Agent-based coalition formation in disaster response applications
DIS '06 Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications
Negotiation-based coalitions in the physical world
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning models of intelligent agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Meeting Scheduling Assembles Children in the Rectangular Forest
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Decentralized time geography for ad-hoc collaborative planning
COSIT'09 Proceedings of the 9th international conference on Spatial information theory
Multiagent and Grid Systems - Advances in Agent-mediated Automated Negotiations
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Canonical problems are simplified representations of a class of real world problems. They allow researchers to compare algorithms in a standard setting which captures the most important challenges of the real world problems being modeled. Such examples are the block world for planning, two-player games for algorithms which learn the behavior of the opponent agent, or the "split the pie" game for a large class of negotiation problems. In this paper we focus on negotiating collaboration in space and time, a problem with many important real world applications. Although technically a multi-issue negotiation, we show that the problem can not be represented in a satisfactory manner by the split the pie model. We propose the "children in the rectangular forest" (CRF) model as a possible canonical problem for negotiating spatio-temporal collaboration. By exploring a centralized and a peer-to-peer negotiation based solution, we demonstrate that the problem captures the main challenges of the real world problems while allows us to simplify away some of the computationally demanding but semantically marginal features of real world problems.