ABSTRACT
University timetabling is a constraint-satisfaction problem where the participants usually have conflicting requirements. Multi-agent environment can solve timetabling problem through representing each participating party with agent. We propose to apply reinforcement learning approach to coordinate agents in a university timetabling system. The system simulation is built on JADE Framework.
- Safaai Deris, Sigeru Omatu, Hiroshi Ohta, Puteh Saad Incorporating constraint propagation in genetic algorithm for university timetable planning Engineering Applications of Artificial Intelligence, Volume 12, Issue 3, June 1999, Pages 241--253Google Scholar
- Mihaela Oprea, MAS_UP-UCT: A Multi-Agent System for University Course Timetable Scheduling, International Journal of Computers, Communications&Control Vol. II (2007), No. 1, pp. 94--102Google Scholar
- Pongcharoen P., Promtet W., Yenradee R., Hicks C., Stochastic Optimization Timetabling: Tool for university course scheduling, International Journal of Production Economics, Vol. 112, Issue 2, April 2008, pp. 903--918Google ScholarCross Ref
- Sutton R. S., G. Barto A. G., (1998) Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning), The MIT Press Google ScholarDigital Library
- Wooldridge M., An Introduction to MultiAgent Systems (2002), John Wiley and Sons Google ScholarDigital Library
- Gaspero L. D., Mizzaro S., Schaerf A., A MultiAgent Architecture for Distributed Course Timetabling, PATAT 2004 Conference paperGoogle Scholar
- Barto, A. G., Sutton, R. S., Watkins, C. J. C. H., Learning and sequential decision making, Learning and Computational Neuroscience, M. Gabriel and J. W. Moore (Eds.), pp 539--602Google Scholar
- Kaplansky E., Meisels A., Negotiation among Scheduling Agents for Distributed Timetabling, 5th International Conference on the Practice and Theory of Automated Timetabling PATAT'04 paperGoogle Scholar
- M. A. S. Kamal and Junichi Murata, International Journal of Knowledge-based and Intelligent Engineering Systems 11 (2007) 181--191 IOS Press Google ScholarDigital Library
- Fabio Luigi Bellifemine, Giovanni Caire, Dominic Greenwood, (2007) Developing Multi-Agent Systems with JADE, Wiley Series in Agent Technology Google ScholarDigital Library
- Dayan P., Watkins C., Reinforcement Learning, Encyclopedia of Cognitive ScienceGoogle Scholar
- S. Daskalaki, T. Birbas, Efficient solutions for a university timetabling problem through integer programming. European Journal of Operational Research, Vol. 160, (2005), pp. 106--120Google ScholarCross Ref
Index Terms
- Reinforcement learning coordination with combined heuristics in multi-agent environment for university timetabling
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