  Research in cooperative games often assumes that agents know the coalitional
values with certainty, and that they can belong to one coalition only. By
contrast, this work assumes that the value of a coalition is based on an
underlying collaboration structure emerging due to existing but unknown
relations among the agents; and that agents can form overlapping coalitions.
Specifically, we first propose Relational Rules, a novel representation scheme
for cooperative games with overlapping coalitions, which encodes the
aforementioned relations, and which extends the well-known MC-nets
representation to this setting. We then present a novel decision-making method
for decentralized overlapping coalition formation, which exploits probabilistic
topic modeling, and in particular, online Latent Dirichlet Allocation. By
interpreting formed coalitions as documents, agents can effectively learn
topics that correspond to profitable collaboration structures.
