Aggregating the opinions of different agents is a powerful way to find high-quality solutions to complex problems. However, when using agents in this fashion, there are
two fundamental open questions. First, given a universe of
agents, how to quickly identify which ones should be used
to form a team? Second, given a team of agents, what is the
best way to aggregate their opinions?
Many researchers value diversity when forming teams. LiCalzi and Surucu (2012) and Hong and Page (2004) propose
models where the agents know the utility of the solutions,
and the team converges to the best solution found by one
of its members. Clearly in complex problems the utility of
solutions would not be available, and agents would have to
resort to other methods, such as voting, to take a common
decision. Lamberson and Page (2012) study diversity in the
context of forecasts, where the solutions are represented by
real numbers and the team takes the average of the opinion
of its members. Domains where the possible solutions are
discrete, however, are not captured by such a model.
I proposed a new model to study teams of agents that vote
in discrete solution spaces (Marcolino, Jiang, and Tambe
2013), where I show that a diverse team of weaker agents can
overcome a uniform team made of copies of the best agent.
However, this phenomenon does not always occur, and it is
still necessary to identify when we should use diverse teams
and when uniform teams would be more appropriate.
Hence, in Marcolino et al. (2014b), I shed a new light into
this problem, by presenting a new, more general model of
diversity for teams of voting agents. Using that model I can
predict that diverse teams perform better than uniform teams
in problems with a large action space.
All my predictions are verified in a real system of voting
agents, in the Computer Go domain. I show that: (i) a team
of diverse players gets a higher winning rate than a uniform
team made of copies of the best agent; (ii) the diverse team
plays increasingly better as the board size increases.
Moreover, I also performed an experimental study in the
building design domain. This is a fundamental domain in
the current scenario, since it is known that the design of
a building has a major impact in the consumption of energy throughout its whole lifespan (Lin and Gerber 2014). It
is fundamental to design energy efficient buildings. Meanwhile, it is important to balance other factors, such as construction cost, creating a multi-objective optimization problem. I show that by aggregating the opinions of a team of
agents, a higher number of 1
st ranked solutions in the Pareto
frontier is found than when using a single agent. Moreover,
my approach eliminates falsely reported 1
st ranked solutions
(Marcolino et al. 2014a; 2015).
As mentioned, studying different aggregation rules is also
fundamental. In Jiang et al. (2014), I introduce a novel
method to extract a ranking from agents, based on the frequency that actions are played when sampling them multiple
times. My method leads to significant improvements in the
winning rate in Go games when using the Borda voting rule
to aggregate the generated rankings.