Ambient Intelligence Services Personalization via Social Choice Theory

1. Ambient Intelligence Services Personalization via Social
Choice Theory
December 2014
http://gsi.dit.upm.es
Emilio Serrano, Pablo Moncada, Mercedes Garijo and Carlos A. Iglesias
{emilioserra,pmoncada,mga,cif}@dit.upm.es
Intelligent Systems Group
Technical University of Madrid
http://www.gsi.dit.upm.es
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2. Outline
• Outline.
 Motivation.
 How easy is (automatic) voting?
 Target system
 Users’ preferences theories
 VoteSim and case study
 Experimental results
 Conclusion
http://gsi.dit.upm.es 2

3. Motivation
• Ambient Intelligence (AmI) focuses on adapting to people’s needs and
particular situations.
• Important question: what happen when resources are shared and there are
conflicts between users’ preferences?
 AmI systems may not only have to offer a good service considering
users’ preferences,
 but also to make a decision in an attempt to maximize the users’ welfare
when they access shared services
• Some examples in hotels:
 screening rooms, decoration based on dynamic decorative panels, dance
clubs, heated swimming pools, etcetera.
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4. Motivation 2
• Accessing shared resources has not
been explored in the AmI specialized
literature
 Just knowing the “who” and the
“where” in a non intrusive
manner is tough
 But still a great research question
for more intrusive or future
scenarios
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5. Motivation 3
• Agreement technologies (ATs) and
multi-agent systems (MASs) have
studied this in depth.
 multi-issue negotiations,
concurrent negotiations, strategy-
proof mechanisms,
argumentation, auctions, voting,
etcetera
 social choice theory seems
straightforward.
 The evaluation of alternative
methods of collective
decision-making
 There aren’t good arguments
for “The football match is
okay, but I’d rather watch
game of thrones”
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6. Motivation 4
• Primary hypothesis: Social choice
theory in Ambient Intelligence systems can
improve significantly users’ satisfaction when
accessing shared resources
 … but the social choice theory, has mainly
focused on theoretical works which deal with
political elections
 … even when the most popular MASs books
include a social choice chapter
• Research methodology based on agent based
social simulations is employed to support this
hypothesis and to evaluate these benefits.
 what are the benefits of using a voting system
in an intelligent environment?; what are the
most suitable voting systems?; and, what
differences does this case present when
compared to political elections?
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7. How easy is (automatic) voting?
• There are a large number voting systems
 Plurality, range, Borda, approval, cumulative, etcetera
 Cumulative: Each voter is given k votes, which can be cast arbitrarily.
• Voting may involve strategy
 In UK we have the Labor (left-wing party), Liberal (center-left) and Conservative (right
wing) parties, what a very left-wing voter should vote in a district with a bias towards
the Conservative party?.
 (Preferences + Voting method) may not be enough to vote
 e.g. cumulative voting
• Everybody knows that voting leads to ignored minorities…
 But this may happen for majorities
Podemos > IU > PSOE
IU> PSOE > Podemos
PSOE > Podemos > IU
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8. How easy is (automatic) voting? 2
• No silver bullet!, the best group decision policy depends on:
 Voting method
 Users strategies
 The users and their preferences when using services
 The environment and how the services are deployed
 The possible services configurations
 The number of repeated services
• Methods and tools are needed a make a decision on how to maximize
social welfare
 …and to minimize the maximum time without a wanted
configuration/service.
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9. Target system
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10. Users’ preferences theories
iduser sports sitcom Doc. soap Cart. news reality quiz movie
u1 1 4 4 9 0 2 3 4 3
u2 9 0 4 7 7 3 8 6 10
u3 9 1 6 10 2 6 2 5 10
u4 2 10 2 3 9 5 9 7 10
u5 6 7 9 3 6 5 1 5 1
u6 0 10 4 3 1 9 4 1 2
u7 10 7 2 7 5 2 6 5 0
• I recommend you to go to room Sx
•(more users in your cluster)
•E.g. u2, try to vote where u3 is and avoid the
place where u6 is.
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(It would be nice to
combine several services: you
like TV here but temperature
is not your cup of tea)

11. VoteSim and case study
• VoteSim is an agent based social simulator designed for:
 finding out the best group decision policy for a specific case
 exploring manners of forming coalitions and their effects
 estimating the usability of a vote system
 studying security issues such as the weakness to tactic vote
 validating software applications for group decision
• VoteSim is based on the UbikSim simulator developed in collaboration with the
University of Murcia
 https://github.com/emilioserra/UbikSim/wiki
• Let us observe the use of VoteSim for a hotel where there are shared TVs in the hall
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12. Experimental results
Forming coalitions is
more important than
the voting method!
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13. Experimental results 2
• Range voting always gets the best satisfaction…
• but pretty bad worst wait (in this scenario) and terrible usability
• …a proposed method gets the best balance among metrics
• …but usability as bad as range voting
• In this scenario, I would choose an approval method
 Kind of like “Tinder” but without getting to flirt
 But it is a design decision, VoteSim is given for new experiments in new scenarios
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14. Conclusion
• This paper concludes, by sound and reproducible results, that the use of social choice theory
in AmI systems can improve considerably users’ satisfaction when accessing shared
resources.
• There are significant differences between the AmI case and the political elections
• Different metrics are proposed to estimate welfare
• The novel voting algorithm called exchange of weights is the most balanced voting system
considered
• The use of greedy pre-selection mechanisms based on Euclidean or Manhattan distances is
more important than the voting method
• To ensure the reproducibility of the experimental results given in this paper, the free and
open-source toolVoteSim
• Future works: more voting methods in VoteSim, more cluster techniques, more complex
preferences:
• More in: Evaluating social choice techniques into intelligent environments by agent
based social simulation. In: Information Sciences , 286 (0), pp. 102–124, 2014, ISBN: 0020-
0255, (Impact Factor 2013, 3.893, Q1).
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