Finding Resilient Solutions for Dynamic Multi-Objective Constraint Optimization Problems

Maxime Clement, Tenda Okimoto, Nicolas Schwind, Katsumi Inoue

2015

Abstract

Systems Resilience is a large-scale multi-disciplinary research that aims to identify general principles underlying the resilience of real world complex systems. Many conceptual frameworks have been proposed and discussed in the literature since Holling’s seminal paper (1973). Schwind et al. (2013) recently adopted a computational point of view of Systems Resilience, and modeled a resilient system as a dynamic constraint optimization problem. However, many real world optimization problems involve multiple criteria that should be considered separately and optimized simultaneously. Also, it is important to provide an algorithm that can evaluate the resilience of a dynamic system. In this paper, a framework for Dynamic Multi-Objective Constraint Optimization Problem (DMO-COP) is introduced and two solution criteria for solving this problem are provided, namely resistance and functionality, which are properties of interest underlying the resilience for DMO-COPs. Also, as an initial step toward developing an efficient algorithm for finding resilient solutions of a DMO-COP, an algorithm called Algorithm for Systems Resilience (ASR), which computes every resistant and functional solution for DMO-COPs, is presented and evaluated with different types of dynamical changes.

References

  1. Bruneau, M. (2003). A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities. In Earthquake Spectra, volume 19.
  2. Cabon, B., Givry, S., Lobjois, L., Schiex, T., and Warners, J. (1999). Radio link frequency assignment. Constraints, 4(1):79-89.
  3. Dechter, R. (2003). Constraint Processing. Morgan Kaufmann Publishers.
  4. Dechter, R. and Dechter, A. (1988). Belief maintenance in dynamic constraint networks. In AAAI, pages 37-42.
  5. Faltings, B. and Gonzalez, S. (2002). Open constraint satisfaction. In CP, pages 356-370.
  6. Holling, C. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4:1-23.
  7. Junker, U. (2006). Preference-based inconsistency proving: When the failure of the best is sufficient. In ECAI, pages 118-122.
  8. Longstaff, P. H., Armstrong, N. J., Perrin, K., Parker, W. M., and Hidek, M. A. (2010). Building resilient communities: A preliminary framework for assessment. Homeland Security Affairs, 6(3).
  9. Marinescu, R. (2010). Best-first vs. depth-first and/or search for multi-objective constraint optimization. In ICTAI, pages 439-446.
  10. Okimoto, T., Ribeiro, T., Clement, M., and Inoue, K. (2014). Modeling and algorithm for dynamic multiobjective weighted constraint satisfaction problem. In ICAART, pages 420-427.
  11. Perny, P. and Spanjaard, O. (2008). Near admissible algorithms for multiobjective search. In ECAI, pages 490-494.
  12. Rollon, E. and Larrosa, J. (2006). Bucket elimination for multiobjective optimization problems. Journal of Heuristics, 12(4-5):307-328.
  13. Rollon, E. and Larrosa, J. (2007). Multi-objective Russian doll search. In AAAI, pages 249-254.
  14. Sandholm, T. (1999). An algorithm for optimal winner determination in combinatorial auctions. In IJCAI, pages 542-547.
  15. Schiex, T., Fargier, H., and Verfaillie, G. (1995). Valued constraint satisfaction problems: Hard and easy problems. In IJCAI, pages 631-639.
  16. Schwind, N., Okimoto, T., Inoue, K., Chan, H., Ribeiro, T., Minami, K., and Maruyama, H. (2013). Systems resilience: a challenge problem for dynamic constraintbased agent systems. In AAMAS, pages 785-788.
  17. Verfaillie, G., Lemaˆitre, M., and Schiex, T. (1996). Russian doll search for solving constraint optimization problems. In AAAI/IAAI, Vol. 1, pages 181-187.
  18. Walker, B. H., Holling, C. S., Carpenter, S. C., and Kinzig, A. (2004). Resilience, adaptability and transformability. Ecology and Society, 9(2):5.
  19. Yeoh, W., Varakantham, P., Sun, X., and Koenig, S. (2011). Incremental DCOP search algorithms for solving dynamic DCOPs. In AAMAS, pages 1069-1070.
Download


Paper Citation


in Harvard Style

Clement M., Okimoto T., Schwind N. and Inoue K. (2015). Finding Resilient Solutions for Dynamic Multi-Objective Constraint Optimization Problems . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 509-516. DOI: 10.5220/0005276305090516


in Bibtex Style

@conference{icaart15,
author={Maxime Clement and Tenda Okimoto and Nicolas Schwind and Katsumi Inoue},
title={Finding Resilient Solutions for Dynamic Multi-Objective Constraint Optimization Problems},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2015},
pages={509-516},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005276305090516},
isbn={978-989-758-074-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Finding Resilient Solutions for Dynamic Multi-Objective Constraint Optimization Problems
SN - 978-989-758-074-1
AU - Clement M.
AU - Okimoto T.
AU - Schwind N.
AU - Inoue K.
PY - 2015
SP - 509
EP - 516
DO - 10.5220/0005276305090516