Modeling decisions for artificial intelligence

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The content includes invited talks and regular papers focusing on various aspects of fuzzy systems and machine learning. Topics cover RoboCup research in Japan, fuzzy integrals, and qualitative models for web quality evaluation. Other studies explore statistical and fuzzy neural network approaches for stock price forecasting, integration of wavelet time shift properties with support vector machines, and the weighting of training patterns in fuzzy rule-based classification systems. Innovative methods such as a quantum computing-based machine learning approach for radar emitter signal recognition and a multiagent system for modeling lower urinary tract control are discussed. The annotation also highlights mining diagnostic taxonomies from clinical databases and combining classifiers using Dempster’s rule for text categorization. Further, it examines assessment errors in LSP criteria, reliability of these criteria, and unsupervised aggregation via the Choquet integral for student evaluations. The preservation of fuzzy measure classes, decision modeling with the Choquet integral, and consensus measurements in multi-granular linguistic group decision-making are also addressed. The exploration of fuzzy granules in symbolic data analysis, learning procedures for estimating missing values in fuzzy preference relations, and decision-making in dynamic systems based on aggregated fuzzy preferences are included. Additionally, a comp