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Meytal Traub, Gal A. Kaminka,
and Noa Agmon. Who Goes there? Selecting a Robot to Reach a Goal
Using Social Regret . In Proceedings of the Tenth International Joint Conference on Autonomous Agents and Multi-Agent
Systems (AAMAS-11), 2011.
A common decision problem in multi-robot applications involves deciding on whichrobot, out of a group of $N$ robots, should travel to a goal location, to carryout a task there. Trivially, this decision problem can be solved greedily, by selecting the robotwith the shortest expected travel time. However, this ignores theinherent 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 goalfaster, but on occasion takes a very long time to arrive. We make several contributions that addressthis challenge. First, we bring to bear economic decision-making theory, todistinguish 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 selectedrobot, and the hypothetical time of other robots) to augment decision-makingin practice. Then, we carry out experiments in simulation and with realrobots, to demonstrate the usefulness of the selection procedures under real-world settings,and find that travel-time distributions have repeating characteristics.
@InProceedings{aamas11meytal,
author = {Meytal Traub and Gal A. Kaminka and Noa Agmon},
title = {Who Goes \emph{there}? Selecting a Robot to Reach a Goal Using Social Regret },
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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