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Algorithmic Learning Theory
This book constitutes the refereed proceedings of the 7th International Workshop on Algorithmic Learning Theory, ALT '96, held in Sydney, Australia, in October 1996. The 16 revised full papers presented were selected from 41 submissions; also included are eight short papers as well as four full length invited contributions by Ross Quinlan, Takeshi Shinohara, Leslie Valiant, and Paul Vitanyi, and an introduction by the volume editors. The book covers all areas related to algorithmic learning theory, ranging from theoretical foundations of machine learning to applications in several areas.
Machine Learning
One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Ea...
Creative and Collaborative Learning through Immersion
This book includes instructional design and practice of how immersive technology is integrated in discipline-based and interdisciplinary curriculum design. It focuses on pedagogical models and learning outcomes of immersive learning experiences and demonstrates how immersive learning can be applied in industries. This book brings scholars, researchers and educators together around an international and interdisciplinary consolidation and reflection on learning through immersion. The originality lies in how advanced technology and contemporary pedagogical models can integrate to enhance student engagement and learning effectiveness in higher education.
Imitation in Animals and Artifacts
An interdisciplinary overview of current research on imitation in animals and artifacts.
PRICAI 2000 Topics in Artificial Intelligence
PRICAI 2000, held in Melbourne, Australia, is the sixth Pacific Rim Interna tional Conference on Artificial Intelligence and is the successor to the five earlier PRICAIs held in Nagoya (Japan), Seoul (Korea), Beijing (China), Cairns (Aus tralia) and Singapore in the years 1990, 1992, 1994, 1996 and 1998 respectively. PRICAI is the leading conference in the Pacific Rim region for the presenta tion of research in Artificial Intelligence, including its applications to problems of social and economic importance. The objectives of PRICAI are: To provide a forum for the introduction and discussion of new research results, concepts and technologies; To provide practising engineers with exposure to ...
Encyclopedia of the Sciences of Learning
Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic ca...
Knowledge Discovery in Databases: PKDD 2003
The proceedings of ECML/PKDD2003 are published in two volumes: the P- ceedings of the 14th European Conference on Machine Learning (LNAI 2837) and the Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (LNAI 2838). The two conferences were held on September 22–26, 2003 in Cavtat, a small tourist town in the vicinity of Dubrovnik, Croatia. As machine learning and knowledge discovery are two highly related ?elds, theco-locationofbothconferencesisbene?cialforbothresearchcommunities.In Cavtat, ECML and PKDD were co-located for the third time in a row, following the successful co-location of the two European conferences in Freiburg (2001) a...
Recent Advances in Reinforcement Learning
Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Intelligent Systems
Published in honour of the 70th birthday of Yoh-Han Pao, George S. Dively Dis tinguished Professor of Engineering at Case Western Reserve University, Cleveland, Ohio, this festschrift embraces a remarkably diverse set of topics. Drawing from the fields of pattern recognition, engineering, artificial intelligence and artificial neural systems, it is a fitting testament to the extraordinary breadth of his professional in terests both in foundational research into the new technology of Intelligent Systems and ill the application of that evolving technology to the solution of hard engineering problems. In common with many scientists who build their reputations in one field before devoting their ...
Machines We Trust
Experts from disciplines that range from computer science to philosophy consider the challenges of building AI systems that humans can trust. Artificial intelligence-based algorithms now marshal an astonishing range of our daily activities, from driving a car ("turn left in 400 yards") to making a purchase ("products recommended for you"). How can we design AI technologies that humans can trust, especially in such areas of application as law enforcement and the recruitment and hiring process? In this volume, experts from a range of disciplines discuss the ethical and social implications of the proliferation of AI systems, considering bias, transparency, and other issues. The contributors, of...
