Methods for task allocation via agent coalition formation
Artificial Intelligence
Reducing buyer search costs: implications for electronic marketplaces
Management Science - Special issue: Frontier research on information systems and economics
Coalition structure generation with worst case guarantees
Artificial Intelligence
A stable and efficient buyer coalition formation scheme for e-marketplaces
Proceedings of the fifth international conference on Autonomous agents
Autonomous Agents and Multi-Agent Systems
Coalition Formation for Large-Scale Electronic Markets
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Mechanisms for coalition formation and cost sharing in an electronic marketplace
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
Integrating parallel interactions into cooperative search
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Physical search problems applying economic search models
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Enhancing cooperative search with concurrent interactions
Journal of Artificial Intelligence Research
Enhancing MAS cooperative search through coalition partitioning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Collaborative multi agent physical search with Probabilistic knowledge
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multi-goal economic search using dynamic search structures
Autonomous Agents and Multi-Agent Systems
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In this paper we study search strategies of agents that represent buyer agents' coalitions in electronic marketplaces. The representative agents operate in environments where numerous potential complex opportunities can be found. Each opportunity is associated with several different terms and conditions thus differing from other opportunities by its value for the coalition. Given a search cost, the goal of the representative agent is to find the best set of opportunities which fulfills the coalition's demands with the maximum overall utility, to be divided among the coalition members. Given the option of side-payments, this strategy will always be preferred by all coalition members (thus no conflict of interests), regardless of the coalition's payoff division protocol. We analyze the incentive to form such coalitions and extract the optimal search strategy for their representative agents, with a distinction between operating in B2C and C2C markets. Based on our findings we suggest efficient algorithms to be used by the representative agents for calculating a strategy that maximizes their expected utilities. A computational-based example is given, illustrating the achieved performance as a function of the coalition's members' heterogeneity level.