Market interfaces for electric vehicle charging
File(s)
Published version
Author(s)
Stein, S
Gerding, EH
Nedea, A
Rosenfeld, A
Jennings, N
Type
Journal Article
Abstract
We consider settings where owners of electric vehicles (EVs) participate in a market mech-
anism to charge their vehicles. Existing work on such mechanisms has typically assumed
that participants are fully rational and can report their preferences accurately via some
interface to the mechanism or to a software agent participating on their behalf. How-
ever, this may not be reasonable in settings with non-expert human end-users.Thus, our
overarching aim in this paper is to determine experimentally if a fully expressive market
interface that enables accurate preference reports is suitable for the EV charging domain,
or, alternatively, if a simpler, restricted interface that reduces the space of possible options
is preferable. In doing this, we measure the performance of an interface both in terms of
how it helps participants maximise their utility and how it affects deliberation time. Our
secondary objective is to contrast two different types of restricted interfaces that vary in
how they restrict the space of preferences that can be reported. To enable this analysis,
we develop a novel game that replicates key features of an abstract EV charging scenario.
In two experiments with over 300 users, we show that restricting the users’ preferences
significantly reduces the time they spend deliberating (by up to half in some cases). An
extensive usability survey confirms that this restriction is furthermore associated with a
lower perceived cognitive burden on the users. More surprisingly, at the same time, using
restricted interfaces leads to an increase in the users’ performance compared to the fully
expressive interface (by up to 70%). We also show that some restricted interfaces have
the desirable effect of reducing the energy consumption of their users by up to 20% while
achieving the same utility as other interfaces. Finally, we find that a reinforcement learning
agent displays similar performance trends to human users, enabling a novel methodology
for evaluating market interfaces.
anism to charge their vehicles. Existing work on such mechanisms has typically assumed
that participants are fully rational and can report their preferences accurately via some
interface to the mechanism or to a software agent participating on their behalf. How-
ever, this may not be reasonable in settings with non-expert human end-users.Thus, our
overarching aim in this paper is to determine experimentally if a fully expressive market
interface that enables accurate preference reports is suitable for the EV charging domain,
or, alternatively, if a simpler, restricted interface that reduces the space of possible options
is preferable. In doing this, we measure the performance of an interface both in terms of
how it helps participants maximise their utility and how it affects deliberation time. Our
secondary objective is to contrast two different types of restricted interfaces that vary in
how they restrict the space of preferences that can be reported. To enable this analysis,
we develop a novel game that replicates key features of an abstract EV charging scenario.
In two experiments with over 300 users, we show that restricting the users’ preferences
significantly reduces the time they spend deliberating (by up to half in some cases). An
extensive usability survey confirms that this restriction is furthermore associated with a
lower perceived cognitive burden on the users. More surprisingly, at the same time, using
restricted interfaces leads to an increase in the users’ performance compared to the fully
expressive interface (by up to 70%). We also show that some restricted interfaces have
the desirable effect of reducing the energy consumption of their users by up to 20% while
achieving the same utility as other interfaces. Finally, we find that a reinforcement learning
agent displays similar performance trends to human users, enabling a novel methodology
for evaluating market interfaces.
Date Issued
2017-06-01
Date Acceptance
2017-05-15
Citation
The Journal of Artificial Intelligence Research, 2017, 59, pp.175-227
ISSN
1076-9757
Publisher
AI Access Foundation
Start Page
175
End Page
227
Journal / Book Title
The Journal of Artificial Intelligence Research
Volume
59
Copyright Statement
© 2017 AI Access Foundation. All rights reserved.
Subjects
0102 Applied Mathematics
0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Artificial Intelligence & Image Processing
Publication Status
Published
Date Publish Online
2017-08-31