On-line estimators for ad-hoc task execution: learning types and parameters of teammates for effective teamwork

HIGHLIGHTS

• who: Elnaz Shafipour Yourdshahi from the (UNIVERSITY) have published the paper: On-line estimators for ad-hoc task execution: learning types and parameters of teammates for effective teamwork, in the Journal: (JOURNAL)
• what: The authors show theoretically that the algorithm can converge to perfect estimations under some assumptions as the number of tasks increases. In fact the authors evaluate a variety of scenarios via the increasing number of agents scenario sizes number of items and number of types showing that the authors can overcome previous works in most cases considering the estimation process besides robustness . . .
  

   

  

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