Advances In Artificial Intelligence Pricai 2000 Workshop Reader

This book constitutes the thoroughly refereed joint post-proceedings of four workshops held during the Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000, held in Melbourne, Australia, in August/September 2000. The 32 revised full papers presented were carefully selected during two rounds of reviewing and revision. In accordance with the four workshops represented, the book is organized in topical sections on applications of artificial intelligence in industry, artificial intelligence in electronic commerce, intelligent information agents, and teamwork and adjustable autonomy in agents.

Most of the modern machine learning models, based on deep learning techniques, depend on carefully curated and cleanly labelled training sets to be reliably trained and deployed. However, the expensive labelling process involved in the acquisition of such training sets limits the number and size of datasets available to build new models, slowing down progress in the field. Alternatively, many poorly curated training sets containing noisy labels are readily available to be used to build new models. However, the successful exploration of such noisy-label training sets depends on the development of algorithms and models that are robust to these noisy labels. Machine learning and Noisy Labels: Definitions, Theory, Techniques and Solutions defines different types of label noise, introduces the theory behind the problem, presents the main techniques that enable the effective use of noisy-label training sets, and explains the most accurate methods developed in the field. This book is an ideal introduction to machine learning with noisy labels suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching into, machine learning methods. Shows how to design and reproduce regression, classification and segmentation models using large-scale noisy-label training sets Gives an understanding of the theory of, and motivation for, noisy-label learning Shows how to classify noisy-label learning methods into a set of core techniques

This book constitutes the thoroughly reviewed joint postprocessings of two international workshops on Coordination, Organization, Institutions and Norms in Agent Systems, COIN@AAMAS 2010, held in Toronto, Canada in May 2010 and COIN@MALLOW 2010, held in Lyon, France in August 2010. The 20 revised full papers presented went through several rounds of reviewing and revision and were carefully selected for presentations. The papers are organized in topical sections on normative systems design and modeling; social aspects; and norms at runtime: learning and enforcing.

Proceedings of the 5th International Conference on Intelligent Human Systems Integration (IHSI 2022): Integrating People and Intelligent Systems, February 22–24, 2022, Venice, Italy

"This book highlights new business products, services, strategies, and philosophies drawn from an emerging collaborative information platform and explores connections between the development of new e-business technologies with consumers, businesses, and links to social and political visions and strategies"--Provided by publisher.