Modeling decisions for artificial intelligence

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The content includes a variety of invited talks and regular papers that explore advanced topics in decision-making, computational models, and fuzzy set theories. Key discussions focus on asymmetric and compound preference aggregators, computational models of language, and the dominance-based rough set approach to case-based reasoning. The text covers preference modeling using rectangular bilattices and strategies for managing ignorance in multiperson decision-making scenarios. It also presents an agent negotiation engine for collaborative decision-making and methods for learning causal Bayesian networks. Additional highlights include the pairwise comparison model, simultaneous decision networks for strategic planning, and multicriteria fuzzy decision systems for sorting contaminated soils. The use of fuzzy set theories in software cost estimation and assessing country-of-origin effects on product attitudes is examined. Various methodologies such as non-monotonic fuzzy measures, decision aggregation in financial planning, and Bayesian correction for SNP ascertainment bias are also discussed. The annotation addresses feature selection in support vector machines, improvements in fuzzy rule-based decision models, and innovative approaches to the re-identification problem using neural networks. It concludes with insights into fuzzy clustering techniques and their applications in data classification and medical imaging.