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Making Friends on the Fly: Advances in Ad Hoc Teamwork [electronic resource] / by Samuel Barrett.

By: Contributor(s): Series: Studies in Computational Intelligence ; 603Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: XX, 144 p. 26 illus., 19 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319180694
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q342
Online resources:
Contents:
Introduction -- Problem Description -- Background -- Related Work -- The PLASTIC Algorithms -- Theoretical Analysis of PLASTIC -- Empirical Evaluation -- Discussion and Conclusion.
In: Springer eBooksSummary: This book is devoted to the encounter and interaction of agents such as robots with other agents and describes how they cooperate with their previously unknown teammates, forming an Ad Hoc team. It presents a new algorithm, PLASTIC, that allows agents to quickly adapt to new teammates by reusing knowledge learned from previous teammates.  PLASTIC is instantiated in both a model-based approach, PLASTIC-Model, and a policy-based approach, PLASTIC-Policy.  In addition to reusing knowledge learned from previous teammates, PLASTIC also allows users to provide expert-knowledge and can use transfer learning (such as the new TwoStageTransfer algorithm) to quickly create models of new teammates when it has some information about its new teammates. The effectiveness of the algorithm is demonstrated on three domains, ranging from multi-armed bandits to simulated robot soccer games.
Item type: eBooks
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Introduction -- Problem Description -- Background -- Related Work -- The PLASTIC Algorithms -- Theoretical Analysis of PLASTIC -- Empirical Evaluation -- Discussion and Conclusion.

This book is devoted to the encounter and interaction of agents such as robots with other agents and describes how they cooperate with their previously unknown teammates, forming an Ad Hoc team. It presents a new algorithm, PLASTIC, that allows agents to quickly adapt to new teammates by reusing knowledge learned from previous teammates.  PLASTIC is instantiated in both a model-based approach, PLASTIC-Model, and a policy-based approach, PLASTIC-Policy.  In addition to reusing knowledge learned from previous teammates, PLASTIC also allows users to provide expert-knowledge and can use transfer learning (such as the new TwoStageTransfer algorithm) to quickly create models of new teammates when it has some information about its new teammates. The effectiveness of the algorithm is demonstrated on three domains, ranging from multi-armed bandits to simulated robot soccer games.

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