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ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 714245, 9 pages
http://dx.doi.org/10.5402/2012/714245
Research Article

Planning for Multiple Preferences versus Planning with No Preference

Department of Computer Science, Utah State University, 4205 Old Main Hill, Logan, UT 84322, USA

Received 26 July 2011; Accepted 27 August 2011

Academic Editors: P. Brezillon, K. W. Chau, and L. Utkin

Copyright © 2012 Daniel Bryce. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Haddawy and S. Hanks, “Representations for decision-theoretic planning: utility functions for deadline goals,” in Proceedings of the International Conference (KR '92), pp. 71–82, 1992.
  2. C. T. Cheng, C. P. Ou, and K. W. Chau, “Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration,” Journal of Hydrology, vol. 268, no. 1–4, pp. 72–86, 2002. View at Publisher · View at Google Scholar
  3. N. Muttil and K. W. Chau, “Neural network and genetic programming for modelling coastal algal blooms,” International Journal of Environment and Pollution, vol. 28, no. 3-4, pp. 223–238, 2006. View at Publisher · View at Google Scholar
  4. J.-X. Xie, C.-T. Cheng, K.-W. Chau, and Y.-Z. Pei, “A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity,” International Journal of Environment and Pollution, vol. 28, no. 3-4, pp. 364–381, 2006. View at Publisher · View at Google Scholar
  5. J. Y. Lin, C. T. Cheng, and K. W. Chau, “Using support vector machines for long-term discharge prediction,” Hydrological Sciences Journal, vol. 51, no. 4, pp. 599–612, 2006. View at Publisher · View at Google Scholar
  6. J. Zhang and K. W. Chau, “Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization,” Journal of Universal Computer Science, vol. 15, no. 4, pp. 840–858, 2009.
  7. C. L. Wu, K. W. Chau, and Y. S. Li, “Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques,” Water Resources Research, vol. 45, no. 8, Article ID W08432, 2009. View at Publisher · View at Google Scholar
  8. B. S. Stewart and C. C. White, “Multiobjective A,” Journal of the ACM, vol. 38, no. 4, pp. 775–814, 1991.
  9. M. Van Den Briel, R. Sanchez, M. B. Do, and S. Kambhampati, “Effective approaches for partial satisfaction (over-subscription) planning,” in Proceedings of the 16th Innovative Applications of Artificial Intelligence Conference (IAAI '04), pp. 562–569, July 2004.
  10. J. Wu and S. Azarm, “Metrics for quality assessment of a multiobjective design optimization solution set,” Journal of Mechanical Design, vol. 123, no. 1, pp. 18–25, 2001.
  11. L. Mandow and J. L. Perez de la Cruz, “A new approach to multiobjective A search,” in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '05), pp. 218–223, 2005.
  12. K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, Wiley-Interscience Series in Systems and Optimization, John Wiley & Sons, Chichester, UK, 2001.
  13. J. Rintanen, “Expressive equivalence of formalisms for planning with sensing,” in Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS '03), pp. 185–194, 2003.
  14. H. L. S. Younes and M. L. Littman, “PPDDL1.0: an extension to PDDL for expressing planning domains with probabilistic effects,” Tech. Rep. CMU-CS-04-167, Carnegie Mellon University, Pittsburgh, Pa, USA, 2004.
  15. D. Bryce, S. Kambhampati, and D. E. Smith, “Sequential Monte Carlo in reachability heuristics for probabilistic planning,” Artificial Intelligence, vol. 172, no. 6-7, pp. 685–715, 2008. View at Publisher · View at Google Scholar
  16. C. Domshlak and J. Hoffmann, “Fast probabilistic planning through weighted model counting,” in Proceedings of the 16th International Conference on Automated Planning and Scheduling (ICAPS '06), pp. 243–252, June 2006.
  17. N. Hyafil and F. Bacchus, “Utilizing structured representations and CSPs in conformant probabilistic planning,” in Proceedings of the European Conference on Artificial Intelligence (ECAI '04), pp. 1033–1034, 2004.
  18. N. Kushmerick, S. Hanks, and D. Weld, “Algorithm for probabilistic least-commitment planning,” in Proceedings of the 12th National Conference on Artificial Intelligence(AAAI '94), pp. 1073–1078, August 1994.
  19. S. M. Majercik and M. L. Littman, “MAXPLAN: a new approach to probabilistic planning,” in Proceedings of the International Conference on AI Planning Systems (AIPS '98), pp. 86–93, 1998.
  20. D. Aberdeen, S. Thiébaux, and L. Zhang, “Decision-theoretic military operations planning,” in Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS '04), pp. 402–411, June 2004.
  21. L. Refanidis and I. Vlahavas, “The MO-GRT system: heuristic planning with multiple criteria,” in Proceedings of the Workshop on Planning and Scheduling with Multiple Criteria, 2002.
  22. M. B. Do and S. Kambhampati, “Sapa: a multi-objective metric temporal planner,” Journal of Artificial Intelligence Research, vol. 20, pp. 155–194, 2003.
  23. B. Srivastava, T. A. Nguyen, A. Gerevini, S. Kambhampati, M. B. Do, and I. Serina, “Domain independent approaches for finding diverse plans,” in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '07), pp. 2016–2022, 2007.