Multi-agent Team Formation: Solving Complex Problems by Aggregating Opinions

Citation:

L. S. Marcolino. 1/2015. “Multi-agent Team Formation: Solving Complex Problems by Aggregating Opinions .” In Conference on Artificial Intelligence (AAAI 2015). (Doctoral Consortium). Texas, USA.

Abstract:

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.
See also: 2015