Reasoning and Unification over Conceptual Graphs is an exploration of automated reasoning and resolution in the expanding field of Conceptual Structures. Designed not only for computing scientists researching Conceptual Graphs, but also for anyone interested in exploring the design of knowledge bases, the book explores what are proving to be the fundamental methods for representing semantic relations in knowledge bases. While it provides the first comprehensive treatment of Conceptual Graph unification and reasoning, the book also addresses fundamental issues of graph matching, automated reasoning, knowledge bases, constraints, ontology and design. With a large number of examples, illustrations, and both formal and informal definitions and discussions, this book is excellent as a tutorial for the reader new to Conceptual Graphs, or as a reference book for a senior researcher in Artificial Intelligence, Knowledge Representation or Automated Reasoning.

Traditional methods for creating intelligent computational systems have
privileged private "internal" cognitive and computational processes. In
contrast, "Swarm Intelligence" argues that human
intelligence derives from the interactions of individuals in a social world
and further, that this model of intelligence can be effectively applied to
artificially intelligent systems. The authors first present the foundations of
this new approach through an extensive review of the critical literature in
social psychology, cognitive science, and evolutionary computation. They
then show in detail how these theories and models apply to a new
computational intelligence methodology particle swarms which focuses
on adaptation as the key behavior of intelligent systems. Drilling down
still further, the authors describe the practical benefits of applying particle
swarm optimization to a range of engineering problems. Developed by
the authors, this algorithm is an extension of cellular automata and
provides a powerful optimization, learning, and problem solving method.

This important book presents valuable new insights by exploring the
boundaries shared by cognitive science, social psychology, artificial life,
artificial intelligence, and evolutionary computation and by applying these
insights to the solving of difficult engineering problems. Researchers and
graduate students in any of these disciplines will find the material
intriguing, provocative, and revealing as will the curious and savvy
computing professional.
* Places particle swarms within the larger context of intelligent
adaptive behavior and evolutionary computation.
* Describes recent results of experiments with the particle swarm
optimization (PSO) algorithm
* Includes a basic overview of statistics to ensure readers can
properly analyze the results of their own experiments using the
algorithm.
* Support software which can be downloaded from the publishers
website, includes a Java PSO applet, C and Visual Basic source
code."

Recently, a new class of heuristic techniques, the swarm intelligence has emerged. In this context, more recently, biologists and computer scientists in the ?eld of"arti?cial life"have been turning to insects for ideas that can be used for heuristics. Many aspects of the collective activities of social insects, such as foraging of ants, birds ?ocking and ?sh schooling are self-organizing, meaning that complex group behavior emerges from the interactions of in- viduals who exhibit simple behaviors by themselves. Swarm intelligence is an innovative computational way to solving hard problems. This discipline is mostly inspired by the behavior of ant colonies, bird ?ocks and ?sh schools and other biological creatures. In general, this is done by mimicking the behavior of these swarms. Swarm intelligence is an emerging research area with similar population and evolution characteristics to those of genetic algorithms. However, it di?erentiates in emphasizing the cooperative behavior among group m- bers. Swarm intelligence is used to solve optimization and cooperative pr- lems among intelligent agents, mainly in arti?cial network training, co- erative and/or decentralized control, operational research, power systems, electro-magnetics device design, mobile robotics, and others. The most we- knownrepresentativesofswarmintelligenceinoptimizationproblemsare:the food-searching behavior of ants, particle swarm optimization, and bacterial colonies. Real-world engineering problems often require concurrent optimization of several design objectives, which are con?icting in most of the cases. Such an optimization is generally called multi-objective or multi-criterion optimi- tion.Inthis context,the developmentofimprovementsfor swarmintelligence methods to multi-objective problems is an emergent research area.

This book aims to understand human cognition and psychology through a comprehensive computational theory of the human mind, namely, a computational "cognitive architecture" (or more specifically, the CLARION cognitive architecture). The goal of this work is to develop a unified framework for understanding the human mind, and within the unified framework, to develop process-based, mechanistic explanations of a large variety of psychological phenomena. Specifically, the book first describes the essential CLARION framework and its cognitive-psychological justifications, then its computational instantiations, and finally its applications to capturing, simulating, and explaining various psychological phenomena and empirical data. The book shows how the models and simulations shed light on psychological mechanisms and processes through the lens of a unified framework. In fields ranging from cognitive science, to psychology, to artificial intelligence, and even to philosophy, researchers, graduate and undergraduate students, and practitioners of various kinds may have interest in topics covered by this book. The book may also be suitable for seminars or courses, at graduate or undergraduate levels, on cognitive architectures or cognitive modeling (i.e. computational psychology).

This volume presents the work of leading scientists from Russia, Georgia, Estonia, Lithuania, Israel and the USA, revealing major insights long unknown to the scientific community. Without any doubt their work will provide a springboard for further research in anticipation. Until recently, Robert Rosen (Anticipatory Systems) and Mihai Nadin (MIND - Anticipation and Chaos) were deemed forerunners in this still new knowledge domain. The distinguished neurobiologist, Steven Rose, pointed to the fact that Soviet neuropsychological theories have not on the whole been well received by Western science. These earlier insights as presented in this volume make an important contribution to the foundation of the science of anticipation. It is shown that the daring hypotheses and rich experimental evidence produced by Bernstein, Beritashvili, Ukhtomsky, Anokhin and Uznadze, among others-extend foundational work to aspects of neuroscience, physiology, motorics, education.

The book discusses the impact of machine learning and computational intelligent algorithms on medical image data processing, and introduces the latest trends in machine learning technologies and computational intelligence for intelligent medical image analysis. The topics covered include automated region of interest detection of magnetic resonance images based on center of gravity; brain tumor detection through low-level features detection; automatic MRI image segmentation for brain tumor detection using the multi-level sigmoid activation function; and computer-aided detection of mammographic lesions using convolutional neural networks.

This book reports on an outstanding thesis that has significantly advanced the state-of-the-art in the area of automated negotiation. It gives new practical and theoretical insights into the design and evaluation of automated negotiators. It describes an innovative negotiating agent framework that enables systematic exploration of the space of possible negotiation strategies by recombining different agent components. Using this framework, new and effective ways are formulated for an agent to learn, bid, and accept during a negotiation. The findings have been evaluated in four annual instantiations of the International Automated Negotiating Agents Competition (ANAC), the results of which are also outlined here. The book also describes several methodologies for evaluating and comparing negotiation strategies and components, with a special emphasis on performance and accuracy measures.

Recently, the study of intelligenceemerged from interactionsamong many agentshasbeenpopular. Inthisstudyitisrecognizedthatanetworkstructure oftheagentsplaysanimportantrole. Thecurrentstate-of-theartinage- based modeling tends to be a mass of agents that have a series of states thattheycanexpressasaresultofthenetworkstructureinwhichtheyare embedded. Agentinteractionsofallkindsareusuallystructuredwithcomplex networks. Researchoncomplexnetworksfocusesonscale-freenessofvarious kindofnetworks. Computationalmodelingofdynamicagentinteractionsonrichlystr- turednetworksisimportantforunderstandingthesometimescounter-intuitive dynamicsofsuchlooselycoupledsystemsofinteractions. Yetourtoolsto model, understand, andpredictdynamicagentinteractionsandtheirbe- vioroncomplexnetworkshavelaggedfarbehind. Evenrecentprogressin networkmodelinghasnotyeto?eredusanycapabilitytomodeldynamic processesamongagentswhointeractatallscalesonsuchassmall-worldand scale-freenetworks. Generallythehigh-dimensional, non-linearnatureofthe resultingnetwork-centricmulti-agentsystemsmakesthemdi?cultorimp- sibletoanalyzeusingtraditionalmethods. Agentsfollowlocalrulesunder complexnetworkconstraints. Theideaofcombiningmulti-agentsystemsand complexnetworksisalsoparticularlyrichandfreshtofostertheresearchon thestudyofverylarge-scalemulti-agentsystems. Weintendtoturnthisintoanengineeringmethodologytodesigncomplex agentnetworks. Multi-agentnetworkdynamicsinvolvesthestudyofmany agents, constituentcomponentsgenerallyactiveoneswithasimplestructures andwhosebehaviorisassumedtofollowlocalrules, andtheirinteractionson complexnetwork. Abasicmethodologyistospecifyhowtheagentsinteract, andthenobserveemergentintelligencethatoccuratthecollectivelevelin ordertodiscoverbasicprinciplesandkeymechanismsforunderstandingand shapingtheresultingintelligentbehavioronnetworkdynamics. Thevolumecontainsrefereedpapersaddressingvariousimportanttopics thataimsattheinvestigationofemergentintelligenceonnetworkedagents. vi Preface Especiallymostpapershighlightonthetopicssuch"networkformationamong agents," "in?uenceofnetworkstructuresonagents," "network-basedcoll- tivephenomenaandemergentintelligenceonnetworkedagents." TheselectedpapersofthisvolumewerepresentedattheWorkshopon EmergentIntelligenceofNetworkedAgents(WEIN06)attheFifthInt- nationalJointConferenceonAutonomousAgentsandMulti-agentSystems (AAMAS2006), whichwasheldatFutureUniversity, Hakodate, Japan, from May8to12,2006. WEIN06isconcernedwithemergenceofintelligentbe- viorsovernetworkedagents andfosteringtheformationofanactivemul- disciplinarycommunityonmulti-agentsystemsandcomplexnetworks. We especiallyintendedtoincreasetheawarenessofresearchersinthesetwo?elds sharingthecommonviewoncombiningagent-basedmodelingandcomplex networksinordertodevelopinsightandfosterpredictivemethodologiesin studyingemergentintelligenceonofnetworkedagents. Fromthebroadsp- trumofactivities, leadingexpertspresentedimportantpaperandnumerous practicalproblemsappearthroughoutthisbook. Weinvitedhighqualityc- tributionsonawidevarietyoftopicsrelevanttothewideresearchareasof multi-agentnetworkdynamics. Weespeciallycoveredin-depthofimportant areas including: Adaptation and evolution in complex networks, Economic agentsandcomplexnetworks, Emergenceincomplexnetworks, Emergent- telligenceinmulti-agentsystems, Collectiveintelligence, Learningandevo- tioninmulti-agentsystems, Webdynamicsascomplexnetworks, Multi-agent basedsupplynetworks, Network-centricagentsystems, Scalabilityinmul- agentsystems, Scale-freenetworks, Small-worldnetworks.

This volume contains a total of thirteen papers covering a variety of AI topics ranging from computer vision and robotics to intelligent modeling, neural networks and fuzzy logic. There are two general articles on robotics and fuzzy logic. The article on robotics focuses on the application of robotics technology in plant production. The second article on fuzzy logic provides a general overview of the basics of fuzzy logic and a typical agricultural application of fuzzy logic. The article End effectors for tomato harvesting' enhances further the robotic research as applied to tomato harvesting. The application of computer vision techniques for different biological/agricultural applications, for example, length determination of cheese threads, recognition of plankton images and morphological identification of cotton fibers, depicts the complexity and heterogeneities of the problems and their solutions. The development of a real-time orange grading system in the article Video grading of oranges in real-time' further reports the capability of computer vision technology to meet the demand of high quality food products. The integration of neural network technology with computer vision and fuzzy logic for defect detection in eggs and identification of lettuce growth shows the power of hybridization of AI technologies to solve agricultural problems. Additional papers also focus on automated modeling of physiological processes during postharvest distribution of agricultural products, the applications of neural networks, fusion of AI technologies and three dimensional computer vision technologies for different problems ranging from botanical identification and cell migration analysis to foodmicrostructure evaluation.

This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents diverse applications of nature-inspired optimization algorithms. The sixth part contains papers describing new optimization algorithms. The seventh part contains papers describing applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. Finally, the eighth part contains papers that present enhancements to meta-heuristics based on fuzzy logic techniques.

Despite differing origins, constraint programming and mathematical programming are beginning to merge. Constraint programming has grown out of the logic programming community as part of an effort to embed constraints in a programming language. Mathematical programming, a much older field, is rooted in the mathematics of optimization. Because these two areas have complementary strengths, there are ongoing efforts to integrate the two.Constraint and Integer Programming presents some of the basic ideas of constraint programming and mathematical programming, explores approaches to integration, brings us up to date on heuristic methods, and attempts to discern future directions in this fast-moving field.
Constraint and Integer Programming is organized as follows: Chapter 1 is a high level overview of the main concepts of Constraint Programming (CP) and Integer Programming (IP) used in the book. Chapter 2 informally introduces integration methods describing and classifying the main works in the field. Chapter 3 outlines a unifying framework which involves the main concepts of CP and IP, underlining similarities and differences, and stating the basis for possible integrations. Chapter 4 describes global constraints as a vehicle for integrating IP concepts in CP in a transparent way for the user. Chapter 5 presents various ways to integrate relaxations in Constraint Programming focussing on global constraints. Chapter 6 presents hybrid solvers and Chapter 7 outlines Column Generation and its integration in Constraint Programming. Chapter 8 examines randomization and problem structure as a basis for understanding the intrinsic difficulty of the combinatorial problems. Chapter 9 studies the use of local search and meta-heuristics in CP. Chapter 10 is devoted to open perspectives and future directions.

As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining.This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.

Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for optimality is challenging, either because of the high computational cost of evaluating objectives and constraints, or because of the nonlinearity, multimodality, discontinuity and uncertainty of the problem functions in the real-world systems. Another complication is that most problems are often NP-hard, that is, the solution time for finding the optimum increases exponentially with the problem size. The development of efficient algorithms and specialized techniques that address these difficulties is of primary importance for contemporary engineering, science and industry. This book consists of 12 self-contained chapters, contributed from worldwide experts who are working in these exciting areas. The book strives to review and discuss the latest developments concerning optimization and modelling with a focus on methods and algorithms for computational optimization. It also covers well-chosen, real-world applications in science, engineering and industry. Main topics include derivative-free optimization, multi-objective evolutionary algorithms, surrogate-based methods, maximum simulated likelihood estimation, support vector machines, and metaheuristic algorithms. Application case studies include aerodynamic shape optimization, microwave engineering, black-box optimization, classification, economics, inventory optimization and structural optimization. This graduate level book can serve as an excellent reference for lecturers, researchers and students in computational science, engineering and industry.

The recent and novel research contributions collected in this book are extended and
reworked versions of a selection of the best papers that were originally presented in
French at the EGC 2011 Conference held in Brest, France, on January 2011.
EGC stands for "Extraction et Gestion des connaissances" in French, and means "Knowledge Discovery and Management" or KDM.
KDM is concerned with the works in computer science at the interface between data and knowledge; such as Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and Semantic Web.
This book is intended to be read by all researchers interested in these fields, including
PhD or MSc students, and researchers from public or private laboratories. It
concerns both theoretical and practical aspects of KDM.
This book has been structured in two parts.
The first part, entitled Data Mining, classification and queries, deals with rule and pattern mining, with topological approaches
and with OLAP.
The second part of the book, entitled Ontology and Semantic, is related
to knowledge-based and user-centered approaches in KDM.

May the Forcing Functions be with You: The Stimulating World of AIED and ITS Research It is my pleasure to write the foreword for Advances in Intelligent Tutoring S- tems. This collection, with contributions from leading researchers in the field of artificial intelligence in education (AIED), constitutes an overview of the many challenging research problems that must be solved in order to build a truly intel- gent tutoring system (ITS). The book not only describes some of the approaches and techniques that have been explored to meet these challenges, but also some of the systems that have actually been built and deployed in this effort. As discussed in the Introduction (Chapter 1), the terms "AIED" and "ITS" are often used int- changeably, and there is a large overlap in the researchers devoted to exploring this common field. In this foreword, I will use the term "AIED" to refer to the - search area, and the term "ITS" to refer to the particular kind of system that AIED researchers build. It has often been said that AIED is "AI-complete" in that to produce a tutoring system as sophisticated and effective as a human tutor requires solving the entire gamut of artificial intelligence research (AI) problems.

The primary aim of this up-to-date research book is to report a sample of the most recent advances in the field of intelligent interactive systems in knowledge-based environments. It contains recent research and case studies of intelligent interactive systems. This book will prove useful to researchers, professors, research students and practitioners as it reports novel research work on innovative topics in the area of intelligent interactive systems.

Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences. After discussing the characterizing properties of abstraction, a formal model, the KRA model, is presented to capture them. This model makes the notion of abstraction easily applicable by means of the introduction of a set of abstraction operators and abstraction patterns, reusable across different domains and applications. It is the impact of abstraction in Artificial Intelligence, Complex Systems and Machine Learning which creates the core of the book. A general framework, based on the KRA model, is presented, and its pragmatic power is illustrated with three case studies: Model-based diagnosis, Cartographic Generalization, and learning Hierarchical Hidden Markov Models.

The book provides a platform for dealing with the flaws and failings of the soft computing paradigm through different manifestations. The different chapters highlight the necessity of the hybrid soft computing methodology in general with emphasis on several application perspectives in particular. Typical examples include (a) Study of Economic Load Dispatch by Various Hybrid Optimization Techniques, (b) An Application of Color Magnetic Resonance Brain Image Segmentation by Para Optimus LG Activation Function, (c) Hybrid Rough-PSO Approach in Remote Sensing Imagery Analysis, (d) A Study and Analysis of Hybrid Intelligent Techniques for Breast Cancer Detection using Breast Thermograms, and (e) Hybridization of 2D-3D Images for Human Face Recognition. The elaborate findings of the chapters enhance the exhibition of the hybrid soft computing paradigm in the field of intelligent computing.

The book covers four research domains representing a trend for modern manufacturing control: Holonic and Multi-agent technologies for industrial systems; Intelligent Product and Product-driven Automation; Service Orientation of Enterprise s strategic and technical processes; and Distributed Intelligent Automation Systems. These evolution lines have in common concepts related to "service orientation" derived from the Service Oriented Architecture (SOA) paradigm.

The service-oriented multi-agent systems approach discussed in the book is characterized by the use of a set of distributed autonomous and cooperative agents, embedded in smart components that use the SOA principles, being oriented by offer and request of services, in order to fulfil production systems and value chain goals.

A new integrated vision combining emergent technologies is offered, to create control structures with distributed intelligence supporting the vertical and horizontal enterprise integration and running in truly distributed and global working environments.

The service value creation model at enterprise level consists into using Service Component Architectures for business process applications, based on entities which handle services. In this componentization view, a service is a piece of software encapsulating the business/control logic or resource functionality of an entity that exhibits an individual competence and responds to a specific request to fulfil a local (product) or global (batch) objective.

The service value creation model at enterprise level consists into using Service Component Architectures for business process applications, based on entities which handle services. In this componentization view, a service is a piece of software encapsulating the business/control logic or resource functionality of an entity that exhibits an individual competence and responds to a specific request to fulfil a local (product) or global (batch) objective.

Self-organizing approaches inspired from biological systems, such as social insects, genetic, molecular and cellular systems under morphogenesis, and human mental development, has enjoyed great success in advanced robotic systems that need to work in dynamic and changing environments. Compared with classical control methods for robotic systems, the major advantages of bio-inspired self-organizing robotic systems include robustness, self-repair and self-healing in the presence of system failures and/or malfunctions, high adaptability to environmental changes, and autonomous self-organization and self-reconfiguration without a centralized control. "Bio-inspired Self-organizing Robotic Systems" provides a valuable reference for scientists, practitioners and research students working on developing control algorithms for self-organizing engineered collective systems, such as swarm robotic systems, self-reconfigurable modular robots, smart material based robotic devices, unmanned aerial vehicles, and satellite constellations.

This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC - beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solving prowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly - thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics. The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential.

This book covers the latest advances in playful user interfaces - interfaces that invite social and physical interaction. These new developments include the use of audio, visual, tactile and physiological sensors to monitor, provide feedback and anticipate the behavior of human users. The decreasing cost of sensor and actuator technology makes it possible to integrate physical behavior information in human-computer interactions. This leads to many new entertainment and game applications that allow or require social and physical interaction in sensor- and actuator-equipped smart environments. The topics discussed include: human-nature interaction, human-animal interaction and the interaction with tangibles that are naturally integrated in our smart environments. Digitally supported remote audience participation in artistic or sport events is also discussed. One important theme that emerges throughout the book is the involvement of users in the digital-entertainment design process or even design and implementation of interactive entertainment by users themselves, including children doing so in educational settings.

This book introduces approaches that have the potential to transform the daily practice of psychiatrists and psychologists. This includes the asynchronous communication between mental health care providers and clients as well as the automation of assessment and therapy.

Speech and language are particularly interesting from the viewpoint of psychological assessment. For instance, depression may change the characteristics of voice in individuals and these changes can be detected by a special form of speech analysis. Computational screening methods that utilize speech and language can detect subtle changes and alert clinicians as well as individuals and caregivers.

The use of online technologies in mental health, however, poses ethical problems that will occupy concerned individuals, governments and the wider public for some time. Assuming that these ethical problems can be solved, it should be possible to diagnose and treat mental health disorders online (excluding the use of medication).

Speech and language are particularly interesting from the viewpoint of psychological assessment. For instance, depression may change the characteristics of voice in individuals and these changes can be detected by a special form of speech analysis. Computational screening methods that utilize speech and language can detect subtle changes and alert clinicians as well as individuals and caregivers.

The use of online technologies in mental health, however, poses ethical problems that will occupy concerned individuals, governments and the wider public for some time. Assuming that these ethical problems can be solved, it should be possible to diagnose and treat mental health disorders online (excluding the use of medication).

Since its origination in the mid-twentieth century, the area of Artificial Intelligence (AI) has undergone a number of developments. While the early interest in AI was mainly triggered by the desire to develop artifacts that show the same intelligent behavior as humans, nowadays scientists have realized that research in AI involves a multitude of separate challenges, besides the traditional goal to replicate human intelligence. In particular, recent history has pointed out that a variety of 'intelligent' computational techniques, part of which are inspired by human intelligence, may be successfully applied to solve all kinds of practical problems. This sub-area of AI, which has its main emphasis on applications of intelligent systems to solve real-life problems, is currently known under the term Applied Intelligence. The objective of the International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE) is to promote and disseminate recent research developments in Applied Intelligence. The current book contains 30 chapters authored by participants of the 26th edition of IEA/AIE, which was held in Amsterdam, the Netherlands. The material of each chapter is self-contained and was reviewed by at least two anonymous referees, to assure a high quality. Readers can select any individual chapter based on their research interests without the need of reading other chapters. We are confident that this book provides useful reference values to researchers and students in the field of Applied Intelligence, enabling them to find opportunities and recognize challenges in the field.

This book highlights electromagnetic actuation (EMA) and sensing systems for a broad range of applications including targeted drug delivery, drug-release-rate control, catheterization, intravitreal needleless injections, wireless magnetic capsule endoscopy, and micromanipulations. It also reviews the state-of-the-art magnetic actuation and sensing technologies with remotely controlled targets used in biomedicine.