Reasoning in Boolean Networks provides a detailed treatment of recent research advances in algorithmic techniques for logic synthesis, test generation and formal verification of digital circuits. The book presents the central idea of approaching design automation problems for logic-level circuits by specific Boolean reasoning techniques. While Boolean reasoning techniques have been a central element of two-level circuit theory for many decades Reasoning in Boolean Networks describes a basic reasoning methodology for multi-level circuits. This leads to a unified view on two-level and multi-level logic synthesis. The presented reasoning techniques are applied to various CAD-problems to demonstrate their usefulness for today's industrially relevant problems. Reasoning in Boolean Networks provides lucid descriptions of basic algorithmic concepts in automatic test pattern generation, logic synthesis and verification and elaborates their intimate relationship to provide further intuition and insight into the subject. Numerous examples are provide for ease in understanding the material. Reasoning in Boolean Networks is intended for researchers in logic synthesis, VLSI testing and formal verification as well as for integrated circuit designers who want to enhance their understanding of basic CAD methodologies.

This book constitutes the refereed conference proceedings of the 28th International Conference on Inductive Logic Programming, ILP 2018, held in Ferrara, Italy, in September 2018. The 10 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.

Systems Biology is the systematic study of the interactions between the components of a biological system and studies how these interactions give rise to the function and behavior of the living system. Through this, a life process is to be understood as a whole system rather than the collection of the parts considered separately. Systems Biology is therefore more than just an emerging field: it represents a new way of thinking about biology with a dramatic impact on the way that research is performed. The logical approach provides an intuitive method to provide explanations based on an expressive relational language. This book covers various aspects of logical modeling of biological systems, bringing together 10 recent logic-based approaches to Systems Biology by leading scientists. The chapters cover the biological fields of gene regulatory networks, signaling networks, metabolic pathways, molecular interaction and network dynamics, and show logical methods for these domains based on propositional and first-order logic, logic programming, answer set programming, temporal logic, Boolean networks, Petri nets, process hitting, and abductive and inductive logic programming. It provides an excellent guide for all scientists, biologists, bioinformaticians, and engineers, who are interested in logic-based modeling of biological systems, and the authors hope that new scientists will be encouraged to join this exciting scientific endeavor.

This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 papers and 3 short papers presented were carefully reviewed and selected from 19 submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.

This book presents outstanding contributions in an exciting, new and multidisciplinary research area: the application of formal, automated reasoning techniques to analyse complex models in systems biology and systems medicine. Automated reasoning is a field of computer science devoted to the development of algorithms that yield trustworthy answers, providing a basis of sound logical reasoning. For example, in the semiconductor industry formal verification is instrumental to ensuring that chip designs are free of defects (or “bugs”). Over the past 15 years, systems biology and systems medicine have been introduced in an attempt to understand the enormous complexity of life from a computational point of view. This has generated a wealth of new knowledge in the form of computational models, whose staggering complexity makes manual analysis methods infeasible. Sound, trusted, and automated means of analysing the models are thus required in order to be able to trust their conclusions. Above all, this is crucial to engineering safe biomedical devices and to reducing our reliance on wet-lab experiments and clinical trials, which will in turn produce lower economic and societal costs. Some examples of the questions addressed here include: Can we automatically adjust medications for patients with multiple chronic conditions? Can we verify that an artificial pancreas system delivers insulin in a way that ensures Type 1 diabetic patients never suffer from hyperglycaemia or hypoglycaemia? And lastly, can we predict what kind of mutations a cancer cell is likely to undergo? This book brings together leading researchers from a number of highly interdisciplinary areas, including: · Parameter inference from time series · Model selection · Network structure identification · Machine learning · Systems medicine · Hypothesis generation from experimental data · Systems biology, systems medicine, and digital pathology · Verification of biomedical devices “This book presents a comprehensive spectrum of model-focused analysis techniques for biological systems ...an essential resource for tracking the developments of a fast moving field that promises to revolutionize biology and medicine by the automated analysis of models and data.”Prof Luca Cardelli FRS, University of Oxford

This book provides broad and comprehensive coverage of the entire EDA flow. EDA/VLSI practitioners and researchers in need of fluency in an "adjacent" field will find this an invaluable reference to the basic EDA concepts, principles, data structures, algorithms, and architectures for the design, verification, and test of VLSI circuits. Anyone who needs to learn the concepts, principles, data structures, algorithms, and architectures of the EDA flow will benefit from this book. - Covers complete spectrum of the EDA flow, from ESL design modeling to logic/test synthesis, verification, physical design, and test - helps EDA newcomers to get "up-and-running" quickly - Includes comprehensive coverage of EDA concepts, principles, data structures, algorithms, and architectures - helps all readers improve their VLSI design competence - Contains latest advancements not yet available in other books, including Test compression, ESL design modeling, large-scale floorplanning, placement, routing, synthesis of clock and power/ground networks - helps readers to design/develop testable chips or products - Includes industry best-practices wherever appropriate in most chapters - helps readers avoid costly mistakes

This Festschrift published in honor of Vladimir Lifschitz on the occasion of his 65th birthday presents 39 articles by colleagues from all over the world with whom Vladimir Lifschitz had cooperation in various respects. The 39 contributions reflect the breadth and the depth of the work of Vladimir Lifschitz in logic programming, circumscription, default logic, action theory, causal reasoning and answer set programming.

FoLLI-LNCS is the publication platform for the Association of Logic, Language and Information (FoLLI, www.folli.org). The Association was founded in 1991 to advance research and education on the interface between logic, linguistics, computer science, and cognitive science. The FoLLI Publications on Logic, Language and Information aim to disseminate results of cutting-edge research and tutorial materials in these interdisciplinary areas. This LNCS volume is part of FoLLi book serie and contains the papers presented at the 5th International Workshop on Logic, Rationality and Interaction/ (LORI-V), held in October 2015 in Taipei, Taiwan. The topics covered in this program well represent the span and depth that hasby now become a trademark of the LORI workshop series, where logic interfaceswith disciplines as diverse as game theory and decision theory, philosophyand epistemology, linguistics, computer science and artificial intelligence.

This book constitutes the thoroughly refereed post-conference proceedings of the 24th International Conference on Inductive Logic Programming, ILP 2014, held in Nancy, France, in September 2014. The 14 revised papers presented were carefully reviewed and selected from 41 submissions. The papers focus on topics such as the inducing of logic programs, learning from data represented with logic, multi-relational machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining.