The decision to invest in oil field development is an extremely complex problem, even in the absence of uncertainty, due to the great number of technological alternatives that may be used, to the dynamic complexity of oil reservoirs - which involves mul- phase flows (oil, gas and water) in porous media with phase change, and to the c- plicated combinatorial optimization problem of choosing the optimal oil well network, that is, choosing the number and types of wells (horizontal, vertical, directional, m- tilateral) required for draining oil from a field with a view to maximizing its economic value. This problem becomes even more difficult when technical uncertainty and e- nomic uncertainty are considered. The former are uncertainties regarding the existence, volume and quality of a reservoir and may encourage an investment in information before the field is developed, in order to reduce these uncertainties and thus optimize the heavy investments required for developing the reservoir. The economic or market uncertainties are associated with the general movements of the economy, such as oil prices, gas demand, exchange rates, etc. , and may lead decision-makers to defer - vestments and wait for better market conditions. Choosing the optimal investment moment under uncertainty is a complex problem which traditionally involves dynamic programming tools and other techniques that are used by the real options theory.

Genetic programming (GP) is a popular heuristic methodology of program synthesis with origins in evolutionary computation. In this generate-and-test approach, candidate programs are iteratively produced and evaluated. The latter involves running programs on tests, where they exhibit complex behaviors reflected in changes of variables, registers, or memory. That behavior not only ultimately determines program output, but may also reveal its `hidden qualities' and important characteristics of the considered synthesis problem. However, the conventional GP is oblivious to most of that information and usually cares only about the number of tests passed by a program. This `evaluation bottleneck' leaves search algorithm underinformed about the actual and potential qualities of candidate programs. This book proposes behavioral program synthesis, a conceptual framework that opens GP to detailed information on program behavior in order to make program synthesis more efficient. Several existing and novel mechanisms subscribing to that perspective to varying extent are presented and discussed, including implicit fitness sharing, semantic GP, co-solvability, trace convergence analysis, pattern-guided program synthesis, and behavioral archives of subprograms. The framework involves several concepts that are new to GP, including execution record, combined trace, and search driver, a generalization of objective function. Empirical evidence gathered in several presented experiments clearly demonstrates the usefulness of behavioral approach. The book contains also an extensive discussion of implications of the behavioral perspective for program synthesis and beyond.

"Interesting, innovative, and proposes an insightful approach towards the IVHS framework. This is a book every researher should go through for better ideas on communications, computers, or automotive technology." -- Telematics India

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 (Wurzburg, 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.

Proceedings of the 2015 Chinese Intelligent Automation Conference presents selected research papers from the CIAC'15, held in Fuzhou, China. The topics include adaptive control, fuzzy control, neural network based control, knowledge based control, hybrid intelligent control, learning control, evolutionary mechanism based control, multi-sensor integration, failure diagnosis, reconfigurable control, etc. Engineers and researchers from academia, industry and the government can gain valuable insights into interdisciplinary solutions in the field of intelligent automation.

This book presents the development of a theory of social goal-objective formation and its relationship to national interest and social vision under a democratic decision-choice system with imperfect information structure. It provides a framework for the application of fuzzy logic and its mathematics to the analysis in resolving conflicts in individual preferences in the collective decision-choice space without violence. The book demonstrates how to use fuzzy logic and its mathematics in the study of economics, social sciences and other complex systems. It also presents the use of collaborative tools of opposites, duality, polarity, continuum in fuzzy paradigm with its logic, laws of thought and mathematics in developing a new approach to the theory of political economy in order to enhance the constructs of social decision-choice theory.

This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as novel information acquisition and processing algorithms to make sense of the acquired data. The book also highlights emerging trends and advances regarding the applications of neuro-engineering in real-world scenarios, such as neural prostheses, diagnosis of neural degenerative diseases, deep brain stimulation, biosensors, real neural network-inspired artificial neural networks (ANNs) and the predictive modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologies/techniques in the medical domain in the treatment of neural disorders.

This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported. Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented. Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference.

This book aims to provide a detailed understanding of IoMT-supported applications while engaging premium smart computing methods and improved algorithms in the field of computer science. It contains thirteen chapters discussing various applications under the umbrella of the Internet of Medical Things. These applications geared towards IoMT cloud analysis, machine learning, computer vision and deep learning have enabled the evaluation of the proposed solutions.

This book is dedicated to the memory of Professor Zdzis{\l}aw Pawlak who passed away almost six year ago. He is the founder of the Polish school of Artificial Intelligence and one of the pioneers in Computer Engineering and Computer Science with worldwide influence. He was a truly great scientist, researcher, teacher and a human being. This book prepared in two volumes contains more than 50 chapters. This demonstrates that the scientific approaches discovered by of Professor Zdzis{\l}aw Pawlak, especially the rough set approach as a tool for dealing with imperfect knowledge, are vivid and intensively explored by many researchers in many places throughout the world. The submitted papers prove that interest in rough set research is growing and is possible to see many new excellent results both on theoretical foundations and applications of rough sets alone or in combination with other approaches. We are proud to offer the readers this book.

Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs. It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design. Both continuous-time and discrete-time dynamical multi-agent systems are treated. Optimal cooperative control is introduced and neural adaptive design techniques for multi-agent nonlinear systems with unknown dynamics, which are rarely treated in literature are developed. Results spanning systems with first-, second- and on up to general high-order nonlinear dynamics are presented. Each control methodology proposed is developed by rigorous proofs. All algorithms are justified by simulation examples. The text is self-contained and will serve as an excellent comprehensive source of information for researchers and graduate students working with multi-agent systems.

The amount of information that humans have gathered and made available to other humans is phenomenal, yet however large this repository of knowledge is, by this time tomorrow, it will be larger still. Applied Natural Language Processing: Identification, Investigation and Resolution is a volume dedicated to the successful application of processing tools to this information. The majority of this knowledge is expressed through textual media, which requires these tools to utilize the research in the field of Applied Natural Language Processing. This book contains state-of-the-art Applied Natural Language Processing techniques as well as their applications. The descriptions and studies are the product of established researchers in fields relating to ANLP, and this work is relevant to teachers, students, and materials developers in fields spanning computer science, linguistics, and cognitive science.

This book introduces computational proximity (CP) as an algorithmic approach to finding nonempty sets of points that are either close to each other or far apart. Typically in computational proximity, the book starts with some form of proximity space (topological space equipped with a proximity relation) that has an inherent geometry. In CP, two types of near sets are considered, namely, spatially near sets and descriptivelynear sets. It is shown that connectedness, boundedness, mesh nerves, convexity, shapes and shape theory are principal topics in the study of nearness and separation of physical aswell as abstract sets. CP has a hefty visual content. Applications of CP in computer vision, multimedia, brain activity, biology, social networks, and cosmology are included. The book has been derived from the lectures of the author in a graduate course on the topology of digital images taught over the past several years. Many of the students have provided important insights and valuable suggestions. The topics in this monograph introduce many forms of proximities with a computational flavour (especially, what has become known as the strong contact relation), many nuances of topological spaces, and point-free geometry.

In recent years, automation has played a vital role in library systems that handle tasks of acquisition, cataloging, serials, and circulation. The automation of these operations has, in turn, minimised the demand for human interaction. Robots in Academic Libraries: Advancements in Library Automation provides an overview on the current state of library automation, addresses the need for changing personnel to accommodate these changes, and assesses the future for academic libraries as a whole. This book is essential for library leaders, technology experts, and library vendors interested in the future of library automation and its impact on the decline of human interaction in libraries.

Design of cognitive systems for assistance to people poses a major challenge to the fields of robotics and artificial intelligence. The Cognitive Systems for Cognitive Assistance (CoSy) project was organized to address the issues of i) theoretical progress on design of cognitive systems ii) methods for implementation of systems and iii) empirical studies to further understand the use and interaction with such systems. To study, design and deploy cognitive systems there is a need to considers aspects of systems design, embodiment, perception, planning and error recovery, spatial insertion, knowledge acquisition and machine learning, dialog design and human robot interaction and systems integration. The CoSy project addressed all of these aspects over a period of four years and across two different domains of application - exploration of space and task / knowledge acquisition for manipulation. The present volume documents the results of the CoSy project. The CoSy project was funded by the European Commission as part of the Cognitive Systems Program within the 6th Framework Program.

The purpose of the 12th Conference Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2011) held on July 6-8, 2011 in Sydney, Australia was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas and research results about all aspects (theory, applications and tools) of computer and information sciences, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

The conference organizers selected 14 outstanding papers from SNPD 2011, all of which you will find in this volume of Springer's Studies in Computational Intelligence. "

An autonomous sailboat robot is a boat that only uses the wind on its sail as propelling force, without remote control or human assistance to achieve its mission. This involves autonomy in energy (using batteries, solar panels, turbines...), sensor data processing (compass, GPS, wind sensor...), actuators control (rudder and sail angle control...) and decision making (embedded computer with adequate algorithms). Although robotic sailing is a relatively new field of research, several applications exist for this type of robots: oceanographic and hydrographic research, maritime environment monitoring, meteorology, harbor safety, assistance and rescue in dangerous areas...
Over the last decade, several events such as the Microtransat challenge, the WRSC/IRSC and SailBot have been set up to stimulate research and development around robotic sailing. These proceedings cover the current and future academic and technology challenges raised by the development of autonomous sailboat robots presented at the WRSC/IRSC (World Robotic Sailing Championship/International Robotic Sailing Conference) 2013, in Brest, France, 2-6 September 2013.

The book written by Dr. Radu B. Rusu presents a detailed description of 3D Semantic Mapping in the context of mobile robot manipulation. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models that include the objects present in the world, together with their position, form, and other semantic aspects, as well as interpretations of these objects with respect to the robot tasks.

The book proposes novel 3D feature representations called Point Feature Histograms (PFH), as well as a frameworks for the acquisition and processing of Semantic 3D Object Maps with contributions to robust registration, fast segmentation into regions, and reliable object detection, categorization, and reconstruction. These contributions have been fully implemented and empirically evaluated on different robotic systems, and have been the original kernel to the widely successful open-source project the Point Cloud Library (PCL) -- see http: //pointclouds.org.

This book brings together the diversified areas of contemporary computing frameworks in the field of Computer Science, Engineering and Electronic Science. It focuses on various techniques and applications pertaining to cloud overhead, cloud infrastructure, high speed VLSI circuits, virtual machines, wireless and sensor networks, clustering and extraction of information from images and analysis of e-mail texts. The state-of-the-art methodologies and techniques are addressed in chapters presenting various proposals for enhanced outcomes and performances. The techniques discussed are useful for young researchers, budding engineers and industry professionals for applications in their respective fields.

This book presents selected research papers from the 2015 Chinese Intelligent Systems Conference (CISC'15), held in Yangzhou, China. The topics covered include multi-agent systems, evolutionary computation, artificial intelligence, complex systems, computation intelligence and soft computing, intelligent control, advanced control technology, robotics and applications, intelligent information processing, iterative learning control, and machine learning. Engineers and researchers from academia, industry and the government can gain valuable insights into solutions combining ideas from multiple disciplines in the field of intelligent systems.

This book is devoted to Slime mould Physarum polycephalum, which is a large single cell capable for distributed sensing, concurrent information processing, parallel computation and decentralized actuation. The ease of culturing and experimenting with Physarum makes this slime mould an ideal substrate for real-world implementations of unconventional sensing and computing devices The book is a treatise of theoretical and experimental laboratory studies on sensing and computing properties of slime mould, and on the development of mathematical and logical theories of Physarum behavior. It is shown how to make logical gates and circuits, electronic devices (memristors, diodes, transistors, wires, chemical and tactile sensors) with the slime mould. The book demonstrates how to modify properties of Physarum computing circuits with functional nano-particles and polymers, to interface the slime mould with field-programmable arrays, and to use Physarum as a controller of microbial fuel cells. A unique multi-agent model of slime is shown to serve well as a software slime mould capable for solving problems of computational geometry and graph optimization. The multiagent model is complemented by cellular automata models with parallel accelerations. Presented mathematical models inspired by Physarum include non-quantum implementation of Shor's factorization, structural learning, computation of shortest path tree on dynamic graphs, supply chain network design, p-adic computing and syllogistic reasoning. The book is a unique composition of vibrant and lavishly illustrated essays which will inspire scientists, engineers and artists to exploit natural phenomena in designs of future and emergent computing and sensing devices. It is a 'bible' of experimental computing with spatially extended living substrates, it spanstopics from biology of slime mould, to bio-sensing, to unconventional computing devices and robotics, non-classical logics and music and arts.

This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators' and learners' data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: * Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; * Using learning analytics to predict student performance; * Using learning analytics to create learning materials and educational courses; and * Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

This book presents a comprehensive study on intelligent container terminals. Based on the development experience gained to date with container terminals, it analyzes information flows and their interactions with container terminals; illustrates the operation management process from information collection to resource planning and from equipment scheduling to field operation; highlights several dynamic decision-making problems concerning digital operation processes and container terminals; reveals the basis of the discrete logistics system; and discusses the future of intelligent container terminals.

This book is useful to understand and write alongside non-human agents, examine the impact of algorithms and AI on writing, and accommodate relationships with autonomous agents. This ground-breaking future-driven framework prepares scholars and practitioners to investigate and plan for the social, digital literacy, and civic implications arising from emerging technologies. This book prepares researchers, students, practitioners, and citizens to work with AI writers, virtual humans, and social robots. This book explores prompts to envision how fields and professions will change. The book's unique integration with Fabric of Digital Life, a database and structured content repository for conducting social and cultural analysis of emerging technologies, provides concrete examples throughout. Readers gain imperative direction for collaborative, algorithmic, and autonomous writing futures.

This book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book's primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the "spin-net," as a dynamic form of auto-associative memory.