1.

Record Nr.

UNINA9910640379403321

Titolo

Decision Making Under Uncertainty and Constraints : A Why-Book / / edited by Martine Ceberio, Vladik Kreinovich

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023

ISBN

3-031-16415-6

Edizione

[1st ed. 2023.]

Descrizione fisica

1 online resource (x, 304 pages) : illustrations (some color)

Collana

Studies in Systems, Decision and Control, , 2198-4190 ; ; 217

Disciplina

327.120971

658.4033

Soggetti

Automatic control

Artificial intelligence

Control and Systems Theory

Artificial Intelligence

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Includes index.

Nota di contenuto

Baudelaire's Ideas of Vagueness and Uniqueness in Art: Algorithm-Based Explanations -- Selfish Gene Theory Explains Oedipus Complex -- How to Teach Advanced Highly Motivated Students: Teaching Strategy of Iosif Yakovlevich Verebeichik -- Why 70/100 Is Satisfactory? Why Five Letter Grades? Why Other Academic Conventions? -- Shall We Ignore All Intermediate Grades? -- Why ∞ is a Reasonable Symbol for Infinity -- What is 1/0 from the Practical Viewpoint: A Pedagogical Note -- Historical Diversity Through base-10 Representation of Mayan Maths.

Sommario/riassunto

This book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation. Example of applications described in the book ranges from science (biosciences, geosciences, and physics) to electrical and civil



engineering, education, psychology and decision making, and religion—and, of course, include computer science, AI (in particular, eXplainable AI), and machine learning. The book can be recommended to researchers and students in these application areas. Many of the examples use general techniques that can be used in other application areas as well, so it is also useful for practitioners and researchers in other areas who are looking for possible theoretical explanations of empirical formulas and techniques.