Electronic mail and weak ties in organizations
Office Technology and People - Computer-Supported Cooperative Work
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Distributed snapshots: determining global states of distributed systems
ACM Transactions on Computer Systems (TOCS)
Satisfying user preferences while negotiating meetings
International Journal of Human-Computer Studies - Special issue: group support systems
Nurse scheduling using constraint logic programming
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
A New Distributed Approach to Solve Meeting Scheduling Problems
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Agent-Based Approach to Dynamic Meeting Scheduling Problems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Scheduling meetings with distributed local consistency reinforcement
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A Meeting Scheduling Problem Respecting Time and Space
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Multi-agent resource allocation (MARA) for modeling construction processes
Proceedings of the 40th Conference on Winter Simulation
A meeting scheduling problem respecting time and space
Geoinformatica
Applications of agent-based models for optimization problems: A literature review
Expert Systems with Applications: An International Journal
Agent and multi-agent applications to support distributed communities of practice: a short review
Autonomous Agents and Multi-Agent Systems
An Agent Based Approach to Patient Scheduling Using Experience Based Learning
International Journal of Agent Technologies and Systems
A personal meeting scheduling agent
Personal and Ubiquitous Computing
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Multi-agent systems are widely used to address large-scale distributed combinatorial applications in the real world. One such application is meeting scheduling (MS), which is defined by a variety of features. The MS problem is naturally distributed and especially subject to many alterations. In addition, this problem is characterized by the presence of users' preferences that turn it into a search for an optimal rather than a feasible solution. However, in real-world applications users usually have conflicting preferences, which makes the solving process an NP-hard problem. Most research efforts in the literature, adopting agent-based technologies, tackle the MS problem as a static problem. They often share some common properties: allowing the relaxation of any user's time restriction, not dealing with achieving any level of consistency among meetings to enhance the efficiency of the solving process, not tackling the consequences of the dynamic environment, and especially not addressing the real difficulty of distributed systems which is the complexity of message passing operations. In an attempt to facilitate and streamline the process of scheduling meetings in any organization, the main contribution of this work is a new scalable agent-based approach for any dynamic MS problem (that we called MSRAC, for Meeting Scheduling with Reinforcement of Arc Consistency). In this approach we authorize only the relaxation of users' preferences while maintaining arc-consistency on the problem. The underlying protocol can efficiently reach the optimal solution (satisfying some predefined optimality criteria) whenever possible, using only minimum localized asynchronous communications. This purpose is achieved with minimal message passing while trying to preserve at most the privacy of involved users. Detailed experimental results on randomly generated MS problems show that MSRAC is scalable and it leads to speed up over other approaches, especially for large problems with strong constraints.