Effective decision-making while trading off the constraints and conflicting multiple objectives under rapid technological developments, massive generation of data, and extreme volatility is of paramount importance to organizations to win over the time-based competition today. While agility is a crucial issue, the firms have been increasingly relying on evidence-based decision-making through intelligent decision support systems driven by computational intelligence and automation to achieve a competitive advantage.  The decisions are no longer confined to a specific functional area. Instead, business organizations today find actionable insight for formulating future courses of action by integrating multiple objectives and perspectives. Therefore, multi-objective decision-making plays a critical role in businesses and industries. In this regard, the importance of Operations Research (OR) models and their applications enables the firms to derive optimum solutions subject to various constraints and/or objectives while considering multiple functional areas of the organizations together. Hence, researchers and practitioners have extensively applied OR models to solve various organizational issues related to manufacturing, service, supply chain and logistics management, human resource management, finance, and market analysis, among others. Further, OR models driven by AI have been enabled to provide intelligent decision-support frameworks for achieving sustainable development goals. The present issue provides a unique platform to showcase the contributions of the leading international experts on production systems and business from academia, industry, and government to discuss the issues in intelligent manufacturing, operations management, financial management, supply chain management, and Industry 4.0 in the Artificial Intelligence era. Some of the general (but not specific) scopes of this proceeding entail OR models such as Optimization and Control, Combinatorial Optimization, Queuing Theory, Resource Allocation Models, Linear and Nonlinear Programming Models, Multi-objective and multi-attribute Decision Models, Statistical Quality Control along with AI, Bayesian Data Analysis, Machine Learning and Econometrics and their applications vis-à-vis AI & Data-driven Production Management, Marketing and Retail Management, Financial Management, Human Resource Management, Operations Management, Smart Manufacturing & Industry 4.0, Supply Chain and Logistics Management, Digital Supply Network, Healthcare Administration, Inventory Management, consumer behavior, security analysis, and portfolio management and sustainability.   The present issue shall be of interest to the faculty members, students, and scholars of various engineering and social science institutions and universities, along with the practitioners and policymakers of different industries and organizations.

This book constitutes the refereed proceedings of the Third International Workshop on Multiple Classifier Systems, MCS 2002, held in Cagliari, Italy, in June 2002.The 29 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on bagging and boosting, ensemble learning and neural networks, design methodologies, combination strategies, analysis and performance evaluation, and applications.

This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Learning Classifier Systems, IWLCS 2001, held in San Francisco, CA, USA, in July 2001.The 12 revised full papers presented together with a special paper on a formal description of ACS have gone through two rounds of reviewing and improvement. The first part of the book is devoted to theoretical issues of learning classifier systems including the influence of exploration strategy, self-adaptive classifier systems, and the use of classifier systems for social simulation. The second part is devoted to applications in various fields such as data mining, stock trading, and power distributionn networks.

Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style. The five chapters in this book illustrate the range of tasks and design choices in current music generation research applying machine learning techniques and highlighting recurring research issues such as training data, music representation, candidate generation, and evaluation. The contributions focus on different aspects of modeling and generating music, including melody, chord sequences, ornamentation, and dynamics. Models are induced from audio data or symbolic data. This book was originally published as a special issue of the Journal of Mathematics and Music.

This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25-28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi?cance, o- ginality, and relevance to theory and practice of machine learning. Additionally, the volume contains the invited talks of ALT 2001 presented by Dana Angluin of Yale University, USA, Paul R. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan. Furthermore, this volume includes abstracts of the invited talks for DS 2001 presented by Lindley Darden and Ben Shneiderman both of the University of Maryland at College Park, USA. The complete versions of these papers are published in the DS 2001 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2226).

This book constitutes the refereed proceedings of the Third International Workshop on Ant Algorithms, ANTS 2002, held in Brussels, Belgium in September 2002.The 17 revised full papers, 11 short papers, and extended poster abstracts presented were carefully reviewed and selected from 52 submissions. The papers deal with theoretical and foundational aspects and a variety of new variants of ant algorithms as well as with a broad variety of optimization applications in networking and operations research. All in all, this book presents the state of the art in research and development in the emerging field of ant algorithms

This book constitutes the refereed preceedings of the 13th European Conference on Machine Learning, ECML 2002, held in Helsinki, Finland in August 2002.The 41 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are computational discovery, search strategies, Classification, support vector machines, kernel methods, rule induction, linear learning, decision tree learning, boosting, collaborative learning, statistical learning, clustering, instance-based learning, reinforcement learning, multiagent learning, multirelational learning, Markov decision processes, active learning, etc.

This book constitutes the refereed proceedings of the 15th Annual Conference on Computational Learning Theory, COLT 2002, held in Sydney, Australia, in July 2002.The 26 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on statistical learning theory, online learning, inductive inference, PAC learning, boosting, and other learning paradigms.

This book presents the thoroughly refereed post-proceedings of the Second International Workshop on Intelligent Memory Systems, IMS 2000, held in Cambridge, MA, USA, in November 2000.The nine revised full papers and six poster papers presented were carefully reviewed and selected from 28 submissions. The papers cover a wide range of topics in intelligent memory computing; they are organized in topical sections on memory technology, processor and memory architecture, applications and operating systems, and compiler technology.

This book constitutes the refereed proceedings of the 11th International Conference on Inductive Logic Programming, ILP 2001, held in Strasbourg, France in September 2001.The 21 revised full papers presented were carefully reviewed and selected from 37 submissions. Among the topics addressed are data mining issues for multi-relational databases, supervised learning, inductive inference, Bayesian reasoning, learning refinement operators, neural network learning, constraint satisfaction, genetic algorithms, statistical machine learning, transductive inference, etc.

This book constitutes the refereed proceedings of the 12th European Conference on Machine Learning, ECML 2001, held in Freiburg, Germany, in September 2001.The 50 revised full papers presented together with four invited contributions were carefully reviewed and selected from a total of 140 submissions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, decision tree-based classification, Web mining, equation discovery, inductive logic programming, text categorization, agent learning, backpropagation, reinforcement learning, sequence prediction, sequential decisions, classification learning, sampling, and semi-supervised learning.

Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.

Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.

Machine learning applications autonomously reason about data at massive scale. It's important that they remain responsive in the face of failure and changes in load. But machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. Reactive Machine Learning Systems teaches readers how to implement reactive design solutions in their machine learning systems to make them as reliable as a well-built web app. Using Scala and powerful frameworks such as Spark, MLlib, and Akka, they'll learn to quickly and reliably move from a single machine to a massive cluster. Key Features: * Example-rich guide * Step-by-step guide * Move from single-machine to massive cluster Readers should have intermediate skills in Java or Scala. No previous machine learning experience is required. About the Technology: Machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. To make machine learning systems reactive, you need to understand both reactive design patterns and modern data architecture patterns.

This book constitutes the refereed proceedings of the 4th European Conference on Genetic Programming, EuroGP 2001, held at Lake Como, Italy in April 2001.The 17 revised full papers and 13 research posters presented were carefully reviewed and selected during a rigorous double-blind refereeing process out of 42 submissions. All current aspects of genetic programming are addressed, ranging from theoretical and foundational issues to applications in a variety of fields such as robotics, artificial retina, character recognition, financial prediction, digital filter and electronic circuit design, image processing, data fusion, and bio-sequencing.

The practical application of Genetic Algorithms to the solution of engineering problems, is rapidly becoming an established approach in the fields of control and signal processing. This book provides comprehensive coverage of the techniques involved, describing the intrinsic characteristics, advantages and constraints of genetic algorithms, as well as discussing genetic operations such as crossover, mutation and reinsertion. In addition, the principle of multiobjective optimization and computing parallelism are discussed. These features are fully illustrated by real-world applications. Also described is a newly proposed and unique hierarchical genetic algorithm designed to address the problems in determining system topology. For added value, a 3.5" disk accompanies the book, that provides the reader with an interactive Genetic Algorithms demonstration programme.

This volume contains all the papers presented at the Eleventh International C- ference on Algorithmic Learning Theory (ALT 2000) held at Coogee Holiday Inn, Sydney, Australia,11-13 December 2000. The conference was sponsored by the School of Computer Science and Engineering, University of New South Wales, and supported by the IFIP Working Group 1.4 on Computational Learning T- ory and the Computer Science Association (CSA) of Australia. In response to the call for papers 39 submissions were received on all aspects of algorithmic learning theory. Out of these 22 papers were accepted for p- sentation by the program committee. In addition, there were three invited talks by William Cohen (Whizbang Labs), Tom Dietterich (Oregon State Univeristy), and Osamu Watanabe (Tokyo Institute of Technology). This year's conference is the last in the millenium and eleventh overall in the ALT series. The ?rst ALT workshop was held in Tokyo in 1990. It was merged with the workshop on Analogical and Inductive Inference in 1994. The conf- ence focuses on all areas related to algorithmic learning theory, including (but not limited to) the design and analysis of learning algorithms, the theory of machine learning, computational logic of/for machine discovery, inductive inf- ence, learning via queries, new learning models, scienti?c discovery, learning by analogy, arti?cial and biological neural networks, pattern recognition, statistical learning, Bayesian/MDL estimation, inductive logic programming, data m- ing and knowledge discovery, and application of learning to biological sequence analysis. In the current conference there were papers from a variety of the above areas, refelecting both the theoretical as well as practical aspec

This volumecontains the papers presentedatthe SeventhInternationalC- ference on Logicfor Programmingand Automated Reasoning (LPAR 2000)held onReunionIsland, France,6 10November2000, followedbythe ReunionWo- shop on Implementation of Logic. Sixty-?ve papers were submitted to LPAR 2000 of which twenty-six papers were accepted. Submissions by the program committee members were not - lowed. There was a special category of experimental papers intended to describe implementations of systems, to report experiments with implemented systems, orto compareimplementedsystems.Eachof thesubmissionswasreviewedbyat least three program committee members and an electronic program committee meeting was held via the Internet. In addition to the refereed papers, this volume contains full papers by two of the four invited speakers, Georg Gottlob and Micha] el Rusinowitch, along with an extended abstract of Bruno Courcelle s invited lecture and an abstract of Erich Gr] adel s invited lecture. WewouldliketothankthemanypeoplewhohavemadeLPAR2000possible. We are grateful to the following groups and individuals: the program and or- nizing committees; the additional referees; the local arrangements chair Teodor Knapik; PascalManoury, who was in chargeof accommodation; Konstantin - rovin, whomaintainedthe programcommittee Webpage;andBillMcCune, who implemented the program committee management software."

Robot learning is an exciting and interdisciplinary ?eld. This state is re?ected in the range and form of the papers presented here. Techniques that have - come well established in robot learning are present: evolutionary methods, neural networkapproaches, reinforcement learning; as are techniques from control t- ory, logic programming, and Bayesian statistics. It is notalbe that in many of the papers presented in this volume several of these techniques are employed in conjunction. In papers by Nehmzow, Grossmann and Quoy neural networks are utilised to provide landmark-based representations of the environment, but di?erent techniques are used in each paper to make inferences based on these representations. Biology continues to provide inspiration for the robot learning researcher. In their paper Peter Eggenberger et al. borrow ideas about the role of n- romodulators in switching neural circuits, These are combined with standard techniques from arti?cial neural networks and evolutionary computing to p- vide a powerful new algorithm for evolving robot controllers. In the ?nal paper in this volume Bianco and Cassinis combine observations about the navigation behaviour of insects with techniques from control theory to produce their visual landmarklearning system. Hopefully this convergence of engineering and biol- ical approaches will continue. A rigourous understanding of the ways techniques from these very di?erent disciplines can be fused is an important challenge if progress is to continue. Al these papers are also testament to the utility of using robots to study intelligence and adaptive behaviour.

In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers.This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.

This volume contains papers presented at the joint 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computat- nal Learning Theory, held at the Trippenhuis in Amsterdam, The Netherlands from July 16 to 19, 2001. The technical program contained 40 papers selected from 69 submissions. In addition, David Stork (Ricoh California Research Center) was invited to give an invited lecture and make a written contribution to the proceedings. The Mark Fulk Award is presented annually for the best paper co-authored by a student. This year's award was won by Olivier Bousquet for the paper "Tracking a Small Set of Modes by Mixing Past Posteriors" (co-authored with Manfred K. Warmuth). We gratefully thank all of the individuals and organizations responsible for the success of the conference. We are especially grateful to the program c- mittee: Dana Angluin (Yale), Peter Auer (Univ. of Technology, Graz), Nello Christianini (Royal Holloway), Claudio Gentile (Universit'a di Milano), Lisa H- lerstein (Polytechnic Univ.), Jyrki Kivinen (Univ. of Helsinki), Phil Long (- tional Univ. of Singapore), Manfred Opper (Aston Univ.) , John Shawe-Taylor (Royal Holloway), Yoram Singer (Hebrew Univ.), Bob Sloan (Univ. of Illinois at Chicago), Carl Smith (Univ. of Maryland), Alex Smola (Australian National Univ.), and Frank Stephan (Univ. of Heidelberg), for their e?orts in reviewing and selecting the papers in this volume.

This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single "Cheat Sheet". The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.

This volume contains the proceedings of EuroGP 2000, the European Conf- ence on Genetic Programming, held in Edinburgh on the 15th and 16th April 2000. This event was the third in a series which started with the two European workshops: EuroGP'98, held in Paris in April 1998, and EuroGP'99, held in Gothenburg in May 1999. EuroGP 2000 was held in conjunction with EvoWo- shops 2000 (17th April) and ICES 2000 (17th-19th April). Genetic Programming (GP) is a growing branch of Evolutionary Compu- tion in which the structures in the population being evolved are computer p- grams. GP has been applied successfully to a large number of di?cult problems like automatic design, pattern recognition, robotic control, synthesis of neural networks, symbolic regression, music and picture generation, biomedical app- cations, etc. In recent years,even human-competitive results have been achieved by a number of groups. EuroGP 2000, the ?rst evolutionary computation conference of the new m- lennium, was the biggest event devoted to genetic programming to be held in Europe in 2000. It was a high quality conference where state-of-the-art work on the theory of GP and applications of GP to real world problems was presented.

Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.

The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.