Abstract
In this paper, an innovative application of scheduling methodology is advocated for the emerging service, which is named “social coordination” in the ubiquitous information environments. A typical service expected in ubiquitous computing is information provision adapted to each user’s current situation. The service is supposed to increase a single person’s convenience. However, a new type of service (“social coordination”) is also possible for improving conveniences of the people sharing the ubiquitous information environment. The author explains the concept of “ubiquitous scheduling” that eludes congestions in the society by scheduling people’s activities efficiently and rationally. To evaluate effectiveness of the concept, a multi-agent scheduler for an amusement park problem is implemented, which coordinates the demands for rides by tens of thousands people and makes suggestions as to when they should visit attractions in the amusement park to avoid standing in long lines.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bonabeau, E., Dorigo, M., Theraulaz, G. (eds.): Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)
Epstein, J.M., Axtell, R.: Growing Artificial Societies - Social Science from the Bottom UP. MIT Press, Cambridge (1996)
Kawamura, H., Kurumatani, K., Ohuchi, A.: Modeling of theme park problem with multiagent for mass support. In: Proc. of The IJCAI-03 Workshop on Multiagent for Mass-User Support, pp. 1–10 (2003)
Kurumatani, K.: Social coordination with architecture for ubiquitous agents: Consorts. In: Proc. of International Conference on Intelligent Agents, Web Technologies and Internet Commerce IAWTIC 2003 (2003)
Liu, J.-S., Sycara, K.P.: Exploiting problem structure for distributed constraint optimization. In: Proceedings of the First International Conference on Multi-Agent Systems, pp. 246–253. AAAI, Menlo Park (1995)
Miyashita, K.: CAMPS: A constraint-based architecture for multiagent planning and scheduling. Journal of Intelligent Manufacturing 9, 147–154 (1998)
Wellman, M.P., Walsh, W.E.: Auction protocols for decentralized scheduling. Games and Economic Behavior 35, 271–303 (2001)
Prado, J.E., Wurman, P.R.: Non-cooperative plannning in multi-agent, resource-constrained environments with markets for reservations. In: AAAI workshop planning with and for Multiagent Systems Technical Report WS-02-12, pp. 60–66 (2002)
Sadeh, N.: Micro-opportunistic scheduling: The micro-boss factory scheduler. In: Zweben, M., Fox, M. (eds.) Intelligent Scheduling. Morgan Kaufmann, San Mateo (1994)
Schilit, B., Adams, N., Want, R.: Context-aware computing applications. In: Proc. of IEEE Whorkshop on Mobile Computing Systems and Applications, pp. 85–90 (1994)
Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers C-29(12), 1104–1113 (1980)
Sycara, K.P., Roth, S.F., Sadeh, N., Fox, M.S.: Resource allocation in distributed factory scheduling. IEEE Expert 6(1), 29–40 (1991)
Veit, H., Richter, G.: The FTA design paradigm for distributed systems. Future Generation Computer Systems 16, 727–740 (2000)
Weiser, M.: Hot topic: Ubiquitous computing. IEEE Computer, 71–72 (1993)
Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: Distributed constraint satisfaction for formalizing distributed problem solving. In: Proceedings of the Twelfth International Conference on Distributed Computing Systems, pp. 614–621 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Miyashita, K. (2005). ASAP: Agent-Based Simulator for Amusement Park. In: Davidsson, P., Logan, B., Takadama, K. (eds) Multi-Agent and Multi-Agent-Based Simulation. MABS 2004. Lecture Notes in Computer Science(), vol 3415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32243-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-32243-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25262-7
Online ISBN: 978-3-540-32243-6
eBook Packages: Computer ScienceComputer Science (R0)