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Towards “Kiga-kiku” Services on Speculative Computation

  • Naoki Fukuta
  • Ken Satoh
  • Takahira Yamaguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5345)

Abstract

In this paper, we propose a concept for service called “kiga-kiku” service. In that, an agent proactively detect potential failures in providing a series of services for users, and prepare and execute follow-up plans for the failures automatically. The name of “kiga-kiku” is derived from a Japanese word meaning of proactive behavior to keep comfort of other people by using prediction of other people’s behaviors, wishes, and preferences with shared social context. We show that a certain kind of “kiga-kiku” service can be realized as a combination of inference capability about preparation of possible failures and execution of follow-up plans in an acceptable cost. We also present a case study about “kiga-kiku” service and we show it is possible to implement such mechanism by simply adding a “kiga-kiku” service agent as a front-end to the existing service systems in a reasonable development cost.

Keywords

MultiAgent System Service Composition Service Process Speculative Computation Association Rule Mining 
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. 1.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  2. 2.
    Ma, H., King, I., Lyu, M.R.: Effective missing data prediction for collaborative filtering. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR 2007), pp. 39–46. ACM, New York (2007)CrossRefGoogle Scholar
  3. 3.
    Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining — a general survey and comparison. SIGKDD Explorations Newsletter 2(1), 58–64 (2000)CrossRefGoogle Scholar
  4. 4.
    Ceglar, A., Roddick, J.F.: Association mining. ACM Computing Surveys 38(2), 5 (2006)CrossRefGoogle Scholar
  5. 5.
    Zhou, L., Yau, S.: Association rule and quantitative association rule mining among infrequent items. In: Proceedings of the 8th International Workshop on Multimedia data mining(MDM 2007), pp. 1–9. ACM Press, New York (2007)Google Scholar
  6. 6.
    Mutschler, B., Weber, B., Reichert, M.: Workflow management versus case handling: results from a controlled software experiment. In: Proceedings of the 2008 ACM symposium on Applied computing(SAC 2008), pp. 82–89 (2008)Google Scholar
  7. 7.
    Klein, M., Dellarocas, C.: A systematic repository of knowledge about handling exceptions in business processes. In: ASES Working Report ASES-WP-2000-2003. Center for Coordination Science, Massachusetts Institute of Technology (2000)Google Scholar
  8. 8.
    Klein, M., Rodriguez-Aquilar, J.A., Dellarocas, C.: Using domain-independent exception handling services to enable robust open multi-agent systems: The case of agent death. The Journal of Autonomous Agents and Multi-Agent Systems 7(1/2), 179–189 (2003)CrossRefGoogle Scholar
  9. 9.
    Gmach, D., Krompass, S., Scholz, A., Wimmer, M., Kemper, A.: Adaptive quality of service management for enterprise services. ACM Transactions on the Web 2(1), 1–46 (2008)CrossRefGoogle Scholar
  10. 10.
    Scerri, P., Pynadath, D.V., Tambe, M.: Towards adjustable autonomy for the real world. Journal of Artificial Intelligence Research 17 (2003)Google Scholar
  11. 11.
    Müller, J.P.: The Design of Intelligent Agents: A Layered Approach. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  12. 12.
    Martin, D., Brustein, M., Hobbs, J., Lassila, O., McDermott, D., McIlraith, S., Narayanan, S., Paolucci, M., Parsia, B., Payne, T., Sirin, E., Srinivasan, N., Sycara, K.: OWL-S: Semantic markup for web services. W3C Member Submission, November 22 (2004)Google Scholar
  13. 13.
    de Brujin, J., Bussler, C., Dommingue, J., Fensel, D., Hepp, M., Keller, U., Kifer, M., KonigRies, B., Kopecky, J., Lara, R., Oren, H.L.E., Polleres, A., Roman, D., Scicluna, J., Stollberg, M.: Web service modeling ontology (WSMO). W3C Member Submission, June 3 (2005)Google Scholar
  14. 14.
    Zeng, L., Benatallah, B., Ngu, A.H., Dumas, M., Kalagnanam, J., Chang, H.: Qos-aware middleware for web services composition. IEEE Transactions on Software Engineering 30(5), 311–327 (2004)CrossRefGoogle Scholar
  15. 15.
    Hayashi, H., Cho, K., Ohsuga, A.: Speculative computation and action execution in multi-agent systems. In: Proc. of Computational Logic in Multiagent Systems (CLIMA-III), pp. 136–148 (2002)Google Scholar
  16. 16.
    Barish, G., Knoblock, C.A.: Speculative execution for information gathering plans. In: Proc. of AIPS 2002, pp. 184–193 (2002)Google Scholar
  17. 17.
    Satoh, K., Inoue, K., Iwanuma, K., Sakama, C.: Speculative computation by abduction under incomplete communication environments. In: Proc. of the 1st International Conference on MultiAgent Systems(ICMAS 2000), pp. 263–270 (2000)Google Scholar
  18. 18.
    Satoh, K., Yamamoto, K.: Speculative computation with multi-agent belief revision. In: Proc. of the 1st International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2002), pp. 897–904 (2002)Google Scholar
  19. 19.
    Satoh, K., Codognet, P., Hosobe, H.: Speculative constraint processing in multi-agent systems. In: Lee, J.-H., Barley, M.W. (eds.) PRIMA 2003. LNCS (LNAI), vol. 2891, pp. 133–144. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  20. 20.
    Satoh, K.: Speculative computation and abduction fro an autonomous agent. IEICE Transactions on Information and Systems E88-D(9), 2031–2038 (2005)CrossRefGoogle Scholar
  21. 21.
    Takabayashi, Y., Niwa, H., Taneda, M., Fukuta, N., Yamaguchi, T.: Managing many web service compositions by task decomposition and service quality evaluation. In: Reimer, U., Karagiannis, D. (eds.) PAKM 2006. LNCS (LNAI), vol. 4333, pp. 291–302. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. 22.
    Minegishi, S., Fukuta, N., Iijima, T., Yamaguchi, T.: Acquiring and refining class hierarchy design of web application integration software. In: Karagiannis, D., Reimer, U. (eds.) PAKM 2004. LNCS (LNAI), vol. 3336, pp. 463–474. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Fukuta, N., Osawa, T., Iijima, T., Yamaguchi, T.: Semantic service integration support for web portal. In: Proc. of IEEE/WIC/ACM International Conference on Web Intelligence (WI 2005), pp. 161–164 (2005)Google Scholar
  24. 24.
    Havens, B., Goebel, R., Berger, J., Proulx, R.: A constraint optimization framework for multi-agent anytime scheduling. In: Proc. of AAAI 1999 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Naoki Fukuta
    • 1
  • Ken Satoh
    • 2
  • Takahira Yamaguchi
    • 3
  1. 1.Shizuoka UniversityHamamatsu ShizuokaJapan
  2. 2.National Institute of Informatics, ChiyodakuTokyoJapan
  3. 3.Keio University, HiyoshiKanagawaJapan

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