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Abstract-Multi-issue negotiations are a central component of many important coordination challenges. Almost all previous work in this area has assumed that negotiation issues are independent, making it relatively easy to find high-quality agreements. In many real-world problem domains, however, issues are interdependent, making hard to find good agreement due to the nonlinearity of the agent's utility functions. The key challenge, in this context, is finding high-quality agreements without making unrealistic demands concerning how much agents reveal about their utilities. In this paper, we propose a protocol wherein the negotiating agents, working with the mediator, progress through a multi-phase narrowing of the space of possible agreements. We show that our method outperforms existing methods in large nonlinear utility spaces, and is computationally feasible for negotiations with as many as ten agents.

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We consider the problem of dynamic reconfiguration of robot teams when they encounter obstacles while navigating in formation, in an initially unknown environment. We have used a framework from coalition game theory called weighted voting games to analyse this problem and proposed two heuristics that can appropriately partition a robot team into sub-teams. We have experimentally verified our technique on teams of e-puck robots of different sizes and with different obstacle geometries, both on the Webots simulator and on physical robots. We have also shown that our technique performs faster and generates considerably fewer partitions than an existing robot coalition formation algorithm.