Supervisory Control (4CM30)
|
|
- Ami Garrison
- 5 years ago
- Views:
Transcription
1 Supervisory Control (4CM30) Verifiction in mcrl2 Michel Reniers
2 Verifiction in mcrl2 M CIF = φ CIF iff M mcrl2 = φ mcrl2 1. Adpt CIF model 2. Formulte property in modl µ-clculus 3. Trnslte CIF model into mcrl2 4. Verify property in mcrl2 Exmple: check supermrket model for property whether it is possible tht queue 1 contins three customers
3 Exmple: supermrket 1 controllble q1enter, q1leve, q2enter, q2leve ; 2 3 plnt queue1 : 4 disc int [0..5] count = 0; 5 loction l0: 6 initil ; 7 mrked ; 8 edge q1enter when count < 5 do count := count + 1; 9 edge q1leve when count > 0 do count := count - 1; 10 end plnt queue2 : 13 disc int [0..5] count = 0; 14 loction l0: 15 initil ; 16 mrked ; 17 edge q2enter when count < 5 do count := count + 1; 18 edge q2leve when count > 0 do count := count - 1; 19 end plnt customer : 22 loction l0: 23 initil ; 24 mrked ; 25 edge q1enter when queue1. count <= queue2. count ; 26 edge q2enter when queue2. count <= queue1. count ; 27 end requirement invrint queue1. count < 3; requirement q2enter needs queue2. count < 3;
4 Step 1: Adpt CIF model Explicitly introduce Boolen loction vribles (using elim-locs-in-exprs) Remove event conditions (using elim-stte-evt-excl-inv) Remove invrints mnully Add self-loop loction events (if needed for property) Add self-loop mrked stte events (if needed for property)
5 Exmple Remove event condition 1 requirement q2enter needs queue2. count < 3; is replced by 1 requirement utomton RequirementStteEvtExcls : 2 loction : 3 initil ; 4 mrked ; 5 edge q2enter when queue2. count < 3; 6 end Remove invrint mnully 1 requirement invrint queue1. count < 3; is replced by (dpted copy of involved plnt(s)) 1 requirement utomton RequirementInvrint : 2 loction l0: 3 initil ; 4 mrked ; 5 edge q1enter when queue1. count < 2; 6 edge q1leve when queue1. count < 4; 7 end
6 Step 2: Formulte property in modl µ-clculus use loction events nd mrked stte events use vrible vlue events to refer to vlues of vribles mcrl2 syntx for modl µ-clculus properties reference/muclc.html file with extension mcf
7 Exmple property of interest: is it possible tht queue 1 contins three customers true queue1.count=3 find right event representing the vrible: vlue count mcrl2 syntx true vlue count(3) true 1 <true *> < vlue_count (3) > true
8 Step 3: Trnslte CIF model to mcrl2 trnsltion in CIF tool hs irritting mistkes use tooldef with nme fix mcrl2 output.tooldef2 with nme of CIF file to be processed in line 3 1 from " lib : cif3 " import *; 2 3 string bse_ nme = " xxx "; 4 string cif_ file = bse_ nme + ". cif "; 5... results in file with nme xxx-fixed.mcrl2 to be used by mcrl2
9 Step 4: Verify property in mcrl2 1. pply mcrl22lps on the mcrl2 file with the option no-lph checked! 2. pply lps2pbes on the lps file nd the mcf file with the property. The result is file with extension pbes. 3. pply ps2bool on this pbes file.
10 Supervisory Control (4CM30) Modl µ-clculus & dt Michel Reniers
11 Even more expressivity... there re still properties we cnnot express ll behviour inevitbly reches stte where formul φ holds there is some behviour where the formul φ holds everywhere formulting properties using the modl µ-clculus requires experience. φ ::= true flse φ φ φ φ φ φ φ φ []φ µx.φ νx.φ X Hennessy-Milner logic is included ction formuls cn be trnslted (s HML is included) regulr formuls cn be trnslted (explined lter)
12 Fixed points in mthemtics in mthemtics: x is fixed point of function f if x = f(x) exmple: 3 is fixed point of function f with f(x) = x 2 2x function my hve multiple fixed points fixed point is solution of n eqution with unknow(s)/vrible(s): x = x 2 2x
13 Fixed points in modl µ-clculus Given trnsition system with stte spce S, modl µ-clculus formul φ represents subset of S for which it holds. Consider the eqution X = true. b
14 Fixed points in modl µ-clculus Given trnsition system with stte spce S, modl µ-clculus formul φ represents subset of S for which it holds. Consider the eqution X = true. true represents the set of sttes where it holds b
15 Fixed points in modl µ-clculus Given trnsition system with stte spce S, modl µ-clculus formul φ represents subset of S for which it holds. Consider the eqution X = true. true represents the set of sttes where it holds set of ll sttes from which n -lbelled trnsition strts is the solution b
16 Fixed points in modl µ-clculus Given trnsition system with stte spce S, modl µ-clculus formul φ represents subset of S for which it holds. Consider the eqution X = true. true represents the set of sttes where it holds set of ll sttes from which n -lbelled trnsition strts is the solution unique solution (independent of X) b
17 Consider the eqution X = X: s Wht is the solution?
18 Consider the eqution X = X: s Wht is the solution? There re only two cndidtes: X = or X = S = {s}
19 Consider the eqution X = X: s Wht is the solution? There re only two cndidtes: X = or X = S = {s} Wht is mening of X? It is the set of sttes tht cn execute nd end up in the set represented by X
20 Consider the eqution X = X: s Wht is the solution? There re only two cndidtes: X = or X = S = {s} Wht is mening of X? It is the set of sttes tht cn execute nd end up in the set represented by X So = nd S = S
21 Consider the eqution X = X: s Wht is the solution? There re only two cndidtes: X = or X = S = {s} Wht is mening of X? It is the set of sttes tht cn execute nd end up in the set represented by X So = nd S = S so both re solution to the eqution
22 Miniml nd mximl solutions Consider the eqution X = X: s µx.φ denotes the miniml solution for the eqution X = φ
23 Miniml nd mximl solutions Consider the eqution X = X: s µx.φ denotes the miniml solution for the eqution X = φ µx. X holds for no sttes since the miniml fixed point of the eqution X = X is
24 Miniml nd mximl solutions Consider the eqution X = X: s µx.φ denotes the miniml solution for the eqution X = φ µx. X holds for no sttes since the miniml fixed point of the eqution X = X is νx.φ denotes the mximl solution
25 Miniml nd mximl solutions Consider the eqution X = X: s µx.φ denotes the miniml solution for the eqution X = φ µx. X holds for no sttes since the miniml fixed point of the eqution X = X is νx.φ denotes the mximl solution µx. X holds for sttes since the mximl fixed point of the eqution X = X is {s}
26 Sfety properties Nothing bd my hppen Assume tht φ chrcterises good sttes µx.[true]φ expresses sfety [true ]φ lso expresses sfety
27 Liveness Something good cn hppen Assume tht phi chrcterises the good thing νx. true φ expresses liveness true φ lso expresses liveness
28 Regulr formuls trnslte to modl µ-clculus R φ = µx.( R X φ) [R ]φ = νx.([r]x φ)
29 Inevitbly φ only expressestht φ cn become vlid in some run of the system
30 Inevitbly φ only expressestht φ cn become vlid in some run of the system often desired: φ will eventully become vlid long every pth µx.([true]x φ)
31 Inevitbly φ only expressestht φ cn become vlid in some run of the system often desired: φ will eventully become vlid long every pth µx.([true]x φ) not expressible without fixed point opertor
32 Inevitbly φ only expressestht φ cn become vlid in some run of the system often desired: φ will eventully become vlid long every pth µx.([true]x φ) not expressible without fixed point opertor this formul will lso become true for pths ending in dedlock, becuse in such stte [true]x becomes vlid
33 Inevitbly φ only expressestht φ cn become vlid in some run of the system often desired: φ will eventully become vlid long every pth µx.([true]x φ) not expressible without fixed point opertor this formul will lso become true for pths ending in dedlock, becuse in such stte [true]x becomes vlid void this by dding bsence of dedlock explicitly: µx.(([true]x true true) φ)
34 Inevitbly φ only expressestht φ cn become vlid in some run of the system often desired: φ will eventully become vlid long every pth µx.([true]x φ) not expressible without fixed point opertor this formul will lso become true for pths ending in dedlock, becuse in such stte [true]x becomes vlid void this by dding bsence of dedlock explicitly: µx.(([true]x true true) φ) Exercise: Formulte the property tht n ction must inevitbly be done unless the system dedlocks µx.([true]x true) or µx.[]x
35 µx.([true]x true) versus µx.[]x b
36 µx.([true]x true) versus µx.[]x b µx.([true]x true) is vlid in the initil stte µx.[]x is not vlid in the initil stte
37 µx.([true]x true) versus µx.[]x b µx.([true]x true) is vlid in the initil stte µx.[]x is not vlid in the initil stte procedure for estblishing vlidity of formul w.r.t. given trnsition system is slightly more complicted only sketched for formuls with only one fixed point symbol
38 Vlidity of miniml fixed point formul µx.φ lbel with subformuls of φ, including X initilly no stte is lbeled with X lbel with ll other strict subformuls from φ when stte is lbeled with φ, it is lso lbeled with X repet from third item until nothing hs chnged w.r.t. previous lbeling µx.φ holds in stte iff it is lbeled with X
39 Exmple Consider the formul µx.( X b true) which expresses tht there is finite sequence of ctions fter which b is possible b true X b true b
40 Exmple Consider the formul µx.( X b true) which expresses tht there is finite sequence of ctions fter which b is possible X X b true b true X b true X, b true X b true b b
41 Exmple Consider the formul µx.( X b true) which expresses tht there is finite sequence of ctions fter which b is possible X, X X b true X, X X b true X X b true X, X X b true b true X b true X, b true X b true X, b true X b true b b b
42 Vlidity of mximl fixed point formul νx.φ similr, but now ll sttes re initilly lbeled with X X is removed from stte if φ does not hold when the lbeling process stbilizes when removing of lbels stbilizes gin, νx.φ is vlid in the sttes lbeled with X
43 Vlidity of mximl fixed point formul νx.φ similr, but now ll sttes re initilly lbeled with X X is removed from stte if φ does not hold when the lbeling process stbilizes when removing of lbels stbilizes gin, νx.φ is vlid in the sttes lbeled with X Exmple: Check νx.([]x true): lwys one more cn be done fter n rbitrry -sequence X, []X true, []X true X, []X true, []X true X, []X
44 Vlidity of mximl fixed point formul νx.φ similr, but now ll sttes re initilly lbeled with X X is removed from stte if φ does not hold when the lbeling process stbilizes when removing of lbels stbilizes gin, νx.φ is vlid in the sttes lbeled with X Exmple: Check νx.([]x true): lwys one more cn be done fter n rbitrry -sequence X, []X true, []X true true X, []X true, []X true X, []X true []X
45 Nested fixed point opertors Firness properties: some event must hppen provided it is unboundedly often enbled, or becuse some other ction hppens only bounded number of times Exmple: from the sttes on ech infinite b-tril, there re only finite number of sttes where -trnsitions re possible µx.νy.(( true [b]x) ( true [b]y ))
46 Modl formuls with dt Modl formuls re extended with dt: modl vribles cn hve rguments ctions cn crry dt rguments existentil nd universl quntifiction is possible f ::= t true flse (t 1,..., t n) f f f f f d : D.f d : D.f R ::= ε f R R R+R R R + φ ::= true flse t φ φ φ φ φ φ φ R φ [R]φ d : D.φ d : D.φ µx(d 1 : D 1 :=t 1,..., d n : D n:=t n).φ νx(d 1 : D 1 :=t 1,..., d n : D n:=t n).φ X(t 1,..., t n) Exmple: whenever n error with some number n is observed, shutdown is inevitble: [true n : IN.error(n)]µX.([shutdown]X true true)
47 mteril from Chpter 6 is tested in written exm modl µ-clculus formuls with nested fixed points re not tested modl µ-clculus formuls with dt re not tested You my use these for the ssignment!
Temporal logic CTL : syntax. Communication and Concurrency Lecture 6. Φ ::= tt ff Φ 1 Φ 2 Φ 1 Φ 2 [K]Φ K Φ AG Φ EF Φ AF Φ EG Φ A formula can be
Temporl logic CTL : syntx Communiction nd Concurrency Lecture 6 Colin Stirling (cps) Φ ::= tt ff Φ 1 Φ Φ 1 Φ [K]Φ K Φ A formul cn be School of Informtics 7th October 013 Temporl logic CTL : syntx Temporl
More informationSoftware Engineering using Formal Methods
Softwre Engineering using Forml Methods Propositionl nd (Liner) Temporl Logic Wolfgng Ahrendt 13th Septemer 2016 SEFM: Liner Temporl Logic /GU 160913 1 / 60 Recpitultion: FormlistionFormlistion: Syntx,
More informationThis lecture covers Chapter 8 of HMU: Properties of CFLs
This lecture covers Chpter 8 of HMU: Properties of CFLs Turing Mchine Extensions of Turing Mchines Restrictions of Turing Mchines Additionl Reding: Chpter 8 of HMU. Turing Mchine: Informl Definition B
More informationLecture 9: LTL and Büchi Automata
Lecture 9: LTL nd Büchi Automt 1 LTL Property Ptterns Quite often the requirements of system follow some simple ptterns. Sometimes we wnt to specify tht property should only hold in certin context, clled
More information1.2. Linear Variable Coefficient Equations. y + b "! = a y + b " Remark: The case b = 0 and a non-constant can be solved with the same idea as above.
1 12 Liner Vrible Coefficient Equtions Section Objective(s): Review: Constnt Coefficient Equtions Solving Vrible Coefficient Equtions The Integrting Fctor Method The Bernoulli Eqution 121 Review: Constnt
More informationFinite Automata-cont d
Automt Theory nd Forml Lnguges Professor Leslie Lnder Lecture # 6 Finite Automt-cont d The Pumping Lemm WEB SITE: http://ingwe.inghmton.edu/ ~lnder/cs573.html Septemer 18, 2000 Exmple 1 Consider L = {ww
More informationNon-Deterministic Finite Automata. Fall 2018 Costas Busch - RPI 1
Non-Deterministic Finite Automt Fll 2018 Costs Busch - RPI 1 Nondeterministic Finite Automton (NFA) Alphbet ={} q q2 1 q 0 q 3 Fll 2018 Costs Busch - RPI 2 Nondeterministic Finite Automton (NFA) Alphbet
More informationHandout: Natural deduction for first order logic
MATH 457 Introduction to Mthemticl Logic Spring 2016 Dr Json Rute Hndout: Nturl deduction for first order logic We will extend our nturl deduction rules for sententil logic to first order logic These notes
More informationChapter 2 Finite Automata
Chpter 2 Finite Automt 28 2.1 Introduction Finite utomt: first model of the notion of effective procedure. (They lso hve mny other pplictions). The concept of finite utomton cn e derived y exmining wht
More informationChapter 0. What is the Lebesgue integral about?
Chpter 0. Wht is the Lebesgue integrl bout? The pln is to hve tutoril sheet ech week, most often on Fridy, (to be done during the clss) where you will try to get used to the ides introduced in the previous
More informationStrong Bisimulation. Overview. References. Actions Labeled transition system Transition semantics Simulation Bisimulation
Strong Bisimultion Overview Actions Lbeled trnsition system Trnsition semntics Simultion Bisimultion References Robin Milner, Communiction nd Concurrency Robin Milner, Communicting nd Mobil Systems 32
More informationImproper Integrals, and Differential Equations
Improper Integrls, nd Differentil Equtions October 22, 204 5.3 Improper Integrls Previously, we discussed how integrls correspond to res. More specificlly, we sid tht for function f(x), the region creted
More informationMain topics for the First Midterm
Min topics for the First Midterm The Midterm will cover Section 1.8, Chpters 2-3, Sections 4.1-4.8, nd Sections 5.1-5.3 (essentilly ll of the mteril covered in clss). Be sure to know the results of the
More informationConvert the NFA into DFA
Convert the NF into F For ech NF we cn find F ccepting the sme lnguge. The numer of sttes of the F could e exponentil in the numer of sttes of the NF, ut in prctice this worst cse occurs rrely. lgorithm:
More informationThe University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER LANGUAGES AND COMPUTATION ANSWERS
The University of Nottinghm SCHOOL OF COMPUTER SCIENCE LEVEL 2 MODULE, SPRING SEMESTER 2016 2017 LNGUGES ND COMPUTTION NSWERS Time llowed TWO hours Cndidtes my complete the front cover of their nswer ook
More informationCS 275 Automata and Formal Language Theory
CS 275 Automt nd Forml Lnguge Theory Course Notes Prt II: The Recognition Problem (II) Chpter II.5.: Properties of Context Free Grmmrs (14) Anton Setzer (Bsed on book drft by J. V. Tucker nd K. Stephenson)
More informationTuring Machines Part One
Turing Mchines Prt One Hello Hello Condensed Condensed Slide Slide Reders! Reders! Tody s Tody s lecture lecture consists consists lmost lmost exclusively exclusively of of nimtions nimtions of of Turing
More informationNondeterminism. Nondeterministic Finite Automata. Example: Moves on a Chessboard. Nondeterminism (2) Example: Chessboard (2) Formal NFA
Nondeterminism Nondeterministic Finite Automt Nondeterminism Subset Construction A nondeterministic finite utomton hs the bility to be in severl sttes t once. Trnsitions from stte on n input symbol cn
More informationFormal Languages and Automata
Moile Computing nd Softwre Engineering p. 1/5 Forml Lnguges nd Automt Chpter 2 Finite Automt Chun-Ming Liu cmliu@csie.ntut.edu.tw Deprtment of Computer Science nd Informtion Engineering Ntionl Tipei University
More information1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true.
York University CSE 2 Unit 3. DFA Clsses Converting etween DFA, NFA, Regulr Expressions, nd Extended Regulr Expressions Instructor: Jeff Edmonds Don t chet y looking t these nswers premturely.. For ech
More informationCMSC 330: Organization of Programming Languages. DFAs, and NFAs, and Regexps (Oh my!)
CMSC 330: Orgniztion of Progrmming Lnguges DFAs, nd NFAs, nd Regexps (Oh my!) CMSC330 Spring 2018 Types of Finite Automt Deterministic Finite Automt (DFA) Exctly one sequence of steps for ech string All
More informationCS 267: Automated Verification. Lecture 8: Automata Theoretic Model Checking. Instructor: Tevfik Bultan
CS 267: Automted Verifiction Lecture 8: Automt Theoretic Model Checking Instructor: Tevfik Bultn LTL Properties Büchi utomt [Vrdi nd Wolper LICS 86] Büchi utomt: Finite stte utomt tht ccept infinite strings
More informationChapter 3. Vector Spaces
3.4 Liner Trnsformtions 1 Chpter 3. Vector Spces 3.4 Liner Trnsformtions Note. We hve lredy studied liner trnsformtions from R n into R m. Now we look t liner trnsformtions from one generl vector spce
More information19 Optimal behavior: Game theory
Intro. to Artificil Intelligence: Dle Schuurmns, Relu Ptrscu 1 19 Optiml behvior: Gme theory Adversril stte dynmics hve to ccount for worst cse Compute policy π : S A tht mximizes minimum rewrd Let S (,
More information63. Representation of functions as power series Consider a power series. ( 1) n x 2n for all 1 < x < 1
3 9. SEQUENCES AND SERIES 63. Representtion of functions s power series Consider power series x 2 + x 4 x 6 + x 8 + = ( ) n x 2n It is geometric series with q = x 2 nd therefore it converges for ll q =
More informationMath 520 Final Exam Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008
Mth 520 Finl Exm Topic Outline Sections 1 3 (Xio/Dums/Liw) Spring 2008 The finl exm will be held on Tuesdy, My 13, 2-5pm in 117 McMilln Wht will be covered The finl exm will cover the mteril from ll of
More informationSWEN 224 Formal Foundations of Programming WITH ANSWERS
T E W H A R E W Ā N A N G A O T E Ū P O K O O T E I K A A M Ā U I VUW V I C T O R I A UNIVERSITY OF WELLINGTON Time Allowed: 3 Hours EXAMINATIONS 2011 END-OF-YEAR SWEN 224 Forml Foundtions of Progrmming
More informationThe Regulated and Riemann Integrals
Chpter 1 The Regulted nd Riemnn Integrls 1.1 Introduction We will consider severl different pproches to defining the definite integrl f(x) dx of function f(x). These definitions will ll ssign the sme vlue
More information1.1. Linear Constant Coefficient Equations. Remark: A differential equation is an equation
1 1.1. Liner Constnt Coefficient Equtions Section Objective(s): Overview of Differentil Equtions. Liner Differentil Equtions. Solving Liner Differentil Equtions. The Initil Vlue Problem. 1.1.1. Overview
More informationKNOWLEDGE-BASED AGENTS INFERENCE
AGENTS THAT REASON LOGICALLY KNOWLEDGE-BASED AGENTS Two components: knowledge bse, nd n inference engine. Declrtive pproch to building n gent. We tell it wht it needs to know, nd It cn sk itself wht to
More informationCSC 473 Automata, Grammars & Languages 11/9/10
CSC 473 utomt, Grmmrs & Lnguges 11/9/10 utomt, Grmmrs nd Lnguges Discourse 06 Decidbility nd Undecidbility Decidble Problems for Regulr Lnguges Theorem 4.1: (embership/cceptnce Prob. for DFs) = {, w is
More informationHennessy-Milner Logic 1.
Hennessy-Milner Logic 1. Colloquium in honor of Robin Milner. Crlos Olrte. Pontifici Universidd Jverin 28 April 2010. 1 Bsed on the tlks: [1,2,3] Prof. Robin Milner (R.I.P). LIX, Ecole Polytechnique. Motivtion
More informationCS 373, Spring Solutions to Mock midterm 1 (Based on first midterm in CS 273, Fall 2008.)
CS 373, Spring 29. Solutions to Mock midterm (sed on first midterm in CS 273, Fll 28.) Prolem : Short nswer (8 points) The nswers to these prolems should e short nd not complicted. () If n NF M ccepts
More informationWorked out examples Finite Automata
Worked out exmples Finite Automt Exmple Design Finite Stte Automton which reds inry string nd ccepts only those tht end with. Since we re in the topic of Non Deterministic Finite Automt (NFA), we will
More informationChapter 4 Regular Grammar and Regular Sets. (Solutions / Hints)
C K Ngpl Forml Lnguges nd utomt Theory Chpter 4 Regulr Grmmr nd Regulr ets (olutions / Hints) ol. (),,,,,,,,,,,,,,,,,,,,,,,,,, (),, (c) c c, c c, c, c, c c, c, c, c, c, c, c, c c,c, c, c, c, c, c, c, c,
More informationExam 2, Mathematics 4701, Section ETY6 6:05 pm 7:40 pm, March 31, 2016, IH-1105 Instructor: Attila Máté 1
Exm, Mthemtics 471, Section ETY6 6:5 pm 7:4 pm, Mrch 1, 16, IH-115 Instructor: Attil Máté 1 17 copies 1. ) Stte the usul sufficient condition for the fixed-point itertion to converge when solving the eqution
More informationProbabilistic Model Checking Michaelmas Term Dr. Dave Parker. Department of Computer Science University of Oxford
Probbilistic Model Checking Michelms Term 2011 Dr. Dve Prker Deprtment of Computer Science University of Oxford Long-run properties Lst lecture: regulr sfety properties e.g. messge filure never occurs
More informationFinite Automata. Informatics 2A: Lecture 3. John Longley. 22 September School of Informatics University of Edinburgh
Lnguges nd Automt Finite Automt Informtics 2A: Lecture 3 John Longley School of Informtics University of Edinburgh jrl@inf.ed.c.uk 22 September 2017 1 / 30 Lnguges nd Automt 1 Lnguges nd Automt Wht is
More informationFinite Automata. Informatics 2A: Lecture 3. Mary Cryan. 21 September School of Informatics University of Edinburgh
Finite Automt Informtics 2A: Lecture 3 Mry Cryn School of Informtics University of Edinburgh mcryn@inf.ed.c.uk 21 September 2018 1 / 30 Lnguges nd Automt Wht is lnguge? Finite utomt: recp Some forml definitions
More information20 MATHEMATICS POLYNOMIALS
0 MATHEMATICS POLYNOMIALS.1 Introduction In Clss IX, you hve studied polynomils in one vrible nd their degrees. Recll tht if p(x) is polynomil in x, the highest power of x in p(x) is clled the degree of
More informationFABER Formal Languages, Automata and Models of Computation
DVA337 FABER Forml Lnguges, Automt nd Models of Computtion Lecture 5 chool of Innovtion, Design nd Engineering Mälrdlen University 2015 1 Recp of lecture 4 y definition suset construction DFA NFA stte
More informationDIRECT CURRENT CIRCUITS
DRECT CURRENT CUTS ELECTRC POWER Consider the circuit shown in the Figure where bttery is connected to resistor R. A positive chrge dq will gin potentil energy s it moves from point to point b through
More informationNotes on specifying systems in EST
Robert Meolic, Ttjn Kpus: Notes on specifying systems in EST 1 Notes on specifying systems in EST Robert Meolic, Ttjn Kpus Fculty of EE & CS University of Mribor Robert Meolic, Ttjn Kpus: Notes on specifying
More informationCS415 Compilers. Lexical Analysis and. These slides are based on slides copyrighted by Keith Cooper, Ken Kennedy & Linda Torczon at Rice University
CS415 Compilers Lexicl Anlysis nd These slides re sed on slides copyrighted y Keith Cooper, Ken Kennedy & Lind Torczon t Rice University First Progrmming Project Instruction Scheduling Project hs een posted
More informationNon Deterministic Automata. Linz: Nondeterministic Finite Accepters, page 51
Non Deterministic Automt Linz: Nondeterministic Finite Accepters, pge 51 1 Nondeterministic Finite Accepter (NFA) Alphbet ={} q 1 q2 q 0 q 3 2 Nondeterministic Finite Accepter (NFA) Alphbet ={} Two choices
More informationMyhill-Nerode Theorem
Overview Myhill-Nerode Theorem Correspondence etween DA s nd MN reltions Cnonicl DA for L Computing cnonicl DFA Myhill-Nerode Theorem Deepk D Souz Deprtment of Computer Science nd Automtion Indin Institute
More informationAssignment 1 Automata, Languages, and Computability. 1 Finite State Automata and Regular Languages
Deprtment of Computer Science, Austrlin Ntionl University COMP2600 Forml Methods for Softwre Engineering Semester 2, 206 Assignment Automt, Lnguges, nd Computility Smple Solutions Finite Stte Automt nd
More informationCS:4330 Theory of Computation Spring Regular Languages. Equivalences between Finite automata and REs. Haniel Barbosa
CS:4330 Theory of Computtion Spring 208 Regulr Lnguges Equivlences between Finite utomt nd REs Hniel Brbos Redings for this lecture Chpter of [Sipser 996], 3rd edition. Section.3. Finite utomt nd regulr
More informationSOLUTIONS FOR ADMISSIONS TEST IN MATHEMATICS, COMPUTER SCIENCE AND JOINT SCHOOLS WEDNESDAY 5 NOVEMBER 2014
SOLUTIONS FOR ADMISSIONS TEST IN MATHEMATICS, COMPUTER SCIENCE AND JOINT SCHOOLS WEDNESDAY 5 NOVEMBER 014 Mrk Scheme: Ech prt of Question 1 is worth four mrks which re wrded solely for the correct nswer.
More informationLecture 1. Functional series. Pointwise and uniform convergence.
1 Introduction. Lecture 1. Functionl series. Pointwise nd uniform convergence. In this course we study mongst other things Fourier series. The Fourier series for periodic function f(x) with period 2π is
More informationFinite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018
Finite Automt Theory nd Forml Lnguges TMV027/DIT321 LP4 2018 Lecture 10 An Bove April 23rd 2018 Recp: Regulr Lnguges We cn convert between FA nd RE; Hence both FA nd RE ccept/generte regulr lnguges; More
More informationFor convenience, we rewrite m2 s m2 = m m m ; where m is repeted m times. Since xyz = m m m nd jxyj»m, we hve tht the string y is substring of the fir
CSCI 2400 Models of Computtion, Section 3 Solutions to Homework 4 Problem 1. ll the solutions below refer to the Pumping Lemm of Theorem 4.8, pge 119. () L = f n b l k : k n + lg Let's ssume for contrdiction
More informationHomework 3 Solutions
CS 341: Foundtions of Computer Science II Prof. Mrvin Nkym Homework 3 Solutions 1. Give NFAs with the specified numer of sttes recognizing ech of the following lnguges. In ll cses, the lphet is Σ = {,1}.
More informationNFAs and Regular Expressions. NFA-ε, continued. Recall. Last class: Today: Fun:
CMPU 240 Lnguge Theory nd Computtion Spring 2019 NFAs nd Regulr Expressions Lst clss: Introduced nondeterministic finite utomt with -trnsitions Tody: Prove n NFA- is no more powerful thn n NFA Introduce
More information5.1 Definitions and Examples 5.2 Deterministic Pushdown Automata
CSC4510 AUTOMATA 5.1 Definitions nd Exmples 5.2 Deterministic Pushdown Automt Definitions nd Exmples A lnguge cn be generted by CFG if nd only if it cn be ccepted by pushdown utomton. A pushdown utomton
More informationTaylor Polynomial Inequalities
Tylor Polynomil Inequlities Ben Glin September 17, 24 Abstrct There re instnces where we my wish to pproximte the vlue of complicted function round given point by constructing simpler function such s polynomil
More informationTuring Machines Part One
Turing Mchines Prt One Wht problems cn we solve with computer? Regulr Lnguges CFLs Lnguges recognizble by ny fesible computing mchine All Lnguges Tht sme drwing, to scle. All Lnguges The Problem Finite
More informationMinimal DFA. minimal DFA for L starting from any other
Miniml DFA Among the mny DFAs ccepting the sme regulr lnguge L, there is exctly one (up to renming of sttes) which hs the smllest possile numer of sttes. Moreover, it is possile to otin tht miniml DFA
More informationIndefinite Integral. Chapter Integration - reverse of differentiation
Chpter Indefinite Integrl Most of the mthemticl opertions hve inverse opertions. The inverse opertion of differentition is clled integrtion. For exmple, describing process t the given moment knowing the
More informationCS 330 Formal Methods and Models
CS 330 Forml Methods nd Models Dn Richrds, George Mson University, Spring 2017 Quiz Solutions Quiz 1, Propositionl Logic Dte: Ferury 2 1. Prove ((( p q) q) p) is tutology () (3pts) y truth tle. p q p q
More informationCSCI FOUNDATIONS OF COMPUTER SCIENCE
1 CSCI- 2200 FOUNDATIONS OF COMPUTER SCIENCE Spring 2015 My 7, 2015 2 Announcements Homework 9 is due now. Some finl exm review problems will be posted on the web site tody. These re prcqce problems not
More informationBases for Vector Spaces
Bses for Vector Spces 2-26-25 A set is independent if, roughly speking, there is no redundncy in the set: You cn t uild ny vector in the set s liner comintion of the others A set spns if you cn uild everything
More informationAnatomy of a Deterministic Finite Automaton. Deterministic Finite Automata. A machine so simple that you can understand it in less than one minute
Victor Admchik Dnny Sletor Gret Theoreticl Ides In Computer Science CS 5-25 Spring 2 Lecture 2 Mr 3, 2 Crnegie Mellon University Deterministic Finite Automt Finite Automt A mchine so simple tht you cn
More informationLecture 6 Regular Grammars
Lecture 6 Regulr Grmmrs COT 4420 Theory of Computtion Section 3.3 Grmmr A grmmr G is defined s qudruple G = (V, T, S, P) V is finite set of vribles T is finite set of terminl symbols S V is specil vrible
More informationp(t) dt + i 1 re it ireit dt =
Note: This mteril is contined in Kreyszig, Chpter 13. Complex integrtion We will define integrls of complex functions long curves in C. (This is bit similr to [relvlued] line integrls P dx + Q dy in R2.)
More informationCS 311 Homework 3 due 16:30, Thursday, 14 th October 2010
CS 311 Homework 3 due 16:30, Thursdy, 14 th Octoer 2010 Homework must e sumitted on pper, in clss. Question 1. [15 pts.; 5 pts. ech] Drw stte digrms for NFAs recognizing the following lnguges:. L = {w
More informationLecture 3. Limits of Functions and Continuity
Lecture 3 Limits of Functions nd Continuity Audrey Terrs April 26, 21 1 Limits of Functions Notes I m skipping the lst section of Chpter 6 of Lng; the section bout open nd closed sets We cn probbly live
More informationIntroduction to Group Theory
Introduction to Group Theory Let G be n rbitrry set of elements, typiclly denoted s, b, c,, tht is, let G = {, b, c, }. A binry opertion in G is rule tht ssocites with ech ordered pir (,b) of elements
More information1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true.
York University CSE 2 Unit 3. DFA Clsses Converting etween DFA, NFA, Regulr Expressions, nd Extended Regulr Expressions Instructor: Jeff Edmonds Don t chet y looking t these nswers premturely.. For ech
More informationReasoning and programming. Lecture 5: Invariants and Logic. Boolean expressions. Reasoning. Examples
Chir of Softwre Engineering Resoning nd progrmming Einführung in die Progrmmierung Introduction to Progrmming Prof. Dr. Bertrnd Meyer Octoer 2006 Ferury 2007 Lecture 5: Invrints nd Logic Logic is the sis
More informationCS 275 Automata and Formal Language Theory
CS 275 Automt nd Forml Lnguge Theory Course Notes Prt II: The Recognition Problem (II) Chpter II.6.: Push Down Automt Remrk: This mteril is no longer tught nd not directly exm relevnt Anton Setzer (Bsed
More informationEXPRESSING MOBILE AMBIENTS IN TEMPORAL LOGIC OF ACTIONS
THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 5, Number /04, pp 95 04 EXPRESSING MOBILE AMBIENTS IN TEMPORAL LOGIC OF ACTIONS Bogdn AMAN, Gbriel CIOBANU
More informationA BRIEF INTRODUCTION TO UNIFORM CONVERGENCE. In the study of Fourier series, several questions arise naturally, such as: c n e int
A BRIEF INTRODUCTION TO UNIFORM CONVERGENCE HANS RINGSTRÖM. Questions nd exmples In the study of Fourier series, severl questions rise nturlly, such s: () (2) re there conditions on c n, n Z, which ensure
More informationLine Integrals. Chapter Definition
hpter 2 Line Integrls 2.1 Definition When we re integrting function of one vrible, we integrte long n intervl on one of the xes. We now generlize this ide by integrting long ny curve in the xy-plne. It
More informationLet's start with an example:
Finite Automt Let's strt with n exmple: Here you see leled circles tht re sttes, nd leled rrows tht re trnsitions. One of the sttes is mrked "strt". One of the sttes hs doule circle; this is terminl stte
More informationState space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies
Stte spce systems nlysis (continued) Stbility A. Definitions A system is sid to be Asymptoticlly Stble (AS) when it stisfies ut () = 0, t > 0 lim xt () 0. t A system is AS if nd only if the impulse response
More informationand that at t = 0 the object is at position 5. Find the position of the object at t = 2.
7.2 The Fundmentl Theorem of Clculus 49 re mny, mny problems tht pper much different on the surfce but tht turn out to be the sme s these problems, in the sense tht when we try to pproimte solutions we
More information3 Regular expressions
3 Regulr expressions Given n lphet Σ lnguge is set of words L Σ. So fr we were le to descrie lnguges either y using set theory (i.e. enumertion or comprehension) or y n utomton. In this section we shll
More informationUnit #9 : Definite Integral Properties; Fundamental Theorem of Calculus
Unit #9 : Definite Integrl Properties; Fundmentl Theorem of Clculus Gols: Identify properties of definite integrls Define odd nd even functions, nd reltionship to integrl vlues Introduce the Fundmentl
More informationReinforcement learning II
CS 1675 Introduction to Mchine Lerning Lecture 26 Reinforcement lerning II Milos Huskrecht milos@cs.pitt.edu 5329 Sennott Squre Reinforcement lerning Bsics: Input x Lerner Output Reinforcement r Critic
More informationLecture 09: Myhill-Nerode Theorem
CS 373: Theory of Computtion Mdhusudn Prthsrthy Lecture 09: Myhill-Nerode Theorem 16 Ferury 2010 In this lecture, we will see tht every lnguge hs unique miniml DFA We will see this fct from two perspectives
More informationCS 301. Lecture 04 Regular Expressions. Stephen Checkoway. January 29, 2018
CS 301 Lecture 04 Regulr Expressions Stephen Checkowy Jnury 29, 2018 1 / 35 Review from lst time NFA N = (Q, Σ, δ, q 0, F ) where δ Q Σ P (Q) mps stte nd n lphet symol (or ) to set of sttes We run n NFA
More informationLecture 3 ( ) (translated and slightly adapted from lecture notes by Martin Klazar)
Lecture 3 (5.3.2018) (trnslted nd slightly dpted from lecture notes by Mrtin Klzr) Riemnn integrl Now we define precisely the concept of the re, in prticulr, the re of figure U(, b, f) under the grph of
More informationCSCI 340: Computational Models. Transition Graphs. Department of Computer Science
CSCI 340: Computtionl Models Trnsition Grphs Chpter 6 Deprtment of Computer Science Relxing Restrints on Inputs We cn uild n FA tht ccepts only the word! 5 sttes ecuse n FA cn only process one letter t
More informationCS 310 (sec 20) - Winter Final Exam (solutions) SOLUTIONS
CS 310 (sec 20) - Winter 2003 - Finl Exm (solutions) SOLUTIONS 1. (Logic) Use truth tles to prove the following logicl equivlences: () p q (p p) (q q) () p q (p q) (p q) () p q p q p p q q (q q) (p p)
More informationCSCI 340: Computational Models. Kleene s Theorem. Department of Computer Science
CSCI 340: Computtionl Models Kleene s Theorem Chpter 7 Deprtment of Computer Science Unifiction In 1954, Kleene presented (nd proved) theorem which (in our version) sttes tht if lnguge cn e defined y ny
More informationCS375: Logic and Theory of Computing
CS375: Logic nd Theory of Computing Fuhu (Frnk) Cheng Deprtment of Computer Science University of Kentucky 1 Tble of Contents: Week 1: Preliminries (set lgebr, reltions, functions) (red Chpters 1-4) Weeks
More information2.4 Linear Inequalities and Problem Solving
Section.4 Liner Inequlities nd Problem Solving 77.4 Liner Inequlities nd Problem Solving S 1 Use Intervl Nottion. Solve Liner Inequlities Using the Addition Property of Inequlity. 3 Solve Liner Inequlities
More informationFormal Methods in Software Engineering
Forml Methods in Softwre Engineering Lecture 09 orgniztionl issues Prof. Dr. Joel Greenyer Decemer 9, 2014 Written Exm The written exm will tke plce on Mrch 4 th, 2015 The exm will tke 60 minutes nd strt
More informationIntroduction to Electronic Circuits. DC Circuit Analysis: Transient Response of RC Circuits
Introduction to Electronic ircuits D ircuit Anlysis: Trnsient esponse of ircuits Up until this point, we hve een looking t the Stedy Stte response of D circuits. StedyStte implies tht nothing hs chnged
More informationContents. Bibliography 25
Contents 1 Bisimultion nd Logic pge 2 1.1 Introduction........................................................ 2 1.2 Modl logic nd bisimilrity......................................... 4 1.3 Bisimultion
More informationc n φ n (x), 0 < x < L, (1) n=1
SECTION : Fourier Series. MATH4. In section 4, we will study method clled Seprtion of Vribles for finding exct solutions to certin clss of prtil differentil equtions (PDEs. To do this, it will be necessry
More informationThe Evaluation Theorem
These notes closely follow the presenttion of the mteril given in Jmes Stewrt s textook Clculus, Concepts nd Contexts (2nd edition) These notes re intended primrily for in-clss presenttion nd should not
More information1 The Riemann Integral
The Riemnn Integrl. An exmple leding to the notion of integrl (res) We know how to find (i.e. define) the re of rectngle (bse height), tringle ( (sum of res of tringles). But how do we find/define n re
More informationNon-Deterministic Finite Automata
Non-Deterministic Finite Automt http://users.comlb.ox.c.uk/luke. ong/teching/moc/nf2up.pdf 1 Nondeterministic Finite Automton (NFA) Alphbet ={} q1 q2 2 Alphbet ={} Two choices q1 q2 3 Alphbet ={} Two choices
More informationAdministrivia CSE 190: Reinforcement Learning: An Introduction
Administrivi CSE 190: Reinforcement Lerning: An Introduction Any emil sent to me bout the course should hve CSE 190 in the subject line! Chpter 4: Dynmic Progrmming Acknowledgment: A good number of these
More informationLecture 08: Feb. 08, 2019
4CS4-6:Theory of Computtion(Closure on Reg. Lngs., regex to NDFA, DFA to regex) Prof. K.R. Chowdhry Lecture 08: Fe. 08, 2019 : Professor of CS Disclimer: These notes hve not een sujected to the usul scrutiny
More informationAnonymous Math 361: Homework 5. x i = 1 (1 u i )
Anonymous Mth 36: Homewor 5 Rudin. Let I be the set of ll u (u,..., u ) R with u i for ll i; let Q be the set of ll x (x,..., x ) R with x i, x i. (I is the unit cube; Q is the stndrd simplex in R ). Define
More informationChapter 4 Contravariance, Covariance, and Spacetime Diagrams
Chpter 4 Contrvrince, Covrince, nd Spcetime Digrms 4. The Components of Vector in Skewed Coordintes We hve seen in Chpter 3; figure 3.9, tht in order to show inertil motion tht is consistent with the Lorentz
More informationAutomata, Games, and Verification
Automt, Gmes, nd Verifiction Prof. Bernd Finkbeiner, Ph.D. Srlnd University Summer Term 2015 Lecture Notes by Bernd Finkbeiner, Felix Klein, Tobis Slzmnn These lecture notes re working document nd my contin
More information