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Record Nr. |
UNINA9910261132703321 |
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Autore |
Steiner Silvan |
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Titolo |
Understanding the Successful Coordination of Team Behavior |
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Pubbl/distr/stampa |
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Descrizione fisica |
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1 online resource (136 p.) |
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Collana |
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Frontiers Research Topics |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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In many areas of human life, people perform in teams. These teams' performances depend, at least partly, on team members' abilities to coordinate their contributions effectively. This includes the making of decisions and the regulation of behavior in reference to the framework provided by the social group- and task-context. Given the high relevance of a deepened and integrated understanding about the mechanisms underlying coordinated team behavior, the aim of this research topic is to provide a platform for different theoretical and methodological approaches to researching and understanding coordinated team behavior in different task contexts. The articles published in this edition offer a multifaceted insight into current work on the topic. |
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2. |
Record Nr. |
UNISALENTO991004402927907536 |
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Autore |
Lattimore, Tor |
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Titolo |
Bandit algorithms / Tor Lattimore, Csaba Szepesvári |
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Pubbl/distr/stampa |
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Cambridge ; New York, NY : Cambridge University Press, 2020 |
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ISBN |
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Descrizione fisica |
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xviii, 518 p. : ill. ; 26 cm |
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Classificazione |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Resource allocation - Mathematical models |
Decision making - Mathematical models |
Algorithms |
Probabilities |
Mathematical optimization |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references (p. [484]-511) and index |
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Sommario/riassunto |
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Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks |
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