It is known that we can aggregate the opinions of different agents to find high-quality solutions to complex problems. However, choosing agents to form a team is still a great challenge. Moreover, it is essential to use a good aggregation methodology in order to unleash the potential of a given team in solving complex problems. In my thesis, I present two different novel models to aid in the team formation process. Moreover, I propose a new methodology for extracting rankings from existing agents. I show experimental results both in the Computer Go domain and in the building design domain.

In this research-in-progress paper we present a new real world domain for studying the aggregation of different opinions: early stage architectural design of buildings. This is an important real world application, not only because building design and construction is one of the world's largest industries measured by global expenditures, but also because the early stage design decision making has a significant impact on the energy consumption of buildings. We present a mapping between the domain of architecture and engineering research and that of the agent models present in the literature. We study the importance of forming diverse teams when aggregating the opinions of different agents for architectural design, and also the effect of having agents optimizing for different factors of a multi-objective optimization design problem. We find that a diverse team of agents is able to provide a higher number of top ranked solutions for the early stage designer to choose from. Finally, we present the next steps for a deeper exploration of our questions.