On-line Estimators for Ad-hoc Task Execution: Learning Types and Parameters of Teammates for Effective Teamwork

Matheus Ap. do Carmo Alves (Lancaster University), Elnaz Shafipour Yourdshahi (University of Surrey), Amokh Varma (Indian Institute of Technology), Leandro Soriano Marcolino (Lancaster University), Jó Ueyama (University of São Paulo), Plamen Angelov (Lancaster University)

Abstract

In this paper, we present On-line Estimators for Ad-hoc Task Execution (OEATE), a novel algorithm for teammates' type and parameter estimation in decentralised task execution. We show theoretically that our algorithm can converge to perfect estimations, under some assumptions, as the number of tasks increases. Empirically, we show better performance against our baselines while estimating type and parameters in several different settings. This is an extended abstract of our JAAMAS paper available online [9].