Enrique Miranda – författare

This three-volume set CCIS 3019-3021 constitutes the proceedings of the 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026, held in Rome, Italy, during June 15-19, 2026.The 111 full papers included in these volumes were carefully reviewed and selected from 254 submissions. They are organized into topical sections as follows:Part I: Aggregation theory; Imprecise probabilities; Knowledge representation and modelling; Statistical inference and data analysis.Part II: Robustness in economics and finance, Foundations of fuzzy sets;  Fuzzy and multivalued logic; Fuzzy and multivalued logic.Part III: Preference modelling; Cooperative automated systems; Fuzzy control: methods and applications; Fuzzy implication functions; Data mining.

This three-volume set CCIS 3019-3021 constitutes the proceedings of the 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026, held in Rome, Italy, during June 15-19, 2026.The 111 full papers included in these volumes were carefully reviewed and selected from 254 submissions. They are organized into topical sections as follows:Part I: Aggregation theory; Imprecise probabilities; Knowledge representation and modelling; Statistical inference and data analysis.Part II: Robustness in economics and finance, Foundations of fuzzy sets;  Fuzzy and multivalued logic; Fuzzy and multivalued logic.Part III: Preference modelling; Cooperative automated systems; Fuzzy control: methods and applications; Fuzzy implication functions; Data mining.

This three-volume set CCIS 3019-3021 constitutes the proceedings of the 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026, held in Rome, Italy, during June 15-19, 2026.The 111 full papers included in these volumes were carefully reviewed and selected from 254 submissions. They are organized into topical sections as follows:Part I: Aggregation theory; Imprecise probabilities; Knowledge representation and modelling; Statistical inference and data analysis.Part II: Robustness in economics and finance, Foundations of fuzzy sets;  Fuzzy and multivalued logic; Explainable AI and decision making.Part III: Preference modelling; Cooperative automated systems; Fuzzy control: methods and applications; Fuzzy implication functions; Data mining.

This three-volume set CCIS 3019-3021 constitutes the proceedings of the 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026, held in Rome, Italy, during June 15-19, 2026.The 111 full papers included in these volumes were carefully reviewed and selected from 254 submissions. They are organized into topical sections as follows:Part I: Aggregation theory; Imprecise probabilities; Knowledge representation and modelling; Statistical inference and data analysis.Part II: Robustness in economics and finance, Foundations of fuzzy sets;  Fuzzy and multivalued logic; Explainable AI and decision making.Part III: Preference modelling; Cooperative automated systems; Fuzzy control: methods and applications; Fuzzy implication functions; Data mining.

This three-volume set CCIS 3019-3021 constitutes the proceedings of the 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026, held in Rome, Italy, during June 15-19, 2026.The 111 full papers included in these volumes were carefully reviewed and selected from 254 submissions. They are organized into topical sections as follows:Part I: Aggregation theory; Imprecise probabilities; Knowledge representation and modelling; Statistical inference and data analysis.Part II: Robustness in economics and finance, Foundations of fuzzy sets;  Fuzzy and multivalued logic; Explainable AI and decision making.Part III: Preference modelling; Cooperative automated systems; Fuzzy control: methods and applications; Fuzzy implication functions; and Data mining.

This three-volume set CCIS 3019-3021 constitutes the proceedings of the 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2026, held in Rome, Italy, during June 15-19, 2026.The 111 full papers included in these volumes were carefully reviewed and selected from 254 submissions. They are organized into topical sections as follows:Part I: Aggregation theory; Imprecise probabilities; Knowledge representation and modelling; Statistical inference and data analysis.Part II: Robustness in economics and finance, Foundations of fuzzy sets;  Fuzzy and multivalued logic; Explainable AI and decision making.Part III: Preference modelling; Cooperative automated systems; Fuzzy control: methods and applications; Fuzzy implication functions; and Data mining.

The idea of soft computing emerged in the early 1990s from the fuzzy systems c- munity, and refers to an understanding that the uncertainty, imprecision and ig- rance present in a problem should be explicitly represented and possibly even - ploited rather than either eliminated or ignored in computations. For instance, Zadeh de?ned ‘Soft Computing’ as follows: Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind. Recently soft computing has, to some extent, become synonymous with a hybrid approach combining AI techniques including fuzzy systems, neural networks, and biologically inspired methods such as genetic algorithms. Here, however, we adopt a more straightforward de?nition consistent with the original concept. Hence, soft methods are understood as those uncertainty formalisms not part of mainstream s- tistics and probability theory which have typically been developed within the AI and decisionanalysiscommunity.Thesearemathematicallysounduncertaintymodelling methodologies which are complementary to conventional statistics and probability theory.

The idea of soft computing emerged in the early 1990s from the fuzzy systems c- munity, and refers to an understanding that the uncertainty, imprecision and ig- rance present in a problem should be explicitly represented and possibly even - ploited rather than either eliminated or ignored in computations. For instance, Zadeh de?ned ‘Soft Computing’ as follows: Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind. Recently soft computing has, to some extent, become synonymous with a hybrid approach combining AI techniques including fuzzy systems, neural networks, and biologically inspired methods such as genetic algorithms. Here, however, we adopt a more straightforward de?nition consistent with the original concept. Hence, soft methods are understood as those uncertainty formalisms not part of mainstream s- tistics and probability theory which have typically been developed within the AI and decisionanalysiscommunity.Thesearemathematicallysounduncertaintymodelling methodologies which are complementary to conventional statistics and probability theory.