Mechanism Selection for Multi-Robot Task Allocation

The work presented here investigates how environmental features can be used to help select a task allocation mechanism from a portfolio in a multi-robot exploration scenario. In particular, we look at clusters of task locations and the positions of team members in relation to cluster centres. In a d...

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Published inLecture notes in computer science pp. 421 - 435
Main Authors Schneider, Eric, Sklar, Elizabeth I., Parsons, Simon
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 01.01.2017
SeriesLecture Notes in Computer Science
Subjects
Auction mechanism
Multi-robot team
Task allocation
Online AccessGet full text
ISBN3319641069
9783319641065
ISSN0302-9743
1611-3349
1611-3349
DOI10.1007/978-3-319-64107-2_33

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Abstract The work presented here investigates how environmental features can be used to help select a task allocation mechanism from a portfolio in a multi-robot exploration scenario. In particular, we look at clusters of task locations and the positions of team members in relation to cluster centres. In a data-driven approach, we conduct experiments that use two different task allocation mechanisms on the same set of scenarios, providing comparative performance data. We then train a classifier on this data, giving us a method for choosing the best mechanism for a given scenario. We show that selecting a mechanism via this method, compared to using a single state-of-the-art mechanism only, can improve team performance in certain environments, according to our metrics.
AbstractList The work presented here investigates how environmental features can be used to help select a task allocation mechanism from a portfolio in a multi-robot exploration scenario. In particular, we look at clusters of task locations and the positions of team members in relation to cluster centres. In a data-driven approach, we conduct experiments that use two different task allocation mechanisms on the same set of scenarios, providing comparative performance data. We then train a classifier on this data, giving us a method for choosing the best mechanism for a given scenario. We show that selecting a mechanism via this method, compared to using a single state-of-the-art mechanism only, can improve team performance in certain environments, according to our metrics.
Author Schneider, Eric
Sklar, Elizabeth I.
Parsons, Simon
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Snippet The work presented here investigates how environmental features can be used to help select a task allocation mechanism from a portfolio in a multi-robot...
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StartPage 421
SubjectTerms Auction mechanism
Multi-robot team
Task allocation
Title Mechanism Selection for Multi-Robot Task Allocation
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