Coalition structure generation with worst case guarantees
Artificial Intelligence
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Marginal contribution nets: a compact representation scheme for coalitional games
Proceedings of the 6th ACM conference on Electronic commerce
A tractable and expressive class of marginal contribution nets and its applications
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
An anytime algorithm for optimal coalition structure generation
Journal of Artificial Intelligence Research
Complexity of constructing solutions in the core based on synergies among coalitions
Artificial Intelligence
Coalition structure generation in multi-agent systems with positive and negative externalities
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Coalition structure generation utilizing compact characteristic function representations
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
A logic-based representation for coalitional games with externalities
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Coalition structure generation in multi-agent systems with mixed externalities
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A distributed algorithm for anytime coalition structure generation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
On the complexity of core, kernel, and bargaining set
Artificial Intelligence
Complexity of coalition structure generation
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Concise characteristic function representations in coalitional games based on agent types
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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A Coalition Structure Generation (CSG) problem involves partitioning a set of agents into coalitions so that the social surplus is maximized. Recently, Ohta et al. developed an efficient algorithm for solving CSG assuming that a characteristic function is represented by a set of rules, such as marginal contribution networks (MC-nets). In this paper, we extend the formalization of CSG in Ohta et al. so that it can handle negative value rules. Here, we assume that a characteristic function is represented by either MC-nets (without externalities) or embedded MC-nets (with externalities). Allowing negative value rules is important since it can reduce the efforts for describing a characteristic function. In particular, in many realistic situations, it is natural to assume that a coalition has negative externalities to other coalitions. To handle negative value rules, we examine the following three algorithms: (i) a full transformation algorithm, (ii) a partial transformation algorithm, and (iii) a direct encoding algorithm. We show that the full transformation algorithm is not scalable in MC-nets (the worst-case representation size is Ω(n2), where n is the number of agents), and does not seem to be tractable in embedded MC-nets (representation size would be Ω(2n)). In contrast, by using the partial transformation or direct encoding algorithms, an exponential blow-up never occurs even for embedded MC-nets. For embedded MC-nets, the direct encoding algorithm creates less rules than the partial transformation algorithm. Experimental evaluations show that the direct encoding algorithm is scalable, i.e., an off-the-shelf optimization package (CPLEX) can solve problem instances with 100 agents and rules within 10 seconds.