Forming coalitions in the face of uncertain rewards
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Anytime coalition structure generation with worst case guarantees
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Satisficing coalition formation among agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Customer Coalitions in Electronic Markets
Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems (includes revised papers from AMEC 2000 Workshop)
Self-organization through bottom-up coalition formation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Generating Coalition Structures with Finite Bound from the Optimal Guarantees
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
On representing coalitional games with externalities
Proceedings of the 10th ACM conference on Electronic commerce
Optimal Coalition Structure Generation In Partition Function Games
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Overlapping coalition formation for efficient data fusion in multi-sensor networks
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Anytime optimal coalition structure generation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Near-optimal anytime coalition structure generation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Task allocation via coalition formation among autonomous agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Coalition structure generation in multi-agent systems with positive and negative externalities
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Modeling and learning synergy for team formation with heterogeneous agents
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Handling negative value rules in MC-net-based coalition structure generation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Synergy graphs for configuring robot team members
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Weighted synergy graphs for effective team formation with heterogeneous ad hoc agents
Artificial Intelligence
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Coalition structure generation (CSG) for multi-agent systems is a well-studied problem. A vast majority of the previous work and the state-of-the-art approaches to CSG assume a characteristic function form of the coalition values, where a coalition's value is independent of the other coalitions in the coalition structure. Recently, there has been interest in the more realistic partition function form of coalition values, where the value of a coalition is affected by how the other agents are partitioned, via externalities. We argue that in domains with externalities, a distributed/adaptive approach to CSG may be impractical, and that a centralized approach to CSG is more suitable. However, the most recent studies in this direction have focused on cases where all externalities are either always positive or always negative, and results on coalition structure generation in more general settings (in particular, mixed externalities) are lacking. In this paper we propose a framework based on agent-types that incorporates mixed externalities and demonstrate that it includes the previous settings as special cases. We also generalize some previous results in anytime CSG, showing that those results are again special cases. In particular, we extend the existing branch and bound algorithm to this new setting and show empirically that significant pruning can be achieved when searching for the optimal coalition structure. This extends the state-of-the-art in CSG for multi-agent systems.