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Titlebook: Research and Development in Intelligent Systems XXIII; Proceedings of AI-20 Max Bramer,Frans Coenen,Andrew Tuson Conference proceedings 200

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書目名稱Research and Development in Intelligent Systems XXIII
副標題Proceedings of AI-20
編輯Max Bramer,Frans Coenen,Andrew Tuson
視頻videohttp://file.papertrans.cn/828/827877/827877.mp4
圖書封面Titlebook: Research and Development in Intelligent Systems XXIII; Proceedings of AI-20 Max Bramer,Frans Coenen,Andrew Tuson Conference proceedings 200
描述M.A.BRAMER University ofPortsmouth, UK This volume comprises the refereed technical papers presented at AI-2006, the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2006. The conference was organised by SGAI, the British Computer Society Specialist Group on Artificial Intelligence. The papers in this volume present new and innovative developments in the field, divided into sections on AI Techniques, Knowledge Discovery in Data, Argumentation, Dialogue Games and Optimisation, Knowledge Representation and Management, Semantic Web, and Model Based Systems and Simulation. For the first time the volume also includes the text of short papers presented as posters at the conference. This year‘s prize for the best refereed technical paper was won by a paper entitled Combining Task Execution with Background Knowledge for the Verification of Medical Guidelines‘ written by a team comprising Arjen Hommersom, Perry Groot and Peter Lucas (University of Nijmegen, The Netherlands) and Michael Balser and Jonathan Schmitt (University ofAugsburg, Germany). SGAI gratefully acknowledges the long-term sponsorsh
出版日期Conference proceedings 2007
關(guān)鍵詞Argumentation; Bayesian network; Emotion; Mutation; Simulation; algorithms; artificial intelligence; bioinf
版次1
doihttps://doi.org/10.1007/978-1-84628-663-6
isbn_softcover978-1-84628-662-9
isbn_ebook978-1-84628-663-6
copyrightSpringer-Verlag London 2007
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978-1-84628-662-9Springer-Verlag London 2007
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Improving the Gradient Based Search Direction to Enhance Training Efficiency of Back Propagation Bas training process. The proposed algorithm is generic and can be implemented in almost all gradient based optimisation processes. The robustness of the proposed algorithm is shown by comparing convergence rates for gradient descent, conjugate gradient and quasi- Newton methods on many benchmark examples.
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Incremental Inductive Learning in a Constructivist Agent learning mechanism, designed to incrementally discover deterministic environmental regularities, even in non-deterministic worlds. Finally, we report some experimental results and discuss how this agent architecture can lead to the construction of more abstract concepts.
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national Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2006. The conference was organised by SGAI, the British Computer Society Specialist Group on Artificial Intelligence. The papers in this volume present new and innovative developme
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