Bid2Charge: Market user interface design for electric vehicle charging
Bid2Charge: Market user interface design for electric vehicle charging
We consider settings where owners of electric vehicles (EVs) participate in a market mechanism to charge their vehicles. Existing work on such mechanisms has typically assumed that participants are fully rational and can report their preferences accurately to the mechanism or to a software agent participating on their behalf. However, this may not be reasonable in settings with non-expert human end-users. To explore this, we compare a fully expressive interface that covers the entire space of preferences to two restricted interfaces that reduce the space of possible options. To enable this analysis, we develop a novel game that replicates key features of an abstract EV charging scenario. In two extensive evaluations with over 300 users, we show that restricting the users' preferences significantly reduces the time they spend deliberating. More surprisingly, it also leads to an increase in their utility compared to the fully expressive interface (up to 70%). Finally, we find that a reinforcement learning agent displays similar performance trends, enabling a novel methodology for evaluating market interfaces.
882-890
Association for Computing Machinery
Stein, S.
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Gerding, E.
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Nedea, A.
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Rosenfeld, A.
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Jennings, N.R.
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May 2016
Stein, S.
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Gerding, E.
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Nedea, A.
f636a77e-28ab-4733-99c4-43d09c6ef950
Rosenfeld, A.
d2209641-6339-434c-b578-74bf364d098b
Jennings, N.R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Stein, S., Gerding, E., Nedea, A., Rosenfeld, A. and Jennings, N.R.
(2016)
Bid2Charge: Market user interface design for electric vehicle charging.
Thangarajah, J., Tuyls, K., Jonker, C. and Marsella, S.
(eds.)
In AAMAS '16 Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems.
Association for Computing Machinery.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
We consider settings where owners of electric vehicles (EVs) participate in a market mechanism to charge their vehicles. Existing work on such mechanisms has typically assumed that participants are fully rational and can report their preferences accurately to the mechanism or to a software agent participating on their behalf. However, this may not be reasonable in settings with non-expert human end-users. To explore this, we compare a fully expressive interface that covers the entire space of preferences to two restricted interfaces that reduce the space of possible options. To enable this analysis, we develop a novel game that replicates key features of an abstract EV charging scenario. In two extensive evaluations with over 300 users, we show that restricting the users' preferences significantly reduces the time they spend deliberating. More surprisingly, it also leads to an increase in their utility compared to the fully expressive interface (up to 70%). Finally, we find that a reinforcement learning agent displays similar performance trends, enabling a novel methodology for evaluating market interfaces.
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Accepted/In Press date: 26 January 2016
Published date: May 2016
Venue - Dates:
2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS '16), , Singapore, Singapore, 2016-05-09 - 2016-05-13
Organisations:
Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 387250
URI: http://eprints.soton.ac.uk/id/eprint/387250
PURE UUID: 17417d64-476a-4c52-b2e8-a02f2ffafa18
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Date deposited: 11 Feb 2016 16:14
Last modified: 16 Mar 2024 03:57
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Contributors
Author:
S. Stein
Author:
E. Gerding
Author:
A. Nedea
Author:
A. Rosenfeld
Author:
N.R. Jennings
Editor:
J. Thangarajah
Editor:
K. Tuyls
Editor:
C. Jonker
Editor:
S. Marsella
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