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An infinite permutation α is a linear ordering of N. We study propertiesof infinite permutations analogous to those of infinite words, and show some resemblancesand some differences between permutations and words. In this paper, we try to extend topermutations the notion of automaticity. As we shall show, the standard definitions whichare equivalent in the case of words are not equivalent in the context of permutations. Weinvestigate the relationships between these definitions and prove that they constitute achain of inclusions. We also construct and study an automaton generating the Thue-Morsepermutation.
The fixed point submonoid of an endomorphism of a free product of a free monoid andcyclic groups is proved to be rational using automata-theoretic techniques. Maslakova’sresult on the computability of the fixed point subgroup of a free group automorphism isgeneralized to endomorphisms of free products of a free monoid and a free group which areautomorphisms of the maximal subgroup.
Multiwords are words in which a single symbol can be replaced by a nonempty set of symbols. They extend the notion of partial words. A word w is certain in a multiword M if it occurs in every word that can be obtained by selecting one single symbol among the symbols provided in each position of M. Motivated by a problem on incomplete databases, we investigate a variant of the pattern matching problem which is to decide whether a word w is certain in a multiword M. We study the language CERTAIN(w) of multiwords in which w is certain. We show that this regular language is aperiodic for three large families of words. We also show its aperiodicity in the case of partial words over an alphabet with at least three symbols.
For an extensive range of infinite words, and the associated symbolic dynamical systems, we compute, together with the usual language complexity function counting the finite words, the minimal and maximal complexity functions we get by replacing finite words by finite patterns, or words with holes.
We study the avoidance of Abelian powers of words and consider three reasonablegeneralizations of the notion of Abelian power to fractional powers. Our main goal is tofind an Abelian analogue of the repetition threshold, i.e., a numericalvalue separating k-avoidable and k-unavoidable Abelianpowers for each size k of the alphabet. We prove lower bounds for theAbelian repetition threshold for large alphabets and all definitions of Abelian fractionalpower. We develop a method estimating the exponential growth rate of Abelian-power-freelanguages. Using this method, we get non-trivial lower bounds for Abelian repetitionthreshold for small alphabets. We suggest that some of the obtained bounds are the exactvalues of Abelian repetition threshold. In addition, we provide upper bounds for thegrowth rates of some particular Abelian-power-free languages.
This book describes the mathematical aspects of the semantics of programming languages. The main goals are to provide formal tools to assess the meaning of programming constructs in both a language-independent and a machine-independent way, and to prove properties about programs, such as whether they terminate, or whether their result is a solution of the problem they are supposed to solve. In order to achieve this the authors first present, in an elementary and unified way, the theory of certain topological spaces that have proved of use in the modelling of various families of typed lambda calculi considered as core programming languages and as meta-languages for denotational semantics. This theory is known as Domain Theory, and was founded as a subject by Scott and Plotkin. One of the main concerns is to establish links between mathematical structures and more syntactic approaches to semantics, often referred to as operational semantics, which is also described. This dual approach has the double advantage of motivating computer scientists to do some mathematics and of interesting mathematicians in unfamiliar application areas from computer science.
Information is a central topic in computer science, cognitive science and philosophy. In spite of its importance in the 'information age', there is no consensus on what information is, what makes it possible, and what it means for one medium to carry information about another. Drawing on ideas from mathematics, computer science and philosophy, this book addresses the definition and place of information in society. The authors, observing that information flow is possible only within a connected distribution system, provide a mathematically rigorous, philosophically sound foundation for a science of information. They illustrate their theory by applying it to a wide range of phenomena, from file transfer to DNA, from quantum mechanics to speech act theory.
Coinduction is a method for specifying and reasoning about infinite data types and automata with infinite behaviour. In recent years, it has come to play an ever more important role in the theory of computing. It is studied in many disciplines, including process theory and concurrency, modal logic and automata theory. Typically, coinductive proofs demonstrate the equivalence of two objects by constructing a suitable bisimulation relation between them. This collection of surveys is aimed at both researchers and Master's students in computer science and mathematics and deals with various aspects of bisimulation and coinduction, with an emphasis on process theory. Seven chapters cover the following topics: history, algebra and coalgebra, algorithmics, logic, higher-order languages, enhancements of the bisimulation proof method, and probabilities. Exercises are also included to help the reader master new material.
Two deterministic finite automata are almost equivalent if they disagree in acceptanceonly for finitely many inputs. An automaton A is hyper-minimized if noautomaton with fewer states is almost equivalent to A. A regular languageL is canonical if the minimal automaton accepting L ishyper-minimized. The asymptotic state complexitys∗(L) of a regular languageL is the number of states of a hyper-minimized automaton for a languagefinitely different from L. In this paper we show that: (1) the class ofcanonical regular languages is not closed under: intersection, union, concatenation,Kleene closure, difference, symmetric difference, reversal, homomorphism, and inversehomomorphism; (2) for any regular languages L1 andL2 the asymptotic state complexity of their sumL1 ∪ L2, intersectionL1 ∩ L2, differenceL1 − L2, and symmetricdifference L1 ⊕ L2 can be boundedbys∗(L1)·s∗(L2).This bound is tight in binary case and in unary case can be met in infinitely many cases.(3) For any regular language L the asymptotic state complexity of itsreversal LR can be bounded by2s∗(L). This bound is tightin binary case. (4) The asymptotic state complexity of Kleene closure and concatenationcannot be bounded. Namely, for every k ≥ 3, there exist languagesK, L, and M such thats∗(K) = s∗(L) = s∗(M) = 1ands∗(K∗) = s∗(L·M) = k.These are answers to open problems formulated by Badr et al.[RAIRO-Theor. Inf. Appl.43 (2009) 69–94].
This paper presents a new lower bound for the recursive algorithm for solving parity games which is induced by the constructive proof of memoryless determinacy by Zielonka. We outline a family of games of linear size on which the algorithm requires exponential time.
An algorithm is corrected here that was presented as Theorem 2 in [Š. Holub, RAIRO-Theor. Inf. Appl. 40 (2006) 583–591]. It is designed to calculate the maximum length of a nontrivial word with a given set of periods.
Given non-negative weights wS on the k-subsets S of a km-element set V, we consider the sum of the products wS1 ⋅⋅⋅ wSm over all partitions V = S1 ∪ ⋅⋅⋅ ∪Sm into pairwise disjoint k-subsets Si. When the weights wS are positive and within a constant factor of each other, fixed in advance, we present a simple polynomial-time algorithm to approximate the sum within a polynomial in m factor. In the process, we obtain higher-dimensional versions of the van der Waerden and Bregman–Minc bounds for permanents. We also discuss applications to counting of perfect and nearly perfect matchings in hypergraphs.
The history of bisimulation is well documented in earlier chapters of this book. In this chapter we will look at a major non-trivial extension of the theory of labelled transition systems: probabilistic transition systems. There are many possible extensions of theoretical and practical interest: real-time, quantitative, independence, spatial and many others. Probability is the best theory we have for handling uncertainty in all of science, not just computer science. It is not an idle extension made for the purpose of exploring what is theoretically possible. Non-determinism is, of course, important, and arises in computer science because sometimes we just cannot do any better or because we lack quantitative data from which to make quantitative predictions. However, one does not find any use of non-determinism in a quantitative science like physics, though it appears in sciences like biology where we have not yet reached a fundamental understanding of the nature of systems.
When we do have data or quantitative models, it is far preferable to analyse uncertainty probabilistically. A fundamental reason that we want to use probabilistic reasoning is that if we merely reported what is possible and then insisted that no bad things were possible, we would trust very few system designs in real life. For example, we would never trust a communication network, a car, an aeroplane, an investment bank nor would we ever take any medication! In short, only very few idealised systems ever meet purely logical specifications. We need to know the ‘odds’ before we trust any system.
This book is about bisimulation and coinduction. It is the companion book of the volume An Introduction to Bisimulation and Coinduction, by Davide Sangiorgi (Cambridge University Press, 2011), which deals with the basics of bisimulation and coinduction, with an emphasis on labelled transition systems, processes, and other notions from the theory of concurrency.
In the present volume, we have collected a number of chapters, by different authors, on several advanced topics in bisimulation and coinduction. These chapters either treat specific aspects of bisimulation and coinduction in great detail, including their history, algorithmics, enhanced proof methods and logic. Or they generalise the basic notions of bisimulation and coinduction to different or more general settings, such as coalgebra, higher-order languages and probabilistic systems. Below we briefly summarise the chapters in this volume.
The origins of bisimulation and coinduction, by Davide Sangiorgi
In this chapter, the origins of the notions of bisimulation and coinduction are traced back to different fields, notably computer science, modal logic, and set theory.
An introduction to (co)algebra and (co)induction, by Bart Jacobs and Jan Rutten
Here the notions of bisimulation and coinduction are explained in terms of coalgebras. These mathematical structures generalise all kinds of infinitedata structures and automata, including streams (infinite lists), deterministic and probabilistic automata, and labelled transition systems. Coalgebras are formally dual to algebras and it is this duality that is used to put both induction and coinduction into a common perspective.