David Leake

Zhongzhi Shi

Sunil Vadera

Agnar Aamodt

This volume comprises the 6th IFIP International Conference on Intelligent Infor- tion Processing. As the world proceeds quickly into the Information Age, it encounters both successes and challenges, and it is well recognized nowadays that intelligent information processing provides the key to the Information Age and to mastering many of these challenges. Intelligent information processing supports the most - vanced productive tools that are said to be able to change human life and the world itself. However, the path is never a straight one and every new technology brings with it a spate of new research problems to be tackled by researchers; as a result we are not running out of topics; rather the demand is ever increasing. This conference provides a forum for engineers and scientists in academia and industry to present their latest research findings in all aspects of intelligent information processing. This is the 6th IFIP International Conference on Intelligent Information Processing. We received more than 50 papers, of which 35 papers are included in this program as regular papers and 4 as short papers. We are grateful for the dedicated work of both the authors and the referees, and we hope these proceedings will continue to bear fruit over the years to come. All papers submitted were reviewed by two referees. A conference such as this cannot succeed without help from many individuals who contributed their valuable time and expertise.

6th IFIP TC 12 International Conference, IIP 2010, Manchester, UK, October 13-16, 2010, Proceedings

Intelligent Information Processing V

Roy Turner

Boicho Kokinov

Anind Dey

Context is of crucial importance for research and applications in many disciplines, as evidenced by many workshops, symposia, seminars, and conferences on specific aspects of context. The International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT), the oldest conference series focusing on context, provides a unique interdisciplinary emphasis, bringing together participants from a wide range of disciplines, including artificial intelligence, cognitive science, computer science, linguistics, organizational science, philosophy, psychology, ubiquitous computing, and application areas such as medicine and law, to discuss and report on context-related research and projects. Previous CONTEXT conferences were held in Rio de Janeiro, Brazil (1997), Trento, Italy (1999, LNCS 1688), Dundee, UK (2001, LNCS 2116), and Palo Alto, USA (2003, LNCS 2680). CONTEXT 2005 was held in Paris, France during July 5–8, 2005. There was a strong response to the CONTEXT 2005 Call for Papers, with 120 submissions received. A careful review process assessed all submissions, with each paper first reviewed by the international Program Committee, and then reviewer discussions were initiated as needed to assure that the final decisions carefully considered all aspects of each paper. Reviews of submissions by the Program Chairs were supervised independently and anonymously, to assure fair consideration of all work. Out of the 120 submissions, 23 were selected as full papers for oral presentation, and 20 were selected as full papers for poster presentation. These outstanding papers are presented in this proceedings.

5th International and Interdisciplinary Conference, CONTEXT 2005, Paris, France, July 5-8, 2005, Proceedings

Modeling and Using Context

Zhaohui Wu

Uli Sattler

This book constitutes the refereed proceedings of the 8th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2014, held in Hangzhou, China, in October 2014. The 32 revised papers presented were carefully reviewed and selected from more than 70 submissions. They are organized in topical sections on machine learning, data mining, web mining, multi-agent systems, automatic reasoning, decision algorithms, multimedia, pattern recognition, and information security.

8th IFIP TC 12 International Conference, IIP 2014, Hangzhou, China, October 17-20, 2014, Proceedings

Intelligent Information Processing VII

Ashwin Ram

David B. Leake

Brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book

Goal-driven Learning

David Leake

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