The scope of this volume is to give to the reader a wide scenario of recent works characterized by a synergistic combination of Soft Computing area with recent trends of Distributed Artificial Intelligence and Ambient Intelligence. The editors present two basic paradigms: the emergence of computational intelligence as a mature and integrated science, and the power of the agent paradigm in realizing complex and distributed environments. This book explores these emerging areas inviting well-known authors whose expertise is widely recognized.

One of the most important reasons for the current intensity of interest in agent technology is that the concept of an agent, as an autonomous system capable of interacting with other agents in order to satisfy its design objectives, is a natural one for software designers. Just as we can understand many systems as being composed of essentially passive objects, which have a state and upon which we can perform operations, so we can understand many others as being made up of interacting semi-autonomous agents. This book brings together revised versions of papers presented at the First International Workshop on Agent-Oriented Software Engineering, AOSE 2000, held in Limerick, Ireland, in conjunction with ICSE 2000, and several invited papers. As a comprehensive and competent overview of agent-oriented software engineering, the book addresses software engineers interested in the new paradigm and technology as well as research and development professionals active in agent technology.

Mobile Data Management and Applications brings together in one place important contributions and up-to-date research results in this fast moving area. Mobile Data Management and Applications serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

As interest in computer, cognitive, and social sciences grow, the need for alternative approaches to models in related-disciplines thrives. ""An Imitation-Based Approach to Modeling Homogeneous Agents Societies"" offers a framework for modeling societies of autonomous agents that is heavily based on fuzzy algebraic tools. This publication overviews platforms developed with the purpose of simulating hypotheses or harvesting data from human subjects in efforts for calibration of the model of early learning in humans. ""An Imitation-Based Approach to Modeling Homogeneous Agents Societies"" reaches out to the cognitive sciences, psychology, and anthropology providing a different perspective on a few ""classical"" problems within these fields.

Wireless Sensor Network Technologies for Information Explosion Era The amount and value of information available due to rapid spread of information technology is exploding. Typically, large enterprises have approximately a petabyte of operational data stored in hundreds of data repositories supporting thousands of applications. Data storage volumes grow in excess of 50% annually. This growth is expected to continue due to vast proliferation of existing infor- tion systems and the introduction of new data sources. Wireless Sensor Networks (WSNs) represent one of the most notable examples of such new data sources. In recent few years, various types of WSNs have been deployed and the amount of information generated by wireless sensors increases rapidly. The information - plosion requires establishing novel data processing and communication techniques for WSNs. This volume aims to cover both theoretical and practical aspects - lated to this challenge, and it explores directions for future research to enable ef- cient utilization of WSNs in the information-explosion era. The book is organized in three main parts that consider (1) technical issues of WSNs, (2) the integration of multiple WSNs, and (3) the development of WSNs systems and testbeds for conducting practical experiments. Each part consists of three chapters.

This book presents latest results and selected applications of Computational Intelligence in Biomedical Technologies. Most of contributions deal with problems of Biomedical and Medical Informatics, ranging from theoretical considerations to practical applications. Various aspects of development methods and algorithms in Biomedical and Medical Informatics as well as Algorithms for medical image processing, modeling methods are discussed. Individual contributions also cover medical decision making support, estimation of risks of treatments, reliability of medical systems, problems of practical clinical applications and many other topics. This book is intended for scientists interested in problems of Biomedical Technologies, for researchers and academic staff, for all dealing with Biomedical and Medical Informatics, as well as PhD students. Useful information is offered also to IT companies, developers of equipment and/or software for medicine and medical professionals.

This monograph presents a comprehensive study of portfolio optimization, an important area of quantitative finance. Considering that the information available in financial markets is incomplete and that the markets are affected by vagueness and ambiguity, the monograph deals with fuzzy portfolio optimization models. At first, the book makes the reader familiar with basic concepts, including the classical mean-variance portfolio analysis. Then, it introduces advanced optimization techniques and applies them for the development of various multi-criteria portfolio optimization models in an uncertain environment. The models are developed considering both the financial and non-financial criteria of investment decision making, and the inputs from the investment experts. The utility of these models in practice is then demonstrated using numerical illustrations based on real-world data, which were collected from one of the premier stock exchanges in India. The book addresses both academics and professionals pursuing advanced research and/or engaged in practical issues in the rapidly evolving field of portfolio optimization.

This book covers the results obtained in the Tera op Workbench project during a four years period from 2004 to 2008. The Tera op Workbench project is a colla- ration betweenthe High PerformanceComputingCenter Stuttgart (HLRS) and NEC Deutschland GmbH (NEC-HPCE) to support users to achieve their research goals using high performance computing. The Tera op Workbench supports users of the HLRS systems to enable and - cilitate leading edge scienti c research. This is achieved by optimizing their codes and improving the process work ow which results from the integration of diff- ent modules into a "hybrid vector system". The assessment and demonstration of industrial relevance is another goal of the cooperation. The Tera op Workbench project consists of numerous individual codes, grouped together by application area and developed and maintained by researchers or c- mercial organizations. Within the project, several of the codes have shown the ab- ity to reach beyond the TFlop/s threshold of sustained performance. This created the possibility for new science and a deeper understanding of the underlying physics. The papers in this book demonstrate the value of the project for different scienti c areas.

Vehicle accidents on the roads and highways occur every minute of every day, most often resulting in a loss of life or property damage. With advancing technology, vehicle infrastructure integration can increase road safety and transport efficiency through wireless sensor communications and other systems. These recent developments can bring inestimable economic value and will play a role in the next generation of vehicle products and traffic safety. Global Advancements in Connected and Intelligent Mobility: Emerging Research and Opportunities is an essential reference source that discusses the recent advances, safety, and efficiency in connected vehicles, as well as the next generation of communication network development. Featuring research on topics such as vehicular networks, telematics, and context-aware intelligence, this book is ideally designed for policymakers, traffic safety specialists, traffic control technicians, auto technicians, planning agencies, environmental managers, standardization governors, academicians, students, researchers, and industry practitioners seeking coverage on intelligent transportation systems.

Blockchain technologies, as an emerging distributed architecture and computing paradigm, have accelerated the development/application of the Cloud/GPU/Edge Computing, Artificial Intelligence, cyber physical systems, social networking, crowdsourcing and crowdsensing, 5G, trust management, and finance. The popularity and rapid development of Blockchain brings many technical and regulatory challenges for research and academic communities. This book will feature contributions from experts on topics related to performance, benchmarking, durability, robustness, as well data gathering and management, algorithms, analytics techniques for transactions processing, and implementation of applications.

There are many myths about Artificial Intelligence (AI) relating to what it is and what it can and cannot do. The people making decisions on AI projects are often not technologically savvy and unable to find easy answers. The spending on and the returns from AI projects are not necessarily straightforward. Part of the reason for this is the lack of understanding of the impact of critical decision criteria. AI touches on many ethical concepts - data privacy, validity, and, more importantly, its potential misuse. AI often replaces human decision-making, as managers do not clearly understand the implications of those choices. This book provides an easy and accessible guide for practitioners without a technological background to understand AI. It guides the reader through the fundamental issues confronting decision-makers. It offers advice on 'how to ask relevant questions' using the 15 decision scales. There is currently no comparable book on the market that acts as a pocketbook management reference guide for the AI layman.

This book is loaded with examples in which computer scientists and engineers have used evolutionary computation - programs that mimic natural evolution - to solve many real-world problems. They aren t abstract, mathematically intensive papers, but accounts of solving important problems, including tips from the authors on how to avoid common pitfalls, maximize the effectiveness and efficiency of the search process, and many other practical suggestions.

Throughout time, scientists have looked to nature in order to understand and model solutions for complex real-world problems. In particular, the study of self-organizing entities, such as social insect populations, presents a new opportunity within the field of artificial intelligence. >Emerging Research on Swarm Intelligence and Algorithm Optimization discusses current research analyzing how the collective behavior of decentralized systems in the natural world can be applied to intelligent system design. Discussing the application of swarm principles, optimization techniques, and key algorithms being used in the field, this publication serves as an essential reference for academicians, upper-level students, IT developers, and IT theorists.

Computational Intelligence is comparatively a new field but it has made a tremendous progress in virtually every discipline right from engineering, science, business, m- agement, aviation to healthcare. Computational intelligence already has a solid track-record of applications to healthcare, of which this book is a continuation. We would like to refer the reader to the excellent previous volumes in this series on computational intelligence in heal- care [1-3]. This book is aimed at providing the most recent advances and state of the art in the practical applications of computational intelligence paradigms in healthcare. It - cludes nineteen chapters on using various computational intelligence methods in healthcare such as intelligent agents and case-based reasoning. A number of fielded applications and case studies are presented. Highlighted are in particular novel c- putational approaches to the semantic management of health information such as in the Web 2.0, mobile agents such as in portable devices, learning agents capable of adapting to diverse clinical settings through case-based reasoning, and statistical - proaches in computational intelligence. This book is targeted towards scientists, application engineers, professors, health professionals, professors, and students. Background information on computational intelligence has been provided whenever necessary to facilitate the comprehension of a broad audience including healthcare practitioners.

This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

In this book, practicing physicians and experts in anticipation present arguments for a new understanding of medicine. Their contributions make it clear that medicine is the decisive test for anticipation. The reader is presented with a provocative hypothesis: If medicine will align itself with the anticipatory condition of life, it can prompt the most important revolution in our time. To this end, all stakeholders-medical practitioners, patients, scientists, and technology developers-will have to engage in the conversation. The book makes the case for the transition from expensive, and only marginally effective, reactive treatment through "spare parts" (joint replacements, organ transplants) and reliance on pharmaceuticals (antibiotics, opiates) to anticipation-informed healthcare. Readers will understand why the current premise of treating various behavioral conditions (attention deficit disorder, hyperactivity, schizophrenia) through drugs has to be re-evaluated from the perspective of anticipation. In the manner practiced today, medicine generates dependence and long-lasting damage to those it is paid to help. As we better understand the nature of the living, the proactive view of healthcare, within which the science and art of healing fuse, becomes a social and political mandate.

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.