Eckerson, W. Business Intelligence Journal 11 1 , 43—48 Google Scholar.
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Fenton, N. Frydman, H. Garcia-Cascales, S. Guy, H. Quick response improves returns of business intelligence investments, Information Systems Management 22 3 , 66—74 Google Scholar. Goria, S. Greco, S. European Journal of Operational Research. Griffith, S.
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Guyton W. March, Google Scholar. Clyde, W. Claudia Imhoff. Jakubchak, L. Greenough, J. Jo, H. Jourdan, Z. King, J. Li, H.
Ma, L. Massa, S. Nasri, W. Negash, S. Is written by experts in the field. This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Show all links.
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- Multi-Criteria Decision Aid and Artificial Intelligence for Competitive Intelligence.
- Multicriteria Decision Aid and Artificial Intelligence: Links, Theory and Applications.
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- Multi-Criteria Decision Aid and Artificial Intelligence for Competitive Intelligence | SpringerLink.
Allow this favorite library to be seen by others Keep this favorite library private. Find a copy in the library Finding libraries that hold this item Reviews Editorial reviews. Publisher Synopsis To conclude this review, I recommend that departmental libraries buy this book for its presentation of state-of-the-art methodologies in multicriteria decision analysis as it relates to AI. User-contributed reviews Add a review and share your thoughts with other readers.
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Michael Doumpos ; Evangelos Grigoroudis. Multiple criteria decision making. User lists Similar Items. Print version: Doumpos, Michael. Part II Intelligent Technologies for Decision Support and Preference ModelingChapter 3 Designing distributed multi-criteria decision support systems for complex and uncertain situations; 3. NO YES. Selected type: Hardcover. Added to Your Shopping Cart. Multicriteria Decision Aid and Artificial Intelligence : Covers all of the recent advances in intelligent decision making. Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems.
Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments. Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications.