Gal A. Kaminka's Publications

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Who Goes there? Using Social Regret to Select a Robot to Reach a Goal

Meytal Traub, Gal A. Kaminka, and Noa Agmon. Who Goes there? Using Social Regret to Select a Robot to Reach a Goal . In Proceedings of the Tenth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-11), 2011.

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Abstract

A common decision problem in multi-robot applications involves deciding on which robot, out of a group of $N$ robots, should travel to a goal location, to carry out a task there. Trivially, this decision problem can be solved greedily, by selecting the robot with the shortest expected travel time. However, this ignores the inherent uncertainty in path traversal times; we may prefer a robot that is slower (but always takes the same time), over a robot that is expected to reach the goal faster, but on occasion takes a very long time to arrive. We make several contributions that address this challenge. First, we bring to bear economic decision-making theory, to distinguish between different selection policies, based on risk (risk averse, risk seeking, etc.). Second, we introduce social regret (the difference between the actual travel time by the selected robot, and the hypothetical time of other robots) to augment decision-making in practice. Then, we carry out experiments in simulation and with real robots, to demonstrate the usefulness of the selection procedures under real-world settings, and find that travel-time distributions have repeating characteristics.

Additional Information

BibTeX

@InProceedings{aamas11meytal,
author = {Meytal Traub and Gal A. Kaminka and Noa Agmon},
title = {Who Goes \emph{there}? Using Social Regret to Select a Robot to Reach a Goal },
booktitle = AAMAS-11,
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year = {2011},
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abstract = {A common decision problem in multi-robot applications involves deciding on which 
robot, out of a group of $N$ robots, should travel to a goal location, to carry 
out a task there. Trivially,  this decision problem can be solved greedily, by selecting the robot 
with the shortest expected travel time. However, this ignores the 
inherent uncertainty in path traversal times; we may prefer a robot that is slower 
(but always takes the same time), over a robot that is expected to reach the goal 
faster, but on occasion takes a very long time to arrive. We make several contributions that address  
this challenge. First, we bring to bear economic decision-making theory, to 
distinguish between different selection policies, based on risk (risk averse, risk seeking, etc.). 
Second, we introduce \emph{social regret} (the difference between the actual travel time by the selected 
robot, and the hypothetical time of other robots) to augment decision-making 
in practice. Then, we carry out experiments in simulation and with real  
robots, to demonstrate the usefulness of the selection procedures under real-world settings, 
and find that travel-time distributions have repeating characteristics. },
  wwwnote = {}, 
}

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