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Intelligence techniques are needed to further enhance the advantage of groups with diversity in problem solving.

, , , , and . HIMA, page 48-55. IEEE, (2009)

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Approximating General Logic Programs., , and . ILPS, page 181-198. MIT Press, (1993)Compressing 3D Measurement Data Under Interval Uncertainty., , , and . PARA, volume 3732 of Lecture Notes in Computer Science, page 142-150. Springer, (2004)How to Predict Nesting Sites and How to Measure Shoreline Erosion: Fuzzy and Probabilistic Techniques for Environment-Related Spatial Data Processing., , , and . WCSC, volume 361 of Studies in Fuzziness and Soft Computing, page 595-604. Springer, (2016)How to Explain Ubiquity of Constant Elasticity of Substitution (CES) Production and Utility Functions Without Explicitly Postulating CES., , and . Robustness in Econometrics, volume 692 of Studies in Computational Intelligence, (2017)Why neural networks in the first place: a theoretical explanation., , , and . J. Intell. Fuzzy Syst., 43 (6): 6947-6951 (2022)Why Geological Regions?, , and . Decision Making Under Uncertainty and Constraints, 217, Springer, (2023)Why Grade Distribution Is Often Multi-modal: An Uncertainty-Based Explanation., , and . IFSA/NAFIPS, volume 1000 of Advances in Intelligent Systems and Computing, page 106-112. Springer, (2019)Data Processing under Fuzzy Uncertainty: Towards More Efficient Algorithms., , and . FUZZ-IEEE, page 1-7. IEEE, (2022)Let Us Use Negative Examples in Regression-Type Problems Too., , , , and . IV, page 296-300. IEEE, (2020)Why Squashing Functions in Multi-Layer Neural Networks., , , , , , and . SMC, page 1705-1711. IEEE, (2020)