Abstract
Crisis is an infrequent and unpredictable event which is challenging to prepare and resolve. Serious-game approach proved to provide potential support in training and simulating event of real-world crisis situation to different stakeholders. Yet in practice, the approach meets with difficulty on how to setup and utilize different core components such as asset management, crisis scenario generation, agent simulation, real-world constraints, and the evaluation process to yield beneficial information upon running the system. To address this issue, the key question is what can be done to propose a general crisis game-based framework providing necessary core components while generating evaluation result yielding potential analytical data for a crisis management process. Therefore, in this paper, we aim to review and consolidate the existing research on scenario generation techniques and related crisis simulation framework, then to propose novel solution to combine both processes and to derive a desirable scenario content which is also being validated in the simulation framework based on the JADE multi-agent architecture.
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References
Walker, W.E., Giddings, J., Armstrong, S.: Training and learning for crisis management using a virtual simulation/gaming environment. Cogn. Technol. Work 13(3), 163–173 (2011)
Zyda, M.: From visual simulation to virtual reality to games. Computer 38(9), 25–32 (2005)
Hullett, K., Mateas, M.: Scenario generation for emergency rescue training games. In: 4th International Conference on Foundations of Digital Games - FDG 2009 (2009)
Grois, E., Hsu, W.H., Voloshin, M., Wilkins, D.C.: Bayesian network models for generation of crisis management training scenarios. In: AAAI/IAAI, pp. 1113–1120 (1998)
Martin, G., Schatz, S., Bowers, C., Hughes, C., Fowlkes, J., Nicholson, D.: Automatic scenario generation through procedural modeling for scenario-based training. Hum. Factors Ergon. Soc. Annu. Meet. 53, 1949–1953 (2009)
Zook, A., Riedl, M.O., Holden, H.K., Sottilare, R.A., Brawner, K.W.: Automated scenario generation: toward tailored and optimized military training in virtual environments. In: 7th International Conference on the Foundations of Digital Games (2012)
Porteous, J., Cavazza, M., Charles, F.: Applying planning to interactive storytelling: narrative control using state constraints. ACM Trans. Intell. Syst. Technol. 1(2), 10 (2010)
Ören, T., Longo, F.: Emergence, anticipation and multisimulation: bases for conflict simulation. In: 20th European Modeling and Simulation Symposium (EMSS), pp. 546–555 (2008)
Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)
Challenger, R., Clegg, C.W., Robinson, M.A.: Understanding crowd behaviours: Simulation tools. U.K. Cabinet Office (2009)
Metello, M.G., Casanova, M.A., de Carvalho, M.T.M.: Using serious game techniques to simulate emergency situations. In: GeoInfo, pp. 121–182 (2008)
Schoenharl, T., Madey, G.: Design and implementation of agent-based simulation for emergency response and crisis management. J. Algorithms Comput. Technol. 5, 4 (2011)
Saoud, N.B.B., Mena, T.B., Dugdale, J., Pavard, B., Ahmed, M.B.: Assessing large scale emergency rescue plans: an agent based approach. Int. J. Intell. Control Syst. 11(4), 260–271 (2006)
Takeuchi, I.: A massively multi-agent simulation system for disaster mitigation. In: Ishida, T., Gasser, L., Nakashima, H. (eds.) MMAS 2005. LNCS (LNAI), vol. 3446, pp. 269–282. Springer, Heidelberg (2005)
Siddhartha, H., Sarika, R., Karlapalem, K.: Score vector: a new evaluation scheme for RoboCup Rescue simulation competition (2009)
Boucher, A., Canal, R., Chu, T., Drogoul, A., Gaudou, B., Le, V., Moraru, V., Van Nguyen, N., Vu, Q., Taillandier, P.: The AROUND project: adapting robotic disaster response to developing countries. In: IEEE Workshop on Safety, Security and Rescue Robotics, pp. 1–6, Washington, D.C. (2009)
Mysore, V., Narzisi, G., Mishra, B.: Agent modeling of a sarin attack in manhattan. In: First International Workshop on Agent Technology for Disaster Management, pp. 108–115 (2006)
Narzisi, G., Mysore, V., Mishra, B.: Multi-objective evolutionary optimization of agent-based models: an application to emergency response planning. In: Computational Intelligence, pp. 228–232 (2006)
Barrett, C.L., Eubank, S.G., Smith, J.P.: If smallpox strikes portland. Sci. Am. J. 292(3), 54–61 (2005)
Mniszewski, S.M., Del Valle, S.Y., Stroud, P.D., Riese, J.M., Sydoriak, S.J.: EpiSimS simulation of a multi-component strategy for pandemic influenza. In: Proceedings of the 2008 Spring Simulation Multiconference, pp. 556–563 (2008)
FIPA Agent Communication Language (ACL) Specification. http://www.fipa.org/repository/aclspecs.html. Accessed 31 Jan 2016
Hoffmann, J.: The Metric-FF planning system: translating ‘‘ignoring delete lists’’ to numeric state variables. J. Artif. Intell. Res. 291–341 (2003)
Yokoo, M.: Constraint satisfaction problem. Distributed Constraint Satisfaction, pp. 1–45. Springer, Heidelberg (2001)
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Praiwattana, P., El Rhalibi, A. (2016). Survey: Development and Analysis of a Games-Based Crisis Scenario Generation System. In: El Rhalibi, A., Tian, F., Pan, Z., Liu, B. (eds) E-Learning and Games. Edutainment 2016. Lecture Notes in Computer Science(), vol 9654. Springer, Cham. https://doi.org/10.1007/978-3-319-40259-8_8
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DOI: https://doi.org/10.1007/978-3-319-40259-8_8
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