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Readings in Agents
This book collects the most significant literature on agents in an attempt top forge a broad foundation for the field. Includes papers from the perspectives of AI, databases, distributed computing, and programming languages. The book will be of interest to programmers and developers, especially in Internet areas.
Knowledge Graphs
A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence. The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.
Planning and Learning by Analogical Reasoning
This research monograph describes the integration of analogical and case-based reasoning into general problem solving and planning as a method of speedup learning. The method, based on derivational analogy, has been fully implemented in PRODIGY/ANALOGY and proven in practice to be amenable to scaling up, both in terms of domain and problem complexity. In this work, the strategy-level learning process is cast for the first time as the automation of the complete cycle of construction, storing, retrieving, and flexibly reusing problem solving experience. The algorithms involved are presented in detail and numerous examples are given. Thus the book addresses researchers as well as practitioners.
Advanced Information Systems Engineering
CAiSE 2000 was the 12th in the series of International Conferences on Advanced Information Systems Engineering. In the year 2000 the conference returned to Stockholm where it was organized the very first time. Since that year, 1989, the CAiSE conferences have developed into an important forum for the presentation and exchange of research results and practical experiences within the field of Information Systems Engineering. The objective of the CAiSE conference series is to bring together researchers and practitioners in the field of information systems engineering to meet annually in order to discuss evolving research issues and applications in this field. The CAiSE conference series also ai...
Conceptual Modeling - ER 2002
For more than 20 years, the series of Conceptual Modeling – ER conferences has provided a forum for research communities and practitioners to present and - change research results and practical experiences in the ?elds of database design and conceptual modeling. Throughout the years, the scope of these conferences has extended from database design and speci?c topics of that area to more u- versal or re?ned conceptual modeling, organizing originally weak or ill-structured information or knowledge in more cultured ways by applying various kinds of principles, abstract models, and theories, for di?erent purposes. At the same time, many technically oriented approaches have been developed which...
Cooperative Information Agents VI
These are the proceedings of the Sixth International Workshop on Cooperative Information Agents (CIA 2002), held at the Universidad de Rey Juan Carlos in Madrid, Spain, September 18–20, 2002. It was colocated with the Third Int- national Workshop on Engineering Societies in the Agents World (ESAW 2002). Since 1997 the annual CIA workshop series has aimed to provide an open forum for all parties interested in the research and development of intelligent infor- tion agents for the Internet and Web. Each event in this renowned series attempts to capture the intrinsic interdisciplinary nature of this research area by calling for contributions from di?erent research communities, and by promoting...
Change of Representation and Inductive Bias
Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses. The effectiveness of learning algorithms is very sensitive to this choice of language; choosing too large a language permits too many possible hypotheses for a program to consider, precluding effective learning, but choosing too small a language can prohibit a program from being able to find acceptable hypotheses. This dependence is not just a pitfall, however; it is also an opportunity. The work of Saul Amarel over the past two decades ha...
