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Investigating Explanation-Based Learning
Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars. The learned concept is generalized directly from this explanation. The first EBL systems of the modern era were Mitchell's LEX2, Silver's LP, and De Jong's KIDNAP natural language system. Two of these systems, Mitchell's and De Jong's, have led to extensive follow-up research in EBL. This book outlines the significant steps in EBL research of the Illinois group under De Jong. This volume describes theoretical research and computer systems that use a broad range of formalisms: schemas, production systems, qualitative reasoning models, non-monotonic logic, situation calculus, and some home-grown ad hoc representations. This has been done consciously to avoid sacrificing the ultimate research significance in favor of the expediency of any particular formalism. The ultimate goal, of course, is to adopt (or devise) the right formalism.
Cameron's Thesis
Both the visualisation and management of large-scale computer hardware is difficult due to its distributed nature. This thesis develops a framework to support both these goals on the SpiNNaker neural network architecture - which can scale to more than a million processors. The solution provides visualisation and management to the SpiNNaker machine, traversing the hardware and software divide to provide a unified solution for the real-time monitoring of artificial neural networks, and the SpiNNaker hardware on which it runs. This book is the story of its development
Advances in Natural Language Processing
This book constitutes the refereed proceedings of the Third International Conference PorTAL 2002 - Portugal for Natural Language Processing, held in Faro, Portugal, in June 2002. The 23 reviewed regular papers and 11 short papers presented were carefully reviewed and selected from 48 submissions. The papers are organized in topical sections on pragmatics, discourse, semantics, and the lexicon; interpreting and generating spoken and written language; language-oriented information retrieval, question answering, summarization, and information extraction; language-oriented machine learning; multi-lingual processing, machine translation, and translation aids; natural language interfaces and dialogue systems; tools and resources; and evaluation of systems.
Artificial General Intelligence, 2008
Includes full-length papers, short position statements and also the papers presented in the post conference workshop on the sociocultural, ethical and futurological implications of Artificial General Intelligence (AGI).
Learning Theory and Kernel Machines
This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
Intelligent Problem Solving. Methodologies and Approaches
The focus of the papers presented in these proceedings is on employing various methodologies and approaches for solving real-life problems. Although the mechanisms that the human brain employs to solve problems are not yet completely known, we do have good insight into the functional processing performed by the human mind. On the basis of the understanding of these natural processes, scientists in the field of applied intelligence have developed multiple types of artificial processes, and have employed them successfully in solving real-life problems. The types of approaches used to solve problems are dependant on both the nature of the problem and the expected outcome. While knowledge-based ...
Computational Legal Studies
Featuring contributions from a diverse set of experts, this thought-provoking book offers a visionary introduction to the computational turn in law and the resulting emergence of the computational legal studies field. It explores how computational data creation, collection, and analysis techniques are transforming the way in which we comprehend and study the law, and the implications that this has for the future of legal studies.
ECAI 2020
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of ...
Machine Learning
Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learn...
