Texts in Theoretical Computer Science. An EATCS Series – serie

Software programs are formal entities with precise meanings independent of their programmers, so the transition from ideas to programs necessarily involves a formalisation at some point.The first part of this graduate-level introduction to formal methods develops an understanding of what constitutes formal methods and what their place is in Software Engineering. It also introduces logics as languages to describe reasoning and the process algebra CSP as a language to represent behaviours. The second part offers specification and testing methods for formal development of software, based on the modelling languages CASL and UML. The third part takes the reader into the application domains of normative documents, human machine interfaces, and security.  Use of notations and formalisms is uniform throughout the book.Topics and features:Explains foundations, and introduces specification, verification, and testing methodsExploresvarious application domainsPresents realistic and practical examples, illustrating conceptsBrings together contributions from highly experienced educators and researchers Offers modelling and analysis methods for formal development of softwareSuitable for graduate and undergraduate courses in software engineering, this uniquely practical textbook will also be of value to students in informatics, as well as to scientists and practical engineers,  who want to learn about or work more effectively with formal theories and methods.Markus Roggenbach is a Professor in the Dept. of Computer Science of Swansea University. Antonio Cerone is an Associate Professor in the Dept. of Computer Science of Nazarbayev University, Nur-Sultan. Bernd-Holger Schlingloff is a Professor in the Institut für Informatik of Humboldt-Universität zu Berlin. Gerardo Schneider is a Professor in the Dept. of Computer Science and Engineering of University of Gothenburg. Siraj Ahmed Shaikh is a Professor in the Institute for Future Transport and Cities of Coventry University.The companion site for the book offers additional resources, including further material for selected chapters, prepared lab classes, a list of errata, slides and teaching material, and virtual machines with preinstalled tools and resources for hands-on experience with examples from the book. The URL is: https://sefm-book.github.io

Software programs are formal entities with precise meanings independent of their programmers, so the transition from ideas to programs necessarily involves a formalisation at some point.The first part of this graduate-level introduction to formal methods develops an understanding of what constitutes formal methods and what their place is in Software Engineering. It also introduces logics as languages to describe reasoning and the process algebra CSP as a language to represent behaviours. The second part offers specification and testing methods for formal development of software, based on the modelling languages CASL and UML. The third part takes the reader into the application domains of normative documents, human machine interfaces, and security.  Use of notations and formalisms is uniform throughout the book.Topics and features:Explains foundations, and introduces specification, verification, and testing methodsExploresvarious application domainsPresents realistic and practical examples, illustrating conceptsBrings together contributions from highly experienced educators and researchers Offers modelling and analysis methods for formal development of softwareSuitable for graduate and undergraduate courses in software engineering, this uniquely practical textbook will also be of value to students in informatics, as well as to scientists and practical engineers,  who want to learn about or work more effectively with formal theories and methods.Markus Roggenbach is a Professor in the Dept. of Computer Science of Swansea University. Antonio Cerone is an Associate Professor in the Dept. of Computer Science of Nazarbayev University, Nur-Sultan. Bernd-Holger Schlingloff is a Professor in the Institut für Informatik of Humboldt-Universität zu Berlin. Gerardo Schneider is a Professor in the Dept. of Computer Science and Engineering of University of Gothenburg. Siraj Ahmed Shaikh is a Professor in the Institute for Future Transport and Cities of Coventry University.The companion site for the book offers additional resources, including further material for selected chapters, prepared lab classes, a list of errata, slides and teaching material, and virtual machines with preinstalled tools and resources for hands-on experience with examples from the book. The URL is: https://sefm-book.github.io

This book presents the fundamentals of concurrency theory with clarity and rigor. The authors start with the semantic structure, namely labelled transition systems, which provides us with the means and the tools to express processes, to compose them, and to prove properties they enjoy. The rest of the book relies on Milner's Calculus of Communicating Systems, tailored versions of which are used to study various notions of equality between systems, and to investigate in detail the expressive power of the models considered.The authors proceed from very basic results to increasingly complex issues, with many examples and exercises that help to reveal the many subtleties of the topic. The book is suitable for advanced undergraduate and graduate students in computer science and engineering, and scientists engaged with theories of concurrency.

This book presents the fundamentals of concurrency theory with clarity and rigor. The authors start with the semantic structure, namely labelled transition systems, which provides us with the means and the tools to express processes, to compose them, and to prove properties they enjoy. The rest of the book relies on Milner's Calculus of Communicating Systems, tailored versions of which are used to study various notions of equality between systems, and to investigate in detail the expressive power of the models considered.The authors proceed from very basic results to increasingly complex issues, with many examples and exercises that help to reveal the many subtleties of the topic. The book is suitable for advanced undergraduate and graduate students in computer science and engineering, and scientists engaged with theories of concurrency.

This textbook explains online computation in different settings, with particular emphasis on randomization and advice complexity. These settings are analyzed for various online problems such as the paging problem, the k-server problem, job shop scheduling, the knapsack problem, the bit guessing problem, and problems on graphs.This book is appropriate for undergraduate and graduate students of computer science, assuming a basic knowledge in algorithmics and discrete mathematics. Also researchers will find this a valuable reference for the recent field of advice complexity.

This book presents in their basic form the most important models of computation, their basic programming paradigms, and their mathematical descriptions, both concrete and abstract. Each model is accompanied by relevant formal techniques for reasoning on it and for proving some properties. After preliminary chapters that introduce the notions of structure and meaning, semantic methods, inference rules, and logic programming, the authors arrange their chapters into parts on IMP, a simple imperative language; HOFL, a higher-order functional language; concurrent, nondeterministic and interactive models; and probabilistic/stochastic models.The authors have class-tested the book content over many years, and it will be valuable for graduate and advanced undergraduate students of theoretical computer science and distributed systems, and for researchers in this domain. Each chapter of the book concludes with a list of exercises addressing the key techniques introduced, solutions to selected exercises are offered at the end of the book.

This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies.This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.

This textbook explains online computation in different settings, with particular emphasis on randomization and advice complexity. These settings are analyzed for various online problems such as the paging problem, the k-server problem, job shop scheduling, the knapsack problem, the bit guessing problem, and problems on graphs.This book is appropriate for undergraduate and graduate students of computer science, assuming a basic knowledge in algorithmics and discrete mathematics. Also researchers will find this a valuable reference for the recent field of advice complexity.

This book presents in their basic form the most important models of computation, their basic programming paradigms, and their mathematical descriptions, both concrete and abstract. Each model is accompanied by relevant formal techniques for reasoning on it and for proving some properties. After preliminary chapters that introduce the notions of structure and meaning, semantic methods, inference rules, and logic programming, the authors arrange their chapters into parts on IMP, a simple imperative language; HOFL, a higher-order functional language; concurrent, nondeterministic and interactive models; and probabilistic/stochastic models.The authors have class-tested the book content over many years, and it will be valuable for graduate and advanced undergraduate students of theoretical computer science and distributed systems, and for researchers in this domain. Each chapter of the book concludes with a list of exercises addressing the key techniques introduced, solutions to selected exercises are offered at the end of the book.

This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies.This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.

Reactive systems are becoming more and more important for essentially all areas of technical and professional activities as well as for many areas of everyday life. The design of these systems is a great challenge and requires sound compromises between safety and time-to-market. To meet these needs, early design phases nowadays include verification of given specifications against system descriptions to find potential design errors as early as possible. This book is devoted to the foundation of the most popular formal methods for the specification and verification of reactive systems. In particular, the µ-calculus, omega-automata, and temporal logics are covered in full detail; their relationship and state-of-the-art verification procedures based on these formal approaches are presented. Furthermore, the advantages and disadvantages of the formalisms from particular points of view are analyzed. Most results are given with detailed proofs, so that the presentation is almost self-contained. This book is targeted to advanced students, lecturers and researchers in the area of formal methods.

Finite model theory,as understoodhere, is an areaof mathematicallogic that has developed in close connection with applications to computer science, in particular the theory of computational complexity and database theory. One of the fundamental insights of mathematical logic is that our understanding of mathematical phenomena is enriched by elevating the languages we use to describe mathematical structures to objects of explicit study. If mathematics is the science of patterns, then the media through which we discern patterns, as well as the structures in which we discern them, command our attention. It isthis aspect oflogicwhichis mostprominentin model theory,“thebranchof mathematical logic which deals with the relation between a formal language and its interpretations”. No wonder, then, that mathematical logic, and ?nite model theory in particular, should ?nd manifold applications in computer science: from specifying programs to querying databases, computer science is rife with phenomena whose understanding requires close attention to the interaction between language and structure. This volume gives a broadoverviewof some central themes of ?nite model theory: expressive power, descriptive complexity, and zero–one laws, together with selected applications to database theory and arti?cial intelligence, es- cially constraint databases and constraint satisfaction problems. The ?nal chapter provides a concise modern introduction to modal logic,which emp- sizes the continuity in spirit and technique with ?nite model theory.

Preface Due to the development of hardware technologies (such as VLSI) in the early 1980s, the interest in parallel and distributive computing has been rapidly growingandinthelate1980sthestudyofparallelalgorithmsandarchitectures became one of the main topics in computer science. To bring the topic to educatorsandstudents,severalbooksonparallelcomputingwerewritten. The involvedtextbook“IntroductiontoParallelAlgorithmsandArchitectures”by F. Thomson Leighton in 1992 was one of the milestones in the development of parallel architectures and parallel algorithms. But in the last decade or so the main interest in parallel and distributive computing moved from the design of parallel algorithms and expensive parallel computers to the new distributive reality – the world of interconnected computers that cooperate (often asynchronously) in order to solve di?erent tasks. Communication became one of the most frequently used terms of computer science because of the following reasons: (i) Considering the high performance of current computers, the communi- tion is often moretime consuming than the computing time of processors. As a result, the capacity of communication channels is the bottleneck in the execution of many distributive algorithms. (ii) Many tasks in the Internet are pure communication tasks. We do not want to compute anything, we only want to execute some information - change or to extract some information as soon as possible and as cheaply as possible. Also, we do not have a central database involving all basic knowledge. Instead, wehavea distributed memorywherethe basickno- edgeisdistributedamongthelocalmemoriesofalargenumberofdi?erent computers. The growing importance of solving pure communication tasks in the - terconnected world is the main motivation forwriting this book.

The aim of this textbook is not only to provide an elegant route through the theoretical fundamentals of computer science, but also to show that theoretical computer science is a fascinating discipline, full of spectacular contributions and miracles, and depth in research, and yet directly applicable. Thus, we aim to excite people about its study. To achieve these goals we do not hesitate to take a lot of space to present motivations, and especially to give the informal development of crucial ideas and concepts and their transparent, but rigorous presentation.An additional aim is to present the development of the computer scientist's way of thinking, so we do not restrict this book to the classic areas like computability and automata theory but we also present fundamental concepts such as approximation and randomization in algorithmics and we explain the basic ideas of cryptography and interconnection network design.

The book contains a completely new presentation of classical results in the field of Lambda Calculus, together with new results. The text is unique in that it presents a new calculus (Parametric Lambda Calculus) which can be instantiated to obtain already known lambda-calculi. Some properties, which in the literature have been proved separately for different calculi, can be proved once for the Parametric one. The lambda calculi are presented from a Computer Science point of view, with a particular emphasis on their semantics, both operational and denotational.

Coq is an interactive proof assistant for the development of mathematical theories and formally certified software. It is based on a theory called the calculus of inductive constructions, a variant of type theory.This book provides a pragmatic introduction to the development of proofs and certified programs using Coq. With its large collection of examples and exercises it is an invaluable tool for researchers, students, and engineers interested in formal methods and the development of zero-fault software.

The book addresses ways and means of organizing computations, highlighting the relationship between algorithms and the basic mechanisms and runtime structures necessary to execute them using machines. It completely abstracts from concrete programming languages and machine architectures, taking instead the lambda calculus as the basic programming and program execution model to design various abstract machines for its correct implementation.The emphasis is on fully normalizing machines based on a full-fledged beta-reduction as an essential prerequisite for symbolic computations that treat functions and variables truly as first-class objects. Their weakly normalizing counterparts are shown to be functional abstract machines that sacrifice the flavors of full beta-reductions for decidedly simpler runtime structures and improved runtime efficiency. Further downgrading of the lambda calculus leads to classical imperative (von Neumann) machines that permit side-effecting operations on the runtime environment.

The art, craft, discipline, logic, practice and science of developing large-scale software products needs a professional base. The textbooks in this three-volume set combine informal, engineeringly sound approaches with the rigor of formal, mathematics-based approaches. This volume covers the basic principles and techniques of abstraction and modeling. First it provides a sound, simple basis of insight into discrete mathematics: numbers, sets, Cartesians, types, functions, the Lambda calculus, algebras and mathematical logic. Then it teaches its readers basic property- and model-oriented specification principles and techniques. The model-oriented concepts that are common to such specification languages as B, VDM-SL and Z are propagated here through the use of the RAISE specification language (RSL). Finally the book covers the basic principles of functional, imperative and parallel specification programming.This book is targeted at university undergraduate students and at college lecturers.

The art, craft, discipline, logic, practice and science of developing large-scale software products needs a professional base. The textbooks in this three-volume set combine informal, engineeringly sound approaches with the rigor of formal, mathematics-based approaches. This volume covers the basic principles and techniques of specifying systems and languages. It deals with modeling the semiotics (pragmatics, semantics and syntax of systems and languages), modeling spatial and simple temporal phenomena, and such specialized topics as modularity (incl. UML class diagrams), Petri nets, live sequence charts, statecharts, and temporal logics, including the duration calculus. Finally, the book presents techniques for interpreter and compiler development of functional, imperative, modular and parallel programming languages.This book is targeted at late undergraduate to early graduate university students, and researchers of programming methodologies. Vol. 1 of this series is a prerequisite text.

The art, craft, discipline, logic, practice and science of developing large-scale software products needs a professional base. The textbooks in this three-volume set combine informal, engineeringly sound approaches with the rigor of formal, mathematics-based approaches. Advocating a novel approach based on understanding the application domain before formulating the requirements, the book takes the reader from the principles and techniques for the development of domain descriptions, via principles and techniques for the derivation of requirements prescriptions from domain models, to principles and techniques for the refinement of requirements into software designs: architectures and component design. Focusing only on the informal parts, this book targets undergraduate students in courses on software engineering and college lecturers in that field, while the full version of the book also targets advanced students, lecturers and researchers.

Finite model theory is an area of mathematical logic that grew out of computer science applications. The main sources of motivational examples for finite model theory are found in database theory, computational complexity, and formal languages, although in recent years connections with other areas, such as formal methods and verification, and artificial intelligence, have been discovered. The birth of finite model theory is often identified with Trakhtenbrot's result from 1950 stating that validity over finite models is not recursively enumerable; in other words, completeness fails over finite models. The tech­ nique of the proof, based on encoding Turing machine computations as finite structures, was reused by Fagin almost a quarter century later to prove his cel­ ebrated result that put the equality sign between the class NP and existential second-order logic, thereby providing a machine-independent characterization of an important complexity class. In 1982, Immerman and Vardi showed that over ordered structures, a fixed point extension of first-order logic captures the complexity class PTIME of polynomial time computable propertiE~s. Shortly thereafter, logical characterizations of other important complexity classes were obtained. This line of work is often referred to as descriptive complexity. A different line of finite model theory research is associated with the de­ velopment of relational databases. By the late 1970s, the relational database model had replaced others, and all the basic query languages for it were es­ sentially first-order predicate calculus or its minor extensions.

This book introduces the reader to the grammatical theory of picture generation, focussing on issues that are of particular interest for computer scientists. Using picture generation as a vehicle, many of the general ideas of the theory of computation are exemplified by results about (un)decidable, NP-complete, or efficiently solvable problems, normal forms, hierarchies of language classes, and related phenomena.Many of the devices and results discussed in the book extend those known from the literature, or are original ones, including a novel approach to generating colored pictures.The software on the enclosed CD-ROM lets you try out all examples or even extend the system.

This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments. The book introduces these two different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an unknown environment. Most AI problems can easily be formulated within this theory, reducing the conceptual problems to pure computational ones. Considered problem classes include sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The discussion includes formal definitions of intelligence order relations, the horizon problem and relations to other approaches. One intention of this book is to excite a broader AI audience about abstract algorithmic information theory concepts, and conversely to inform theorists about exciting applications to AI.

Modern cryptology increasingly employs mathematically rigorous concepts and methods from complexity theory. Conversely, current research topics in complexity theory are often motivated by questions and problems from cryptology. This book takes account of this situation, and therefore its subject is what may be dubbed "cryptocomplexity'', a kind of symbiosis of these two areas.This book is written for undergraduate and graduate students of computer science, mathematics, and engineering, and can be used for courses on complexity theory and cryptology, preferably by stressing their interrelation. Moreover, it may serve as a valuable source for researchers, teachers, and practitioners working in these fields. Starting from scratch, it works its way to the frontiers of current research in these fields and provides a detailed overview of their history and their current research topics and challenges.

A man may imagine he understands something, but still not understand anything in the way that he ought to. (Paul of Tarsus, 1 Corinthians 8:2) Calling this a ‘practical theory’ may require some explanation. Theory and practice are often thought of as two di?erent worlds, governed bydi?erentideals,principles, andlaws.DavidLorgeParnas, forinstance,who hascontributedmuchtoourtheoreticalunderstandingofsoftwareengineering and also to sound use of theory in the practice of it, likes to point out that ‘theoretically’ is synonymous to ‘not really’. In applied mathematics the goal is to discover useful connections between these two worlds. My thesis is that in software engineering this two-world view is inadequate, and a more intimate interplay is required between theory and practice. That is, both theoretical and practical components should be integrated into a practical theory. It should beclearfrom theabovethattheintended readership of this book is not theoreticians. They would probably have di?culties in appreciating a book on theory where the presentation does not proceed in a logical sequence from basic de?nitions to theorems and mathematical proofs, followed by - plication examples. In fact, all this would not constitute what I understand by a practical theory in this context.

Randomness is a powerful phenomenon that can be harnessed to solve various problems in all areas of computer science. Randomized algorithms are often more efficient, simpler and, surprisingly, also more reliable than their deterministic counterparts. Computing tasks exist that require billions of years of computer work when solved using the fastest known deterministic algorithms, but they can be solved using randomized algorithms in a few minutes with negligible error probabilities.Introducing the fascinating world of randomness, this book systematically teaches the main algorithm design paradigms ? foiling an adversary, abundance of witnesses, fingerprinting, amplification, and random sampling, etc. ? while also providing a deep insight into the nature of success in randomization. Taking sufficient time to present motivations and to develop the reader's intuition, while being rigorous throughout, this text is a very effective and efficient introduction to this exciting field.

Parameterized complexity theory is a recent branch of computational complexity theory that provides a framework for a refined analysis of hard algorithmic problems. The central notion of the theory, fixed-parameter tractability, has led to the development of various new algorithmic techniques and a whole new theory of intractability.This book is a state-of-the-art introduction to both algorithmic techniques for fixed-parameter tractability and the structural theory of parameterized complexity classes, and it presents detailed proofs of recent advanced results that have not appeared in book form before. Several chapters are each devoted to intractability, algorithmic techniques for designing fixed-parameter tractable algorithms, and bounded fixed-parameter tractability and subexponential time complexity. The treatment is comprehensive, and the reader is supported with exercises, notes, a detailed index, and some background on complexity theory and logic.The book will be of interest to computer scientists, mathematicians and graduate students engaged with algorithms and problem complexity.

The first edition of the monograph Information and Randomness: An Algorithmic Perspective by Crist ian Calude was published in 1994. In my Foreword I said: "The research in algorithmic information theory is already some 30 years old. However, only the recent years have witnessed a really vigorous growth in this area. . . . The present book by Calude fits very well in our series. Much original research is presented. . . making the approach richer in consequences than the classical one. Remarkably, however, the text is so self-contained and coherent that the book may also serve as a textbook. All proofs are given in the book and, thus, it is not necessary to consult other sources for classroom instruction. " The vigorous growth in the study of algorithmic information theory has continued during the past few years, which is clearly visible in the present second edition. Many new results, examples, exercises and open prob­ lems have been added. The additions include two entirely new chapters: "Computably Enumerable Random Reals" and "Randomness and Incom­ pleteness". The really comprehensive new bibliography makes the book very valuable for a researcher. The new results about the characterization of computably enumerable random reals, as well as the fascinating Omega Numbers, should contribute much to the value of the book as a textbook. The author has been directly involved in these results that have appeared in the prestigious journals Nature, New Scientist and Pour la Science.

Algorithmic design, especially for hard problems, is more essential for success in solving them than any standard improvement of current computer tech­ nologies. Because of this, the design of algorithms for solving hard problems is the core of current algorithmic research from the theoretical point of view as well as from the practical point of view. There are many general text books on algorithmics, and several specialized books devoted to particular approaches such as local search, randomization, approximation algorithms, or heuristics. But there is no textbook that focuses on the design of algorithms for hard computing tasks, and that systematically explains, combines, and compares the main possibilities for attacking hard algorithmic problems. As this topic is fundamental for computer science, this book tries to close this gap. Another motivation, and probably the main reason for writing this book, is connected to education. The considered area has developed very dynami­ cally in recent years and the research on this topic discovered several profound results, new concepts, and new methods. Some of the achieved contributions are so fundamental that one can speak about paradigms which should be in­ cluded in the education of every computer science student. Unfortunately, this is very far from reality. This is because these paradigms are not sufficiently known in the computer science community, and so they are insufficiently com­ municated to students and practitioners.