This book constitutes revised papers of the Third International Workshop on approaches and Applications of Inductive Programming, AAIP 2009, held in Edinburgh, UK, in September 2009. The 7 full papers included in this volume were carefully reviewed and selected. The book also contains two invited papers.

This book constitutes the refereed proceedings of the Second Hellenic Conference on Artificial Intelligence, SETN 2002, held in Thessaloniki, Greece, in April 2002. The 42 revised full papers presented together with two invited contributions were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on knowledge representation and reasoning, logic programming and constraint satisfaction, planning and scheduling, natural language processing, human-computer interaction, machine learning, intelligent Internet and multiagent systems, and intelligent applications.

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

What Is Genetic Programming In the field of artificial intelligence, genetic programming, abbreviated GP, is a method for making a population of unsuited programs evolve into programs that are more suited to a certain task. This is accomplished by applying operations that are equivalent to natural genetic processes to the population of programs. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Genetic programming Chapter 2: Bio-inspired computing Chapter 3: CMA-ES Chapter 4: Fitness approximation Chapter 5: Gene expression programming Chapter 6: Genetic representation Chapter 7: Grammatical evolution Chapter 8: Inductive programming Chapter 9: Linear genetic programming Chapter 10: Multi expression programming (II) Answering the public top questions about genetic programming. (III) Real world examples for the usage of genetic programming in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of genetic programming' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of genetic programming.

KI 2008 was the 31st Annual German Conference on Arti?cial Intelligence held September 23–26 at the University of Kaiserslautern and the German Research Center for Arti?cial Intelligence DFKI GmbH in Kaiserslautern, Germany. The conference series started in 1975 with the German Workshop on AI (GWAI), which took place in Bonn, and represents the ?rst forum of its type for the German AI Community. Over the years AI has become a major ?eld in c- puter scienceinGermanyinvolvinga numberof successfulprojects thatreceived much international attention. Today KI conferences are international forums where participants from academia and industry from all over the world meet to exchange their recent research results and to discuss trends in the ?eld. Since 1993 the meeting has been called the “Annual German Conference on Arti?cial Intelligence,” designated by the German acronym KI. This volume contains the papers selected out of 77 submissions, including a number of submissions from outside German-speaking countries. In total, 15 submissions (19%) were accepted for oral and 30 (39%) for poster presentation. Oralpresentationsattheconferenceweresingletrack. Becauseofthis,thechoice of presentation form (oral, poster) was based on how well reviews indicated that the paper would ?t into one or the other format. The proceedings allocate the same space to both types of papers. In addition, we selected six papers that show high application potential - scribing systems or prototypical implementations of innovative AI technologies. They are also included in this volume as two-page extended abstracts.

"This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality while offering readers suggestions by proposing future work in this emerging research field"--Provided by publisher.

For contemporary societies, data mining has emerged as a serious challenge. Thanks to more advanced analytical tools, the Big Data explosion has enabled businesses to assess their performance more thoroughly and accurately. For example, transitioning from using a basic spreadsheet to using data lake modeling offers more flexibility in terms of consulting and summarizing vast amounts of data from many business angles. Data mining, which is the foundation for this optimization of data analysis, has been strengthened by artificial intelligence and machine learning to find patterns in this deluge of data and build future prediction models, turning it into a critical tool for decision-making. This book provides an understanding of the most modern techniques and uses for data mining. It examines data mining in order to classify datasets, predict outcomes, and optimize analyses. Furthermore, the book demonstrates these technological developments by highlighting relevant applications of data mining in industry, biology, education, medicine, and health.

Across a variety of disciplines, data and statistics form the backbone of knowledge. To ensure the reliability and validity of data, appropriate measures must be taken in conducting studies and reporting findings. Research Methods: Concepts, Methodologies, Tools, and Applications compiles chapters on key considerations in the management, development, and distribution of data. With its focus on both fundamental concepts and advanced topics, this multi-volume reference work will be a valuable addition to researchers, scholars, and students of science, mathematics, and engineering.

This unique volume is the first publication on software engineering and computational intelligence (CI) viewed as a synergistic interplay of neurocomputing, granular computation (including fuzzy sets and rough sets), and evolutionary methods. It presents a unified view of CI in the context of software engineering.The book addresses a number of crucial issues: what is CI, what role does it play in software development, how are CI elements built into successive phases of the software life cycle, and what is the role played by CI in quantifying fundamental features of software artifacts?With contributions from leading researchers and practitioners, the book provides the reader with a wealth of new concepts and approaches, complete algorithms, in-depth case studies, and thought-provoking exercises. The topics coverage include neurocomputing, granular as well as evolutionary computing, object-oriented analysis and design in software engineering. There is also an extensive bibliography.