On the complexity of cooperative solution concepts
Mathematics of Operations Research
Coalition, cryptography, and stability: mechanisms for coalition formation in task oriented domains
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On the complexity of testing membership in the core of min-cost spanning tree games
International Journal of Game Theory
Methods for task allocation via agent coalition formation
Artificial Intelligence
Marginal contribution nets: a compact representation scheme for coalitional games
Proceedings of the 6th ACM conference on Electronic commerce
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Coalitional games in open anonymous environments
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Complexity of determining nonemptiness of the core
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Anonymity-proof Shapley value: extending shapley value for coalitional games in open environments
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Reasoning about coalitional games
Artificial Intelligence
False name manipulations in weighted voting games: splitting, merging and annexation
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
On representing coalitional games with externalities
Proceedings of the 10th ACM conference on Electronic commerce
Enhancing MAS cooperative search through coalition partitioning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An anytime coalition restructuring algorithm in an open environment
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Multi-goal economic search using dynamic search structures
Autonomous Agents and Multi-Agent Systems
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
Computational Aspects of Extending the Shapley Value to Coalitional Games with Externalities
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
False-name manipulations in weighted voting games
Journal of Artificial Intelligence Research
Coalition structure generation over graphs
Journal of Artificial Intelligence Research
An efficient vector-based representation for coalitional games
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Coalition formation is an important capability of automated negotiation among self-interested agents. In order for coalitions to be stable, a key question that must be answered is how the gains from cooperation are to be distributed. Recent research has revealed that traditional solution concepts, such as the Shapley value, core, least core, and nucleolus, are vulnerable to various manipulations in open anonymous environments such as the Internet. These manipulations include submitting false names, collusion, and hiding some skills. To address this, a solution concept called the anonymity-proof core, which is robust against such manipulations, was developed. However, the representation size of the outcome function in the anonymity-proof core (and similar concepts) requires space exponential in the number of agents/skills. This paper proposes a compact representation of the outcome function, given that the characteristic function is represented using a recently introduced compact language that explicitly specifies only coalitions that introduce synergy. This compact representation scheme can successfully express the outcome function in the anonymity-proof core. Furthermore, this paper develops a new solution concept, the anonymity-proof nucleolus, that is also expressible in this compact representation. We show that the anonymity-proof nucleolus always exists, is unique, and is in the anonymity-proof core (if the latter is nonempty). and assigns the same value to symmetric skills.