Exploiting meta-level information in a distributed scheduling system
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Distributed constraint optimization for medical appointment scheduling
Proceedings of the fifth international conference on Autonomous agents
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Secure Distributed Constraint Satisfaction: Reaching Agreement without Revealing Private Information
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Poaching and Distraction in Asynchronous Agent Activities
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Coordinated Hospital Patient Scheduling
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Constraint Processing
Distributed Constraint Satisfaction and Optimization with Privacy Enforcement
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
CMRadar: a personal assistant agent for calendar management
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Texture-based heuristics for scheduling revisited
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Learning user preferences in distributed calendar scheduling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Autonomous Agents and Multi-Agent Systems
Hierarchical variable ordering for distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Solving abduction by computing joint explanations
Annals of Mathematics and Artificial Intelligence
Collaboration among a satellite swarm
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Constraint Relaxation Approach for Over-Constrained Agent Interaction
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Addressing the Brittleness of Agent Interaction
PRIMA '08 Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems
ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking
Autonomous Agents and Multi-Agent Systems
Privatizing constraint optimization
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Analysis of privacy loss in distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. The goal is to represent problems in which agents must agree on scheduling decisions, for example, to agree on the start time of a meeting. We investigate a challenging class of MAP - private, incremental MAP (piMAP) in which agents do incremental scheduling of activities and there exist privacy restrictions on information exchange. We investigate a range of strategies for piMAP, called "bumping" strategies. We empirically evaluate these strategies in the domain of calendar management where a personal assistant agent must schedule meetings on behalf of its human user. Our results show that bumping decisions based on scheduling difficulty models of other agents can significantly improve performance over simpler bumping strategies.