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Coalition structure generation with worst case guarantees
Artificial Intelligence
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AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent 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
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
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AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Cooperative exploration in the electronic marketplace
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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Journal of Artificial Intelligence Research
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Artificial Intelligence
Market-based control of computational systems: introduction to the special issue
Autonomous Agents and Multi-Agent Systems
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WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Competitive Shopbots-Mediated Markets
ACM Transactions on Economics and Computation
Tasks for agent-based negotiation teams: Analysis, review, and challenges
Engineering Applications of Artificial Intelligence
Sequential multi-agent exploration for a common goal
Web Intelligence and Agent Systems
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This paper investigates cooperative search strategies for agents engaged in costly search in a complex environment. Searching cooperatively, several search goals can be satisfied within a single search effort. Given the searchers' preferences, the goal is to conduct a search in a way that the expected overall utility out of the set of opportunities found (e.g., products when operating in a market) minus the costs associated with finding that set is maximized. This search scheme, given in the context of a group search, applies also to scenarios where a single agent has to search for a set of items for satisfying several different goals. The uniqueness of the proposed mechanism is in the ability to partition the group of agents/goals into sub-groups where the search continues for each group autonomously. As we show throughout the paper, this strategy is favorable as it weakly dominates (i.e., can improve but never worsen) cooperative and autonomous search techniques. The paper presents a comprehensive analysis of the new search method and highlights the specific characteristics of the optimal search strategy. Furthermore, we introduce innovative algorithms for extracting the optimal search strategy in a range of common environments, that eliminates the computational overhead associated with the use of the partitioning technique.