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We study how to coordinate a team of mobile robots to visit a number of given targets in a partially unknown terrain. Robotics researchers have studied single-item auctions to perform this exploration task but these do not make synergies between the targets into account. We therefore design combinatorial auctions, propose different combinatorial bidding strategies and compare their performance with each other, as well as to single item auctions and an optimal centralized mechanism. Our computational results in teambots, a multi-robot simulator, indicate that combinatorial auctions generally lead to significantly superior team performance than single-item auctions, and generate very good results compared to an optimal centralized mechanism.

Airline transportation systems are inherently random. However, airline planning models do not explicitly consider stochasticity in operations. Because of this, there is often a notable discrepancy between a schedule's planned and actual performance. SimAir is a modular airline simulation that simulates the daily operations of a domestic airline. Its primary purpose is to evaluate plans, such as crew schedules, as well as recovery policies in a random environment. We describe the structure of SimAir, and we give future directions for the study of airline planning under uncertainty.