Active Diagnosis. Serge Haddad. Vecos 16. October the 6th 2016

Similar documents
Foundation of Diagnosis and Predictability in Probabilistic Systems

Optimal Constructions for Active Diagnosis

Decentralized Diagnosis for Nonfailures of Discrete Event Systems Using Inference-Based Ambiguity Management

Petri Nets. Rebecca Albrecht. Seminar: Automata Theory Chair of Software Engeneering

Behavior Composition in the Presence of Failure

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS

Controlling a population of identical NFA

Automatic Synthesis of New Behaviors from a Library of Available Behaviors

NON-DETERMINISTIC FSA

Nondeterministic Automata vs Deterministic Automata

Behavior Composition in the Presence of Failure

Learning Partially Observable Markov Models from First Passage Times

CS 573 Automata Theory and Formal Languages

Alpha Algorithm: Limitations

Hybrid Systems Modeling, Analysis and Control

On Determinism in Modal Transition Systems

On Determinisation of History-Deterministic Automata.

Transition systems (motivation)

Algorithm Design and Analysis

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution

Algorithm Design and Analysis

CIT 596 Theory of Computation 1. Graphs and Digraphs

Regular languages refresher

Prefix-Free Regular-Expression Matching

CONTROLLABILITY and observability are the central

CS415 Compilers. Lexical Analysis and. These slides are based on slides copyrighted by Keith Cooper, Ken Kennedy & Linda Torczon at Rice University

Graph width-parameters and algorithms

Discrete Structures, Test 2 Monday, March 28, 2016 SOLUTIONS, VERSION α

Regular expressions, Finite Automata, transition graphs are all the same!!

Infinite-Step Opacity of Stochastic Discrete-Event Systems

Chapter 4 State-Space Planning

On the Maximally-Permissive Range Control Problem in Partially-Observed Discrete Event Systems

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6

State Complexity of Union and Intersection of Binary Suffix-Free Languages

Nondeterministic Finite Automata

Compiler Design. Spring Lexical Analysis. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Counting Paths Between Vertices. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs

Finite State Automata and Determinisation

Designing finite automata II

Linear choosability of graphs

Solutions for HW9. Bipartite: put the red vertices in V 1 and the black in V 2. Not bipartite!

Stealthy Deception Attacks for Cyber-Physical Systems

Graph States EPIT Mehdi Mhalla (Calgary, Canada) Simon Perdrix (Grenoble, France)

Arrow s Impossibility Theorem

Good-for-Games Automata versus Deterministic Automata.

1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true.

Test Generation from Timed Input Output Automata

Alpha Algorithm: A Process Discovery Algorithm

Running an NFA & the subset algorithm (NFA->DFA) CS 350 Fall 2018 gilray.org/classes/fall2018/cs350/

= state, a = reading and q j

CS 2204 DIGITAL LOGIC & STATE MACHINE DESIGN SPRING 2014

Arrow s Impossibility Theorem

where the box contains a finite number of gates from the given collection. Examples of gates that are commonly used are the following: a b

Section 1.3 Triangles

The DOACROSS statement

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides.

Bisimulation, Games & Hennessy Milner logic

Abstraction of Nondeterministic Automata Rong Su

On Decentralized Observability of Discrete Event Systems

System Validation (IN4387) November 2, 2012, 14:00-17:00

Spacetime and the Quantum World Questions Fall 2010

MAT 403 NOTES 4. f + f =

Convert the NFA into DFA

Génération aléatoire uniforme pour les réseaux d automates

nd edges. Eh edge hs either one endpoint: end(e) = fxg in whih se e is termed loop t vertex x, or two endpoints: end(e) = fx; yg in whih se e is terme

Chapter 2 Finite Automata

A Disambiguation Algorithm for Finite Automata and Functional Transducers

Implication Graphs and Logic Testing

Lecture Notes No. 10

Graph Theory. Simple Graph G = (V, E). V={a,b,c,d,e,f,g,h,k} E={(a,b),(a,g),( a,h),(a,k),(b,c),(b,k),...,(h,k)}

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. NFA for (a b)*abb.

CS 310 (sec 20) - Winter Final Exam (solutions) SOLUTIONS

On-Time Diagnosis of Discrete Event Systems

CODIAGNOSABILITY OF NETWORKED DISCRETE EVENT SYSTEMS WITH TIMING STRUCTURE. Gustavo da Silva Viana

ANALYSIS AND MODELLING OF RAINFALL EVENTS

CS 491G Combinatorial Optimization Lecture Notes

Subsequence Automata with Default Transitions

Automata and Regular Languages

Nondeterminism and Nodeterministic Automata

Random subgroups of a free group

Finite Automata-cont d

Comparing the Pre-image and Image of a Dilation

The size of subsequence automaton

s the set of onsequenes. Skeptil onsequenes re more roust in the sense tht they hold in ll possile relities desried y defult theory. All its desirle p

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. Comparing DFAs and NFAs (cont.) Finite Automata 2

A Lower Bound for the Length of a Partial Transversal in a Latin Square, Revised Version

CS103B Handout 18 Winter 2007 February 28, 2007 Finite Automata

CMSC 330: Organization of Programming Languages

Generalization of 2-Corner Frequency Source Models Used in SMSIM

The Word Problem in Quandles

Myhill-Nerode Theorem

Synchronizing Automata with Random Inputs

CSE 332. Sorting. Data Abstractions. CSE 332: Data Abstractions. QuickSort Cutoff 1. Where We Are 2. Bounding The MAXIMUM Problem 4

Homework 3 Solutions

NFA DFA Example 3 CMSC 330: Organization of Programming Languages. Equivalence of DFAs and NFAs. Equivalence of DFAs and NFAs (cont.

Thoery of Automata CS402

Intermediate Math Circles Wednesday 17 October 2012 Geometry II: Side Lengths

Lecture 6: Coding theory

Table of contents: Lecture N Summary... 3 What does automata mean?... 3 Introduction to languages... 3 Alphabets... 3 Strings...

CS241 Week 6 Tutorial Solutions

Transcription:

Ative Dignosis Serge Hddd LSV, ENS Chn & CNRS & Inri, Frne Veos 16 Otoer the 6th 2016 joint work with Nthlie Bertrnd 2, Eri Fre 2, Sten Hr 1,2, Loï Hélouët 2, Trek Melliti 1, Sten Shwoon 1 (1) FSTTCS 2013 nd (2) FOSSACS 2014 1/39

Dignosis: rom ilures to ults Exmple: MYCIN, n expert system, tht used rtiiil intelligene to identiy teri using severe inetions (1975). 2/39

Dignosis: deteting ults Fult detetion: suield o ontrol engineering whih onerns itsel with monitoring system, identiying when ult hs ourred, nd pinpointing the type o ult nd its lotion. 3/39

Dignosis: prediting ults Enhning retivity: (see Foundtion o Dignosis nd Preditility in Proilisti Systems N. Bertrnd, H., E. Leuheux, FSTTCS14) 4/39

Ative dignosis: oring detetion Comining ontrol nd dignosis 5/39

Outline 1 Amiguity in Lelled Trnsition System (LTS) Ative dignosis in LTS From LTS to proilisti LTS Anlysis o tive dignosis in LTS 6/39

Oserving Lelled Trnsition System Sttes re unoservle. Events re either oservle or unoservle. Fults () re unoservle. q 1 q 2 q 0 d u q 3 q 4 q 5 An exeution sequene yields n oserved sequene. Let σ = q 0 uq 3 q 4 q 0 q 1 (q 2 ) ω. Then P(σ) = ω. We only onsider live nd onvergent systems: There is t lest n event rom ny stte. There is no ininite sequene o unoservle events rom ny rehle stte. 7/39

Clssiition o oserved sequenes An exeution sequene is ulty i it ontins ult otherwise it is orret. An oserved sequene σ is surely ulty i or ll σ P 1 (σ), σ is ulty. An oserved sequene σ is surely orret i or ll σ P 1 (σ), σ is orret. An oserved sequene σ is miguous i it is neither surely ulty nor surely orret. q 1 q 2 q 0 u d q 3 q 4 q 5 d ω is surely ulty: the ourrene o d implies the ourrene o. ω is surely orret: P 1 () = {q 0 uq 3 q 4 q 5 q 5 }. ω is miguous: P 1 ( ω ) = {q 0 uq 3 (q 4 ) ω, q 0 q 1 (q 2 ) ω }. 8/39

How to determine unmiguous sequenes? Build Bühi utomton s synhronized produt o the LTS with ult memory nd the LTS without ults. q 1 q 2 (q 4,q 4) (q 5,q 5) q 0 d (q 0,q 0) (q 0,q 5) u q 3 q 4 q 5 (q 2,q 4) (q 5,q 0) Determinize nd omplement it s: Street utomton with 2 O(n 2 log(n)) sttes where n is the numer o sttes o the LTS. Bühi utomton with 3 2n 2 sttes using the rekpoint onstrution o Miyno nd Hyshi pproprite or the initil Bühi utomton. 9/39

An optiml hrteriztion Build deterministi Bühi utomton whose sttes re triples (U, V, W ) with: U the set o possile sttes rehed y orret sequene; W the set o possile sttes rehed y n erliest ulty sequene; V the set o other possile sttes rehed y ulty sequenes. The epting sttes re (U, V, W ) with: U =, i.e. the oserved sequene is (nd will remin) surely ulty; W =, i.e. the erliest ulty sequenes re disrded. q 0 u q 1 q 2 d q 3 q 4 q 5 ({q 0 },, ) ({q 4 },,{q 2 }) d (,,{q 4 }) (,,{q 2,q 4 }) d ({q 0,q 5 },, ) (,,{q 0,q 5 }) ({q 5 },, ) (,,{q 5 }) The numer o sttes is t most 7 n. 10/39

A lower ound or miguity...,, l 0 l 1 l 2 l n 1 l n l n+1 d...,,,,d q 0 q 1 q 2 q n 1 q n q n+1,... r 0 r 1 r 2 r n 1 r n r n+1,, d Amiguous sequenes re either {, } k {, } n 1 d ω or {, } k {, } n 1 ω (with 0 k n 1). So deterministi utomton or miguity must hve (t lest) 2 n sttes rehle ter n events. 11/39

Outline Amiguity in Lelled Trnsition System (LTS) 2 Ative dignosis in LTS From LTS to proilisti LTS Anlysis o tive dignosis in LTS 12/39

Controllle LTS nd tive dignoser Events re lso prtitioned in ontrollle nd unontrollle events. Controllle events must e oservle. A ontroller orids ontrollle events depending on the urrent oserved sequene. An tive dignoser is ontroller suh tht the ontrolled LTS: is still live; does not ontin miguous sequenes. The dely o n tive dignoser is the mximl numer o event ourrenes etween exeution sequene is ulty nd n oserved sequene is surely ulty. 13/39

An exmple o tive dignoser The miguous sequenes re {, } ω. The (inite-stte) tive dignoser orids two onseutive. Its dely is 3 (t most n ourrene o ). q 0 q 1 q 2 Σ, Σ, Σ\{},, 14/39

Ative dignosis prolems The tive dignosis deision prolem, i.e. deide whether LTS is tively dignosle. The synthesis prolem, i.e. deide whether LTS is tively dignosle nd in the positive se uild n tive dignoser. The miniml-dely synthesis prolem, i.e. deide whether LTS is tively dignosle nd in the positive se uild n tive dignoser with miniml dely. 15/39

Bühi gmes A two-plyer (I nd II) Bühi gme is deined y: A grph (V, E) whose verties re owned y plyers with epting verties F ; In vertex v owned y plyer, he selets n edge (v, w) nd the gme goes on with w s urrent vertex. Plyer I wins i Plyer II is stuk in ded vertex or the ininite pth ininitely oten visits F. Gme prolems: Does there exists winning strtegy or Plyer I? In the positive se how to uild suh strtegy? Clssil results: The deision prolem is PTIME-omplete. In the positive se, there is positionl winning strtegy. 16/39

A Bühi gme or tive dignosis Verties o the gme The verties o Plyer I re the sttes o the Bühi utomton. The verties o Plyer II re pirs o sttes o the Bühi utomton nd (susets o) events o the LTS. The epting verties re the epting sttes o the Bühi utomton. Edges o the gme There is n edge ((U, V, W ), ((U, V, W ), Σ )) i Σ is suset o events (inluding the unontrollle ones) suh tht rom ll stte o U V W, there is n oserved sequene lelled y some e Σ. There is n edge (((U, V, W ), Σ ), ((U, V, W ), e) i e Σ. There is n edge (((U, V, W ), e), (U, V, W ) i there is trnsition (U, V, W ) e (U, V, W ) in the Bühi utomton. 17/39

Exmple o Bühi gme q 0 q 1 q 2, 0 1 ({q 0 },, ) ({q 0 },,{q 2 }) 4 5 ({q 0 },{q 1 }, ) (,,{q 2 }) ({q 0 },,{q 1 }) ({q 0 },{q 2 }, ) 2 3 0 (0,{,}) (0,) 1 (0,{,,}) (0,)... (0,{,}) (0,) 2 18/39

Results o this onstrution Correspondene etween prolems There is winning strtegy or Plyer I i nd only i there is n tive dignoser. The sttes o this tive dignoser re the sttes o the Bühi utomton. Consequenes The deision prolem is EXPTIME-omplete (the lower ound holds y redution rom sety gmes with prtil oservtion D. Berwnger nd L. Doyen FSTTCS 2008). The synthesis lgorithm yields n tive dignoser with 2 O(n) sttes. The previous synthesis lgorithm yields douly exponentil numer o sttes (M. Smpth, S. Lortune, nd D. Teneketzis, IEEE TAC 1998). For ll n N, there is LTS with n sttes suh tht ny tive dignoser requires 2 Ω(n) sttes. 19/39

A lower ound or the synthesis prolem...,, l 0 l 1 l 2 l n 1 l n l n+1... q 0 q 1 q 2 q n 1 q n q n+1,,,,d... r 0 r 1 r 2 r n 1 r n r n+1,, An tive dignoser must orid d (resp. ) i it hs oserved n (resp. ) n times eore. So n tive dignoser must hve (t lest) 2 n sttes rehle ter n oservle events., d d 20/39

Wht out miniml dely synthesis? Our synthesis lgorithm provides dely t most twie the miniml dely. For ll n N, there is LTS with n sttes suh tht ny tive dignoser with miniml dely requires 2 Ω(n log(n)) sttes. We hve designed synthesis lgorithm o n tive dignoser with miniml dely tht requires 2 O(n2) sttes. 21/39

Outline Amiguity in Lelled Trnsition System (LTS) Ative dignosis in LTS 3 From LTS to proilisti LTS Anlysis o tive dignosis in LTS 22/39

plts A proilisti lelled trnsition system (plts) is live LTS with trnsition proility mtrix P., 1 2,1 q 1 q 2, 1 2 q 0, 1 3 d, 1 2 u, 1 2 q 3 q 4,1, 1 3 q 5, 1 3,1 Without lels, plts is disrete time Mrkov hin. Without trnsition proilities, plts is LTS. 23/39

(Se) Dignosility A plts is dignosle i the set o sequenes yielding miguous oserved sequenes hs null mesure. A plts is sely dignosle i it is dignosle nd the set o orret sequenes hs positive mesure., 1 2,1 q 1 q 2, 1 2, 1 2,1 q 1 q 2, 1 2 q 0, 1 3 d, 1 2 q 0, 1 2 d, 1 2 u, 1 2 q 3 q 4,1, 1 3 q 5 u, 1 2 q 3 q 4,1, 1 3,1, 1 2 sely dignosle dignosle ut not sely dignosle 24/39

LTS A ontrollle proilisti lelled trnsition system (LTS) is live plts with integer weights on trnsitions. nd prtition etween ontrollle nd unontrollle events. An ontroller orids ontrollle events depending on the urrent oserved sequene. It n rndomly selet the oridden events. A ontroller must not introdue dedloks. Let C e LTS nd π e ontroller. Then C π is plts where the proility re otined y normliztion mong the llowed events. Controller π is (se) tive dignoser i C π is (sely) dignosle. 25/39

Illustrtion, 1 2,1 q 1 q 2, 1 2 A deterministi tive dignoser π: Forid two onseutive ter n. q 0 u, 1 2, 1 d, 1 3 2, 1 3 q 3 q 4,1 q 5, 1 3,1, 1 3 ε,q 4,Σ d,1 d, 1,1 2, 1 2, 1 3 ε,q 1,Σ,q 2,Σ,q, 2,Σ\{} 1 3, 1 2, 1 3 ε,q 0,Σ ε,q 5,Σ u, 1 2,1, 1 3, 1 2 ε,q 3,Σ,q 4,Σ,q 4,Σ\{},1, 1 3, 1 2 26/39

Ative proilisti dignosis The tive proilisti dignosis prolem sks whether there exists n tive dignoser π or C. The se tive proilisti dignosis prolem sks whether there exists se tive dignoser π or C. The synthesis prolems onsist in uilding (se) tive dignoser π or C in the positive se. 27/39

Outline Amiguity in Lelled Trnsition System (LTS) Ative dignosis in LTS From LTS to proilisti LTS 4 Anlysis o tive dignosis in LTS 28/39

Prtilly oserved Mrkov deision proess A prtilly oservle Mrkov deision proess (POMDP) is tuple M = Q, q 0, Os, At, T where: Q is inite set o sttes with q0 the initil stte; Os : Q O ssigns n oservtion O O to eh stte. At is inite set o tions; T : Q At Dist(Q) is prtil trnsition untion. q 0 1 3 2 3 q 1... q 2 Given sequene o oservtions, strtegy rndomly selets n tion to e perormed. Given strtegy, POMDP eomes (possily ininite) plts. 29/39

From LTS dignosis to POMDP prolems Let C e LTS nd its Bühi utomton B, M C is uilt s ollows. Sttes re pirs (l, q) with l stte o B nd q stte o C with Os(l, q) = l. Ations o M C re suset o events tht inludes the unontrollle events. Given some tion Σ, the trnsition proility o M C rom (l, q) to (l, q ) is: the sum o proilities o pths in C rom q to q ; lelled y unoservle events o Σ ; ending with n oservle event Σ suh tht l B l. The proility o ny suh pth is the produt o the individul step proilities. The ltter re then deined y the normliztion o weights w.r.t. Σ. When in C, some pth rehes stte where no event o Σ is possile, one rehes in M C n dditionl stte lost. 30/39

Illustrtion, 1 2,1 q 1 q 2, 1 2 q 0, 1 3 d, 1 2 u, 1 2 q 3 q 4,1, 1 3 q 5, 1 3,1 ({q 0},, ),q 0 Σ\{} Σ 1 2 1 2 ({q 4},,{q 2}),q 2... 1 lost ({q 4},,{q 2}),q 4... 31/39

Deidility o the tive dignosis prolem C is tively dignosle i there exists strtegy in M C suh tht: lmost surely (W = U = ) The existene o strtegy in POMDP or lmost surely stisying Bühi ojetive is deidle (Bier, Bertrnd, Größer, FoSSCS 2008). The proo in (Bertrnd, Genest, Gimert, LICS 2009) is more generl nd elegnt. Anlyzing the redution to the POMDP prolem, we get tht the tive dignosis prolem is EXPTIME-omplete. C is sely tively dignosle i there exists strtegy in M C suh tht: lmost surely (W = U = ); with positive proility U. 32/39

Belie-sed dignosers re not enough In our ontext, the elie is the urrent stte o the Bühi utomton. q 1 q 0,, q 3 q 4 q 2 The LTS is strightorwrdly dignosle ut it is not se. A se tive dignoser must perorm guess nd keep in memory one it: oridding ter n odd numer o oservtions; nd oridding ter n even numer o oservtions. 33/39

Finite-memory dignosers re not enough u r 0 q 2 q 1 q 0 r 1 r 2 An oserved sequene σ is surely ulty i σ Σ ω. An oserved sequene σ is surely orret i σ ( + ) ω. 34/39

Finite-memory dignosers re not enough u r 0 q 2 q 1 q 0 r 1 r 2 A se tive dignoser Pik ny sequene o positive integers {α i } i 1 suh tht i 1 1 > 0. 2 αi Let A = {, u,, } nd A = {, u,, }. Let π e the ontroller tht onsists in seleting, t instnt n, the n th suset in the ollowing sequene A α1 AA α2 A.... Then π is se tive dignoser: All oserved sequenes re either surely ulty or surely orret. The proility tht sequene is orret is 1 2 i 1 1 2 αi > 0. There is no inite-memory se tive dignoser. 35/39

From lind POMDP to se tive dignosis The existene o n ininite word epted y Bühi proilisti utomton with positive proility is undeidle (Bier, Bertrnd, Größer, Fosss 2008). The existene o winning strtegy with positive proility or Bühi ojetive in lind POMDP (i.e. without oservtion) is undeidle (Chtterjee, Doyen, Gimert, Henzinger, MFCS 2010). We redue the ltter prolem to se tive dignosility prolem. Corollry. The prolem whether, given POMDP M with susets o sttes F nd I, there exists strtegy π with P π (M = F ) = 1 nd P π (M = I) > 0, is undeidle. Oservtion: The existene o strtegy or eh ojetive is deidle. 36/39

Sheme o the redution s 2 s M v 2, p t 2 C, p, p, p v 1 i 2, p s 1, p i 1 v, p, p t r 0 u q, 0 r 1 r 2 i t 1, An oserved sequene σ is surely ulty i σ Σ ω. An oserved sequene σ is surely orret i σ (( + ) + ( + )) ω. 37/39

Restrition to inite-memory dignosers Oservtion A priori the inite-memory requirement does not ensure deidility. A deision proedure in EXPTIME: Computing the se elies tht ensure the existene o n tive dignoser surely yielding orret sequenes. Cheking the existene o dignoser tht ensure tive dignosility lmost surely nd rehing elie inluding se elie with positive proility. The tive dignoser only requires n dditionl oolen (or swithing its mode). The prolem is EXPTIME-hrd (using the sme redution s eore). 38/39

Contriutions Conlusion nd perspetives Strong improvement o the tive dignosis proedures or trnsition systems. Almost mthing lower ounds o the tive dignosis prolems or trnsition systems. Introdution o (se) tive dignosis prolems or proilisti systems. Anlysis o the prolems or proilisti systems using POMDP rmework. Perspetives Closing the gp etween lower nd upper ounds relted to the miniml dely synthesis prolem. Introduing the tive preditility prolem (nd other relted issues). Investigting urther POMDP prolems with multiple ojetives. Modelling nd nlyzing dignosis with stohsti gmes. 39/39