This book presents extended versions of selected papers from the annual International Workshops on Constraint Programming and Decision Making from 2016 to 2018. The papers address all stages of decision-making under constraints: (1) precisely formulating the problem of multi-criteria decision-making; (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms and making these algorithms as efficient as possible; and (4) taking into account interval, probabilistic, and fuzzy uncertainty inherent in the corresponding decision-making problems. In many application areas, it is necessary to make effective decisions under constraints, and there are several area-specific techniques for such decision problems. However, because they are area-specific, it is not easy to apply these techniques in other application areas. As such, the annual International Workshops on Constraint Programming and Decision Making focus on cross-fertilization between different areas, attracting researchers and practitioners from around the globe. The book includes numerous papers describing applications, in particular, applications to engineering, such as control of unmanned aerial vehicles, and vehicle protection against improvised explosion devices.

This book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation. Example of applications described in the book ranges from science (biosciences, geosciences, and physics) to electrical and civil engineering, education, psychology and decision making, and religion—and, of course, include computer science, AI (in particular, eXplainable AI), and machine learning. The book can be recommended to researchers and students in these application areas. Many of the examples use general techniques that can be used in other application areas as well, so it is also useful for practitioners and researchers in other areas who are looking for possible theoretical explanations of empirical formulas and techniques.

The problem of selection of alternatives or the problem of decision making in the modern world has become the most important class of problems constantly faced by business people, researchers, doctors and engineers.The fields that are almost entirely focused on conflicts, where applied mathematics is successfully used, are law, military science, many branches of economics, sociology, political science, and psychology. There are good grounds to believe that medicine and some branches of biology and ethics can also be included in this list. Modern applied mathematics can produce solutions to many tens of classes of conflicts differing by the composition and structure of the participants, specific features of the set of their objectives or interests, and various characteristics of the set of their actions, strategies, behaviors, controls, and decisions as applied to various principles of selection or notions of decision optimization.The current issues of social and economic systems involve the necessity to coordinate and jointly optimize various lines of development and activities of modern society. For this reason, the decision problems arising in investigation of such systems are versatile, which shows up not only in the multiplicity of participants, their interests and complexity of reciprocal effects, but also in the laborious development of social utility criteria for a variety of indices and versatile objectives. The efficient decision methods for such complex systems can be developed only the basis of specially developed mathematical tools.

Previous studies conducted within the aviation industry have examined a multitude of crucial aspects such as policy, airline service quality, and revenue management. An extensive body of literature has also recognised the importance of decision-making in aviation, with the focus predominantly on pilots and air traffic controllers. Understanding Decision-Making Processes in Airline Operations Control focuses instead on an area largely overlooked: an airline's Operations Control Centre (OCC). This serves as the nerve centre of the airline and is responsible for decision-making with respect to operational control of an airline's daily schedules. The environment within an OCC is extremely intense and a key role of controllers is to make decisions that facilitate the airline's recovery from frequent, highly complex, and often multiple disruptions. As such, decision-making in this domain is critical to minimise the operational, commercial and financial impact resulting from disruptions. The book examines many aspects of individual decision-making in airline operations, and addresses the deficiencies found by presenting to the reader an examination of the relationships among situation awareness, information completeness, experience, expertise, decision considerations and decision alternatives in OCCs. The text utilises a multiple case study approach and proposes a number of relevant and important implications for OCC management. Practical outcomes highlight the need for enhancing training programs enabling existing controllers to readily identify and classify elements of situation awareness and decision considerations as a means of improving the decision-making process. They also draw attention to the need for airline OCCs to understand the extent to which industry experience and expertise of controllers is important in the selection of future staff.

Translation may be regarded as a response to a problem of communication usually between two language-bound cultural entities. The act itself is a multi-dimensional and multi-layered process involving in one consensual domain problem solving and decision making. This book explores translation as a decision-making process under constraints. It examines these constraints, and develops a three-tier decision-based translation model that can be used in self-evaluation of translations and in translation quality assessment. The model is anchored in optimality theory and regards translation as a temporary system of conflicting forces embodied by constraints yielding an optimal translation within these constraints. -- Product Description.

The book comprises original articles on topical issues of risk theory, rational decision making, statistical decisions, and control of stochastic systems. The articles are the outcome of a series international projects involving the leading scholars in the field of modern stochastic optimization and decision making. The structure of stochastic optimization solvers is described. The solvers in general implement stochastic quasi-gradient methods for optimization and identification of complex nonlinear models. These models constitute an important methodology for finding optimal decisions under risk and uncertainty. While a large part of current approaches towards optimization under uncertainty stems from linear programming (LP) and often results in large LPs of special structure, stochastic quasi-gradient methods confront nonlinearities directly without need of linearization. This makes them an appropriate tool for solving complex nonlinear problems, concurrent optimization and simulation models, and equilibrium situations of different types, for instance, Nash or Stackelberg equilibrium situations. The solver finds the equilibrium solution when the optimization model describes the system with several actors. The solver is parallelizable, performing several simulation threads in parallel. It is capable of solving stochastic optimization problems, finding stochastic Nash equilibria, and of composite stochastic bilevel problems where each level may require the solution of stochastic optimization problem or finding Nash equilibrium. Several complex examples with applications to water resources management, energy markets, pricing of services on social networks are provided. In the case of power system, regulator makes decision on the final expansion plan, considering the strategic behavior of regulated companies and coordinating the interests of different economic entities. Such a plan can be an equilibrium − a planned decision where a company cannot increase its expected gain unilaterally.

This book describes new algorithms and ideas for making effective decisions under constraints, including applications in control engineering, manufacturing (how to optimally determine the production level), econometrics (how to better predict stock market behavior), and environmental science and geosciences (how to combine data of different types). It also describes general algorithms and ideas that can be used in other application areas. The book presents extended versions of selected papers from the annual International Workshops on Constraint Programming and Decision Making (CoProd’XX) from 2013 to 2016. These workshops, held in the US (El Paso, Texas) and in Europe (Würzburg, Germany, and Uppsala, Sweden), have attracted researchers and practitioners from all over the world. It is of interest to practitioners who benefit from the new techniques, to researchers who want to extend the ideas from these papers to new application areas and/or further improve the corresponding algorithms, and to graduate students who want to learn more – in short, to anyone who wants to make more effective decisions under constraints.

Recognizing that the generation of natural language is a goal- driven process, where many of the goals are pragmatic (i.e., interpersonal and situational) in nature, this book provides an overview of the role of pragmatics in language generation. Each chapter states a problem that arises in generation, develops a pragmatics-based solution, and then describes how the solution is implemented in PAULINE, a language generator that can produce numerous versions of a single underlying message, depending on its setting.

This book contains the carefully selected and reviewed papers presented at three satellite events that were held in conjunction with the 11th International Conference on Web Information Systems Engineering, WISE 2010, in Hong Kong, China, in December 2010. The collection comprises a total of 40 contributions that originate from the First International Symposium on Web Intelligent Systems and Services (WISS 2010), from the First International Workshop on Cloud Information Systems Engineering (CISE 2010) and from the Second International Workshop on Mobile Business Collaboration (MBC 2010). The papers address a wide range of hot topics and are organized in topical sections on: decision and e-markets; rules and XML; web service intelligence; semantics and services; analyzing web resources; engineering web systems; intelligent web applications; web communities and personalization; cloud information system engineering; mobile business collaboration.

In many application areas, it is necessary to make effective decisions under constraints. Several area-specific techniques are known for such decision problems; however, because these techniques are area-specific, it is not easy to apply each technique to other applications areas. Cross-fertilization between different application areas is one of the main objectives of the annual International Workshops on Constraint Programming and Decision Making. Those workshops, held in the US (El Paso, Texas), in Europe (Lyon, France) and in Asia (Novosibirsk, Russia), from 2008 to 2012, have attracted researchers and practitioners from all over the world. This volume presents extended versions of selected papers from those workshops. These papers deal with all stages of decision making under constraints: (1) formulating the problem of multi-criteria decision making in precise terms, (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms and making these algorithms as efficient as possible and (4) taking into account interval, probabilistic and fuzzy uncertainty inherent in the corresponding decision making problems. The resulting application areas include environmental studies (selecting the best location for a meteorological tower), biology (selecting the most probable evolution history of a species), and engineering (designing the best control for a magnetic levitation train).