On the Origins of Bisimulation and Coinduction
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CTL Model Checking
CTL Model Checking Prof. P.H. Schmitt Institut fur¨ Theoretische Informatik Fakult¨at fur¨ Informatik Universit¨at Karlsruhe (TH) Formal Systems II Prof. P.H. Schmitt CTLMC Summer 2009 1 / 26 Fixed Point Theory Prof. P.H. Schmitt CTLMC Summer 2009 2 / 26 Fixed Points Definition Definition Let f : P(G) → P(G) be a set valued function and Z a subset of G. 1. Z is called a fixed point of f if f(Z) = Z . 2. Z is called the least fixed point of f is Z is a fixed point and for all other fixed points U of f the relation Z ⊆ U is true. 3. Z is called the greatest fixed point of f is Z is a fixed point and for all other fixed points U of f the relation U ⊆ Z is true. Prof. P.H. Schmitt CTLMC Summer 2009 3 / 26 Finite Fixed Point Lemma Let G be an arbitrary set, Let P(G) denote the power set of a set G. A function f : P(G) → P(G) is called monotone of for all X, Y ⊆ G X ⊆ Y ⇒ f(X) ⊆ f(Y ) Let f : P(G) → P(G) be a monotone function on a finite set G. 1. There is a least and a greatest fixed point of f. S n 2. n≥1 f (∅) is the least fixed point of f. T n 3. n≥1 f (G) is the greatest fixed point of f. Prof. P.H. Schmitt CTLMC Summer 2009 4 / 26 Proof of part (2) of the finite fixed point Lemma Monotonicity of f yields ∅ ⊆ f(∅) ⊆ f 2(∅) ⊆ .. -
The Joys of Bisimulation
The Joys of Bisimulation Colin Stirling Department of Computer Science University of Edinburgh Edinburgh EH JZ UK email cpsdcsedacuk Intro duction Bisimulation is a rich concept which app ears in various areas of theoretical com puter science Its origins lie in concurrency theory for instance see Milner and in mo dal logic see for example van Benthem In this pap er we review results ab out bisimulation from b oth the p oint of view of automata and from a logical p oint of view We also consider how bisimulation has a role in nite mo del theory and we oer a new undenability result Basics Lab elled transition systems are commonly encountered in op erational semantics of programs and systems They are just lab elled graphs A transition system a is a pair T S f a Ag where S is a nonempty set of states A is a a nonempty set of lab els and for each a L is a binary relation on S a a We write s s instead of s s Sometimes there is extra structure in a transition system a set of atomic colours Q such that each colour q S the subset of states with colour q Bisimulations were intro duced by Park as a small renement of the b e havioural equivalence dened by Hennessy and Milner in b etween basic CCS pro cesses whose b ehaviour is a transition system Denition A binary relation R b etween states of a transition system is a bisimulation just in case whenever s t R and a A a a if s s then t t for some t such that s t R and a a if t t then s s for some s such that s t R In the case of an enriched transition system -
A Networked Robotic System and Its Use in an Oil Spill Monitoring Exercise
A Networked Robotic System and its Use in an Oil Spill Monitoring Exercise El´oiPereira∗† Co-authors: Pedro Silvay, Clemens Krainerz, Christoph Kirschz, Jos´eMorgadoy, and Raja Sengupta∗ cpcc.berkeley.edu, eloipereira.com [email protected] Swarm at the Edge of the Could - ESWeek'13, Montreal, Canada September 29, 2013 ∗ - Systems Engineering, UC Berkeley y - Research Center, Portuguese Air Force Academy z - Computer Science Dept., University of Salzburg Programming the Ubiquitous World • In networked mobile systems (e.g. teams of robots, smartphones, etc.) the location and connectivity of \machines" may vary during the execution of its \programs" (computation specifications) • We investigate models for bridging \programs" and \machines" with dynamic structure (location and connectivity) • BigActors [PKSBdS13, PPKS13, PS13] are actors [Agh86] hosted by entities of the physical structure denoted as bigraph nodes [Mil09] E. Pereira, C. M. Kirsch, R. Sengupta, and J. B. de Sousa, \Bigactors - A Model for Structure-aware Computation," in ACM/IEEE 4th International Conference on Cyber-Physical Systems, 2013, pp. 199-208. Case study: Oil spill monitoring scenario • \Bilge dumping" is an environmental problem of great relevance for countries with large area of jurisdictional waters • EC created the European Maritime Safety Agency to \...prevent and respond to pollution by ships within the EU" • How to use networked robotics to monitor and take evidences of \bilge dumping" Figure: Portuguese Jurisdiction waters and evidences of \Bilge dumping". -
Lecture Notes: Axiomatic Set Theory
Lecture Notes: Axiomatic Set Theory Asaf Karagila Last Update: May 14, 2018 Contents 1 Introduction 3 1.1 Why do we need axioms?...............................3 1.2 Classes and sets.....................................4 1.3 The axioms of set theory................................5 2 Ordinals, recursion and induction7 2.1 Ordinals.........................................8 2.2 Transfinite induction and recursion..........................9 2.3 Transitive classes.................................... 10 3 The relative consistency of the Axiom of Foundation 12 4 Cardinals and their arithmetic 15 4.1 The definition of cardinals............................... 15 4.2 The Aleph numbers.................................. 17 4.3 Finiteness........................................ 18 5 Absoluteness and reflection 21 5.1 Absoluteness...................................... 21 5.2 Reflection........................................ 23 6 The Axiom of Choice 25 6.1 The Axiom of Choice.................................. 25 6.2 Weak version of the Axiom of Choice......................... 27 7 Sets of Ordinals 31 7.1 Cofinality........................................ 31 7.2 Some cardinal arithmetic............................... 32 7.3 Clubs and stationary sets............................... 33 7.4 The Club filter..................................... 35 8 Inner models of ZF 37 8.1 Inner models...................................... 37 8.2 Gödel’s constructible universe............................. 39 1 8.3 The properties of L ................................... 41 8.4 Ordinal definable sets................................. 42 9 Some combinatorics on ω1 43 9.1 Aronszajn trees..................................... 43 9.2 Diamond and Suslin trees............................... 44 10 Coda: Games and determinacy 46 2 Chapter 1 Introduction 1.1 Why do we need axioms? In modern mathematics, axioms are given to define an object. The axioms of a group define the notion of a group, the axioms of a Banach space define what it means for something to be a Banach space. -
The Space and Motion of Communicating Agents
The space and motion of communicating agents Robin Milner December 1, 2008 ii to my family: Lucy, Barney, Chloe,¨ and in memory of Gabriel iv Contents Prologue vii Part I : Space 1 1 The idea of bigraphs 3 2 Defining bigraphs 15 2.1 Bigraphsandtheirassembly . 15 2.2 Mathematicalframework . 19 2.3 Bigraphicalcategories . 24 3 Algebra for bigraphs 27 3.1 Elementarybigraphsandnormalforms. 27 3.2 Derivedoperations ........................... 31 4 Relative and minimal bounds 37 5 Bigraphical structure 43 5.1 RPOsforbigraphs............................ 43 5.2 IPOsinbigraphs ............................ 48 5.3 AbstractbigraphslackRPOs . 53 6 Sorting 55 6.1 PlacesortingandCCS ......................... 55 6.2 Linksorting,arithmeticnetsandPetrinets . ..... 60 6.3 Theimpactofsorting .......................... 64 Part II : Motion 66 7 Reactions and transitions 67 7.1 Reactivesystems ............................ 68 7.2 Transitionsystems ........................... 71 7.3 Subtransitionsystems .. .... ... .... .... .... .... 76 v vi CONTENTS 7.4 Abstracttransitionsystems . 77 8 Bigraphical reactive systems 81 8.1 DynamicsforaBRS .......................... 82 8.2 DynamicsforaniceBRS.... ... .... .... .... ... .. 87 9 Behaviour in link graphs 93 9.1 Arithmeticnets ............................. 93 9.2 Condition-eventnets .. .... ... .... .... .... ... .. 95 10 Behavioural theory for CCS 103 10.1 SyntaxandreactionsforCCSinbigraphs . 103 10.2 TransitionsforCCSinbigraphs . 107 Part III : Development 113 11 Further topics 115 11.1Tracking................................ -
The Origins of Structural Operational Semantics
The Origins of Structural Operational Semantics Gordon D. Plotkin Laboratory for Foundations of Computer Science, School of Informatics, University of Edinburgh, King’s Buildings, Edinburgh EH9 3JZ, Scotland I am delighted to see my Aarhus notes [59] on SOS, Structural Operational Semantics, published as part of this special issue. The notes already contain some historical remarks, but the reader may be interested to know more of the personal intellectual context in which they arose. I must straightaway admit that at this distance in time I do not claim total accuracy or completeness: what I write should rather be considered as a reconstruction, based on (possibly faulty) memory, papers, old notes and consultations with colleagues. As a postgraduate I learnt the untyped λ-calculus from Rod Burstall. I was further deeply impressed by the work of Peter Landin on the semantics of pro- gramming languages [34–37] which includes his abstract SECD machine. One should also single out John McCarthy’s contributions [45–48], which include his 1962 introduction of abstract syntax, an essential tool, then and since, for all approaches to the semantics of programming languages. The IBM Vienna school [42, 41] were interested in specifying real programming languages, and, in particular, worked on an abstract interpreting machine for PL/I using VDL, their Vienna Definition Language; they were influenced by the ideas of McCarthy, Landin and Elgot [18]. I remember attending a seminar at Edinburgh where the intricacies of their PL/I abstract machine were explained. The states of these machines are tuples of various kinds of complex trees and there is also a stack of environments; the transition rules involve much tree traversal to access syntactical control points, handle jumps, and to manage concurrency. -
Curriculum Vitae Thomas A
Curriculum Vitae Thomas A. Henzinger February 6, 2021 Address IST Austria (Institute of Science and Technology Austria) Phone: +43 2243 9000 1033 Am Campus 1 Fax: +43 2243 9000 2000 A-3400 Klosterneuburg Email: [email protected] Austria Web: pub.ist.ac.at/~tah Research Mathematical logic, automata and game theory, models of computation. Analysis of reactive, stochastic, real-time, and hybrid systems. Formal software and hardware verification, especially model checking. Design and implementation of concurrent and embedded software. Executable modeling of biological systems. Education September 1991 Ph.D., Computer Science Stanford University July 1987 Dipl.-Ing., Computer Science Kepler University, Linz August 1986 M.S., Computer and Information Sciences University of Delaware Employment September 2009 President IST Austria April 2004 to Adjunct Professor, University of California, June 2011 Electrical Engineering and Computer Sciences Berkeley April 2004 to Professor, EPFL August 2009 Computer and Communication Sciences January 1999 to Director Max-Planck Institute March 2000 for Computer Science, Saarbr¨ucken July 1998 to Professor, University of California, March 2004 Electrical Engineering and Computer Sciences Berkeley July 1997 to Associate Professor, University of California, June 1998 Electrical Engineering and Computer Sciences Berkeley January 1996 to Assistant Professor, University of California, June 1997 Electrical Engineering and Computer Sciences Berkeley January 1992 to Assistant Professor, Cornell University December 1996 Computer Science October 1991 to Postdoctoral Scientist, Universit´eJoseph Fourier, December 1991 IMAG Laboratory Grenoble 1 Honors Member, US National Academy of Sciences, 2020. Member, American Academy of Arts and Sciences, 2020. ESWEEK (Embedded Systems Week) Test-of-Time Award, 2020. LICS (Logic in Computer Science) Test-of-Time Award, 2020. -
Least Fixed Points Revisited
LEAST FIXED POINTS REVISITED J.W. de Bakker Mathematical Centre, Amsterdam ABSTRACT Parameter mechanisms for recursive procedures are investigated. Con- trary to the view of Manna et al., it is argued that both call-by-value and call-by-name mechanisms yield the least fixed points of the functionals de- termined by the bodies of the procedures concerned. These functionals dif- fer, however, according to the mechanism chosen. A careful and detailed pre- sentation of this result is given, along the lines of a simple typed lambda calculus, with interpretation rules modelling program execution in such a way that call-by-value determines a change in the environment and call-by- name a textual substitution in the procedure body. KEY WORDS AND PHRASES: Semantics, recursion, least fixed points, parameter mechanisms, call-by-value, call-by-name, lambda calculus. Author's address Mathematical Centre 2e Boerhaavestraat 49 Amsterdam Netherlands 28 NOTATION Section 2 s,t,ti,t',... individual } function terms S,T,... functional p,p',... !~individual} boolean .J function terms P,... functional a~A, aeA} constants b r B, IBeB x,y,z,u X, ~,~ X] variables qcQ, x~0. J r149149 procedure symbols T(t I ..... tn), ~(tl,.-.,tn) ~ application T(TI' '~r)' P(~I' '~r)J ~x I . .xs I . .Ym.t, ~x] . .xs I . .ym.p, abstraction h~l ...~r.T, hX1 ..Xr.~ if p then t' else t"~ [ selection if p then p' else p"J n(T), <n(T),r(r)> rank 1 -< i <- n, 1 -< j -< r,) indices 1 -< h -< l, 1 _< k <- t,T,x,~, . -
Purely Functional Data Structures
Purely Functional Data Structures Chris Okasaki September 1996 CMU-CS-96-177 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Thesis Committee: Peter Lee, Chair Robert Harper Daniel Sleator Robert Tarjan, Princeton University Copyright c 1996 Chris Okasaki This research was sponsored by the Advanced Research Projects Agency (ARPA) under Contract No. F19628- 95-C-0050. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of ARPA or the U.S. Government. Keywords: functional programming, data structures, lazy evaluation, amortization For Maria Abstract When a C programmer needs an efficient data structure for a particular prob- lem, he or she can often simply look one up in any of a number of good text- books or handbooks. Unfortunately, programmers in functional languages such as Standard ML or Haskell do not have this luxury. Although some data struc- tures designed for imperative languages such as C can be quite easily adapted to a functional setting, most cannot, usually because they depend in crucial ways on as- signments, which are disallowed, or at least discouraged, in functional languages. To address this imbalance, we describe several techniques for designing functional data structures, and numerous original data structures based on these techniques, including multiple variations of lists, queues, double-ended queues, and heaps, many supporting more exotic features such as random access or efficient catena- tion. In addition, we expose the fundamental role of lazy evaluation in amortized functional data structures. -
The Bristol Model (5/5)
The Bristol model (5/5) Asaf Karagila University of East Anglia 22 November 2019 RIMS Set Theory Workshop 2019 Set Theory and Infinity Asaf Karagila (UEA) The Bristol model (5/5) 22 November 2019 1 / 23 Review Bird’s eye view of Bristol We started in L by choosing a Bristol sequence, that is a sequence of permutable families and permutable scales, and we constructed one step at a time models, Mα, such that: 1 M0 = L. Mα Mα+1 2 Mα+1 is a symmetric extension of Mα, and Vω+α = Vω+α . 3 For limit α, Mα is the finite support limit of the iteration, and Mα S Mβ Vω+α = β<α Vω+β. By choosing our Mα-generic filters correctly, we ensured that Mα ⊆ L[%0], where %0 was an L-generic Cohen real. We then defined M, the Bristol model, S S Mα as α∈Ord Mα = α∈Ord Vω+α. Asaf Karagila (UEA) The Bristol model (5/5) 22 November 2019 2 / 23 Properties of the Bristol model Small Violations of Choice Definition (Blass) We say that V |= SVC(X) if for every A there is an ordinal η and a surjection f : X × η → A. We write SVC to mean ∃X SVC(X). Theorem If M = V (x) where V |= ZFC, then M |= SVC. Theorem If M is a symmetric extension of V |= ZFC, Then M |= SVC. The following are equivalent: 1 SVC. 2 1 ∃X such that X<ω AC. 3 ∃P such that 1 P AC. 4 V is a symmetric extension of a model of ZFC (Usuba). -
Bisimulations and the Standard Translation
1 Philosophy 244: Bisimulations and the Standard Translation Guest Lecture:Cosmo Grant,October 26, 2016 The Plan Sources: Modal Logic for Open Minds, by Johan van Benthem, and Modal Logic, by Blackburn, de Rijke and Venema. We’ll look at some more metatheory of propositional modal logic. Our They’re superb—check them out! previous work on propositional modal logic focused on frames. Today we’ll focus on models. The central new concept is that of a bisimulation between two models. We’ll use bisimulations to prove some results Bisimulations, under the name p- about the expressive power of modal logic. And bisimulations turn out relations, were introduced by Johan van Benthem in his PhD thesis. to be exactly what we need to connect modal logic with first-order logic. Here’s the plan: 1. Bisimulations: definition and examples 2. Bisimulations and modal equivalence 3. Bisimulations and expressive power 4. Bisimulations and the Standard Translation Bisimulations: definition and examples Definition 1 (Modal equivalence). Let’s say that (M, w), (M0, w0) are 0 0 0 0 modally equivalent just if for every formula f, (M, w) j= f iff (M , w ) j= notation: (M, w) ! (M , w ) f. 0 0 When are (M, w), (M , w ) modally equivalent? Well, if there is an iso- A bijection f : W ! W0 is an isomorphism 0 morphism f : W ! W0 then (M, w), (M0, w0) will certainly be modally from M to M just if for all w, v in M: ( ) equivalent. But an isomorphism is over-kill: it guarantees modal 1. w and f w satisfy the same proposi- tion letters equivalence but in a heavy-handed way. -
Domain Theory: an Introduction
Domain Theory: An Introduction Robert Cartwright Rebecca Parsons Rice University This monograph is an unauthorized revision of “Lectures On A Mathematical Theory of Computation” by Dana Scott [3]. Scott’s monograph uses a formulation of domains called neighborhood systems in which finite elements are selected subsets of a master set of objects called “tokens”. Since tokens have little intuitive significance, Scott has discarded neighborhood systems in favor of an equivalent formulation of domains called information systems [4]. Unfortunately, he has not rewritten his monograph to reflect this change. We have rewritten Scott’s monograph in terms of finitary bases (see Cartwright [2]) instead of information systems. A finitary basis is an information system that is closed under least upper bounds on finite consistent subsets. This convention ensures that every finite answer is represented by a single basis object instead of a set of objects. 1 The Rudiments of Domain Theory Motivation Programs perform computations by repeatedly applying primitive operations to data values. The set of primitive operations and data values depends on the particular programming language. Nearly all languages support a rich collection of data values including atomic objects, such as booleans, integers, characters, and floating point numbers, and composite objects, such as arrays, records, sequences, tuples, and infinite streams. More advanced languages also support functions and procedures as data values. To define the meaning of programs in a given language, we must first define the building blocks—the primitive data values and operations—from which computations in the language are constructed. Domain theory is a comprehensive mathematical framework for defining the data values and primitive operations of a programming language.