Algorithmic Decision Theory [electronic resource] : 6th International Conference, ADT 2019, Durham, NC, USA, October 25-27, 2019, Proceedings / edited by Saša Pekeč, Kristen Brent Venable.
Contributor(s): Pekeč, Saša [editor.] | Venable, Kristen Brent [editor.] | SpringerLink (Online service).
Material type: BookSeries: Lecture Notes in Artificial Intelligence: 11834Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: VIII, 181 p. 578 illus., 18 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030314897.Subject(s): Artificial intelligence | Computer science -- Mathematics | Application software | Artificial Intelligence | Mathematics of Computing | Computer and Information Systems ApplicationsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online In: Springer Nature eBookSummary: This book constitutes the conference proceedings of the 6th International Conference on Algorithmic Decision Theory, ADT 2019, held in Durham, NC, USA, in October 2019. The 10 full papers presented together with 7 short papers were carefully selected from 31 submissions. The papers focus on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of computer science, economics and operations research in order to improve the theory and practice of modern decision support.No physical items for this record
This book constitutes the conference proceedings of the 6th International Conference on Algorithmic Decision Theory, ADT 2019, held in Durham, NC, USA, in October 2019. The 10 full papers presented together with 7 short papers were carefully selected from 31 submissions. The papers focus on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of computer science, economics and operations research in order to improve the theory and practice of modern decision support.
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