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Anytime Contract Search

Abstract

Heuristic search is a fundamental problem solving paradigm in artificial intelligence. We address the problem of developing heuristic search algorithms where intermediate results are sought at intervals of time which may or may not be known apriori. In this paper, we propose an efficient anytime algorithm called Anytime Contract Search (based on the contract search framework) which incrementally explores the state-space with the given contracts (intervals of reporting). The algorithm works without restarting and dynamically adapts for the next iteration based on the current contract and the currently explored state-space. The proposed method is complete on bounded graphs. Experimental results with different contract sequences on the Sliding-tile Puzzle Problem and the Travelling Salesperson Problem (TSP) show that Anytime Contract Search outperforms some of the state-of-the art anytime search algorithms that are oblivious to the given contracts. Also, the non-parametric version of the proposed algorithm which is oblivious of the reporting intervals (making it an anytime algorithm) performs well compared to many available schemes.

Keywords

  • Search Contract
  • Traveling Salesperson Problem (TSP)
  • Contraction Series
  • Depth-first Branch-and-bound (DFBB)
  • Testcase

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Aine, S., Chakrabarti, P.P., Kumar, R.: AWA* - A window constrained anytime heuristic search algorithm. In: M.M. Veloso (ed.) IJCAI, pp. 2250–2255 (2007).

    Google Scholar 

  2. Aine, S., Chakrabarti, P.P., Kumar, R.: Contract search: Heuristic search under node expansion constraints. In: ECAI, pp. 733–738 (2010).

    Google Scholar 

  3. Aine, S., Chakrabarti, P.P., Kumar, R.: Heuristic search under contract. Computational Intelligence 26(4), 386–419 (2010).

    Google Scholar 

  4. Dean, T., Boddy, M.: An analysis of time-dependent planning. In: Proceedings of 6th National Conference on Artificial Intelligence (AAAI 88), pp. 49–54. AAAI Press, St. Paul, MN (1988).

    Google Scholar 

  5. Dionne, A.J., Thayer, J.T., Ruml, W.: Deadline-aware search using on-line measures of behavior. In: SOCS (2011).

    Google Scholar 

  6. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968).

    Google Scholar 

  7. Hiraishi, H., Ohwada, H., Mizoguchi, F.: Time-constrained heuristic search for practical route finding. In: H.Y. Lee, H. Motoda (eds.) PRICAI98: Topics in Artificial Intelligence,Lecture Notes in Computer Science, vol. 1531, pp. 389–398. Springer, Berlin Heidelberg (1998).

    Google Scholar 

  8. Korf, R.E., Felner, A.: Disjoint pattern database heuristics. Artif. Intell. 134(1–2), 9–22 (2002).

    Google Scholar 

  9. Lawler, E.L., Wood, D.E.: Branch-and-bound methods: A survey. Operational Research 14(4), 699–719 (1966).

    Google Scholar 

  10. Likhachev, M., Gordon, G.J., Thrun, S.: ARA*: Anytime A* with provable bounds on sub-optimality. In: Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA (2004).

    Google Scholar 

  11. Lowerre, B.: The Harpy Speech Recognition System. PhD thesis, Carnegie Mellon University (1976).

    Google Scholar 

  12. Norvig, P.: Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1992).

    Google Scholar 

  13. Reinelt, G.: TSPLIB - A traveling salesman problem library. ORSA Journal on Computing 3, 376–384 (1991).

    Google Scholar 

  14. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2 edn. Pearson, Education (2003).

    Google Scholar 

  15. Sturtevant, N.R., Felner, A., Likhachev, M., Ruml, W.: Heuristic search comes of age. In: AAAI (2012).

    Google Scholar 

  16. van den Berg, J., Shah, R., Huang, A., Goldberg, K.Y.: Anytime nonparametric A*. In: AAAI (2011).

    Google Scholar 

  17. Zhou, R., Hansen, E.A.: Beam-stack search: Integrating backtracking with beam search. In: Proceedings of the 15th International Conference on Automated Planning and Scheduling (ICAPS-05), pp. 90–98. Monterey, CA (2005).

    Google Scholar 

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Correspondence to Sunandita Patra .

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Patra, S., Vadlamudi, S.G., Chakrabarti, P.P. (2013). Anytime Contract Search. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-02621-3_10

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  • Online ISBN: 978-3-319-02621-3

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