Automated Configuration Problem Solving

Automated Configuration has long been the subject of intensive research, especially in Artificial Intelligence. It is a pervasive problem to be solved, and it is a good test of various knowledge representation and reasoning techniques. The problem shows up in applications such as various electrical circuit design, utility computing and even concurrent engineering. Automated Configuration Problem Solving defines the ubiquitous problem, illustrates the various solution techniques, and includes a survey using these techniques from the mid-70's until the mid-90's. During this time, various general approaches were developed, and then become more specialized. This book covers the development of the general problem solving techniques for automated configuration, which are based on both published academic work and patents.

This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization.

This book constitutes the refereed proceedings of the 18th International Conference on Theory and Applications of Satisfiability Testing, SAT 2015, held in Austin, TX, USA, in September 2015. The 21 regular papers, 2 short papers and 7 tool papers presented together with 3 invited talks were carefully reviewed and selected from 70 submissions. The papers address different aspects of SAT, including theoretical advances (exact algorithms, proof complexity, and other complexity issues), practical search algorithms, knowledge compilation, implementation-level details of SAT solvers and SAT-based systems, problem encodings and reformulations, and applications, as well as case studies and reports on insightful findings based on rigorous experimentation.The paper 'Constructing SAT Filters with a Quantum Annealer' is published open access under a CC BY-NC 2.5 license at link.springer.com.

Researchers in Artificial Intelligence have traditionally been classified into two categories: the “neaties” and the “scruffies”. According to the scruffies, the neaties concentrate on building elegant formal frameworks, whose properties are beautifully expressed by means of definitions, lemmas, and theorems, but which are of little or no use when tackling real-world problems. The scruffies are described (by the neaties) as those researchers who build superficially impressive systems that may perform extremely well on one particular case study, but whose properties and underlying theories are hidden in their implementation, if they exist at all. As a life-long, non-card-carrying scruffy, I was naturally a bit suspicious when I first started collaborating with Dieter Fensel, whose work bears all the formal hallmarks of a true neaty. Even more alarming, his primary research goal was to provide sound, formal foundations to the area of knowledge-based systems, a traditional stronghold of the scruffies - one of whom had famously declared it “an art”, thus attempting to place it outside the range of the neaties (and to a large extent succeeding in doing so).

This two-volume set LNCS 13398 and LNCS 13399 constitutes the refereed proceedings of the 17th International Conference on Parallel Problem Solving from Nature, PPSN 2022, held in Dortmund, Germany, in September 2022. The 87 revised full papers were carefully reviewed and selected from numerous submissions. The conference presents a study of computing methods derived from natural models. Amorphous Computing, Artificial Life, Artificial Ant Systems, Artificial Immune Systems, Artificial Neural Networks, Cellular Automata, Evolutionary Computation, Swarm Computing, Self-Organizing Systems, Chemical Computation, Molecular Computation, Quantum Computation, Machine Learning, and Artificial Intelligence approaches using Natural Computing methods are just some of the topics covered in this field.

This book constitutes the refereed proceedings of the 7th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2010, held in Bologna, Italy, in June 2010. The 18 revised full papers and 17 revised short papers presented together with the extended abstracts of 3 invited talks were carefully reviewed and selected from 72 submissions. The papers are focused on both theoretical and practical, application-oriented issues and present current research with a special focus on the integration and hybridization of the approaches of constraint programming, artificial intelligence, and operations research technologies for solving large scale and complex real life combinatorial optimization problems.