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Research in cooperative games often assumes that agents know the coalitionalvalues with certainty, and that they can belong to one coalition only. Bycontrast, this work assumes that the value of a coalition is based on anunderlying collaboration structure emerging due to existing but unknownrelations among the agents; and that agents can form overlapping coalitions.Specifically, we first propose Relational Rules, a novel representation schemefor cooperative games with overlapping coalitions, which encodes theaforementioned relations, and which extends the well-known MC-netsrepresentation to this setting. We then present a novel decision-making methodfor decentralized overlapping coalition formation, which exploits probabilistictopic modeling, and in particular, online Latent Dirichlet Allocation. Byinterpreting formed coalitions as documents, agents can effectively learntopics that correspond to profitable collaboration structures.

Overlapping Coalition Formation via Probabilistic Topic Modeling