Payoff distribution in robust coalitional games on time-varying networks

In this article, we consider a sequence of transferable utility coalitional games, where the actual coalitional values are unknown but vary within known bounds. As a solution to the resulting family of games, we formalize the notion of 'robust core.' Our main contribution is to design two distributed algorithms, namely 1) distributed payoff...

Online Multi-Robot Task Assignment with Stochastic Blockages

In this paper we study the multi-robot task assignment problem with tasks that appear online and need to be serviced within a fixed time window in an uncertain environment. For example, when deployed in dynamic, human-centered environments, the team of robots may not have perfect information about the environment. Parts of the environment may...

Uncertain uncertainty in data-driven stochastic optimization: towards structured ambiguity sets

Ambiguity sets of probability distributions are a prominent tool to hedge against distributional uncertainty in stochastic optimization. The aim of this paper is to build tight Wasserstein ambiguity sets for data-driven optimization problems. The method exploits independence between the distribution components to introduce structure in the...

Distributionally Robust Optimization via Haar Wavelet Ambiguity Sets

This paper introduces a spectral parameterization of ambiguity sets to hedge against distributional uncertainty in stochastic optimization problems. We build an ambiguity set of probability densities around a histogram estimator, which is constructed by independent samples from the unknown distribution. The densities in the ambiguity set are...