊕ R = P p·q⊕ (Q (1−p)·q1−p·q

⊕ R)

(P p⊕ Q) q⊕ R = P p·q⊕ (Q (1−p)·q1−p·q

⊕ R)

The endLots of work to do:

◮ Prove relationship between simulations and may testing◮ Must testing:

◮ relate to failure simulations◮ equations ?

◮ Modal logics ?

Related work

◮ Segala, Wang Yi, Jonsson, Larsen, Skou, Lowe, Cleaveland,Smolka, . . .

◮ Google:◮ probabilistic process calculi: 631,000◮ testing probabilistic process calculi: 394,000◮ testing probabilistic processes: 4,800,000◮ probabilistic processes: 7,200,000

The endLots of work to do:

◮ Prove relationship between simulations and may testing◮ Must testing:

◮ relate to failure simulations◮ equations ?

◮ Modal logics ?

Related work

◮ Segala, Wang Yi, Jonsson, Larsen, Skou, Lowe, Cleaveland,Smolka, . . .

◮ Google:◮ probabilistic process calculi: 631,000◮ testing probabilistic process calculi: 394,000◮ testing probabilistic processes: 4,800,000◮ probabilistic processes: 7,200,000

Good old days: Observing trees in JCMB · Testing theory pCSP Testing power Simulations Equations...

Documents

Transcript of Good old days: Observing trees in JCMB · Testing theory pCSP Testing power Simulations Equations...

Observing Cosmic Superfluidity in Glitches of the Pulsars ...conferences.illinois.edu/bcs50/PDF/Alpar.pdfObserving Cosmic Superfluidity in Glitches of the Pulsars BCS50 Urbana –

CSE 5311 Notes 4b: van Emde Boas Trees - Rangerranger.uta.edu/~weems/NOTES5311/NEWNOTES/notes04b.pdf · CSE 5311 Notes 4b: van Emde Boas Trees (Last updated 9/25/15 1:11 PM) CLRS

Applied Statistics - ΣΣ Statistical · PDF fileApplied Statistics James Reilly - 3 - Table of Contents 1. Observing Data 1A. ... 8C. Acceptance Sampling

10. AVL Trees · AVL trees have worst-case asymptotic runtimes of O(logn) for searching, insertion and deletion of keys. Insertion and deletion is relatively involved and an overkill

CSC 2300 Data Structures & Algorithms February 16, 2007 Chapter 4. Trees.

CSCI 104 Log Structured Merge Trees - USC Viterbiee.usc.edu/~redekopp/cs104/slides/L24b_MergeTrees.pdf · 5 Merge Trees Find Operation • To find an element (or check if it exists)

Decision Trees - Donald Bren School of Information and ...xhx/courses/CS273P/08-dectree-273p.pdf · Decision Trees PROF XIAOHUI XIE SPRING 2019 CS 273P Machine Learning and Data Mining

Plant trees in kerkini lake

AVL Trees - York · PDF file•A good balance conditions ensures the height of a tree ... // Different deﬁnition for height for AVL trees. // Height of leaf is 1 (Figure 10.10 p435)

Monte-Carlo Planning Look Ahead Trees

S. I. Gutman Chief, GPS-Met Observing Systems Section NOAA ...

pbl/galop2016/ribaslides.pdf · Introduction Introduction Fix D non-empty and ﬁnite. Full -labelled D-ary trees are maps t : D ! Monadic-Second Order Logic (MSO) (over D-ary trees)

Comparison on Calculated Helicity Parameters at Different Observing Sites Haiqing Xu (NAOC) Collaborators: Hongqi, Zhang, NAOC Kirill Kuzanyan, IZMIRAN,

An involution on bicubic maps and 1)-trees · 2014-11-24 · ways of decomposing (0;1)-trees. Using this we de ne an endofunc-tion on (0;1)-trees, and thus also on bicubic maps. We

Suffix Trees and Arraystaoyf/course/wst540/notes/lec12.pdf · 2017. 6. 30. · for q = bab, return 4 and 6. for q = bbb, return nothing. Y. Tao, May 8, 2013 Su x Trees and Arrays.

Graph Edge Coloring: Tashkinov Trees and Goldberg’s … · Graph Edge Coloring: Tashkinov Trees and Goldberg’s Conjecture ... [13, 14] a simple but very ... tional edge coloring

Beyond Serre’s “Trees” in two directions: Λ–trees and ... · are considered in the second part of the book. Bass-Serre theory states ... The theory of isometric group actions

NORMALIZING ITERATION TREES AND COMPARING ITERATION ...

The Living Cosmos - from Oak Trees to the “Big Bang ” Chris Clarke

Trees & plants