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Context-Mediated Behavior

  • Roy M. Turner
Chapter

Abstract

Context-mediated behavior (CMB) is an approach to giving intelligent agents the ability to recognize their context at all times and to behave appropriately for it. It is based on the idea that contexts—classes of situations—should be represented explicitly as first-class objects. These representations (contextual schemas) are then retrieved based on a diagnostic process of context assessment. Contextual schemas contain descriptive knowledge about the context, including predicted features and context-dependent meaning of concepts. They also include prescriptive features that tell the agent how to behave in the context. This approach has been implemented in several systems, including an intelligent controller for autonomous underwater vehicles (AUVs), and the author is now exploring distributing the process in multiagent systems.

Keywords

Membership Function Multiagent System Autonomous Underwater Vehicle Context Manager Performance Element 
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.

Notes

Acknowledgements

The author thanks the members of the Maine Software Agents and AI Laboratory (MaineSAIL) for their work on projects described here. This work has been supported by ONR grants N000—14–00–1–00–614, N0001–14–98–1–0648, and N0001–14–96–1–5009, and NSF grant BES–9696044.

References

  1. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, New York (2011)CrossRefGoogle Scholar
  2. Akman, V., Surav, M.: Contexts, oracles, and relevance. In: AAAI Fall Symposium on Formalizing Context, AAAI Technical Report Series, No. FS-95-02, pp. 23–30. AAAI Press, Menlo Park (1995)Google Scholar
  3. Albert, E., Turner, E.H., Turner, R.M.: Appropriate commitment planning for AUV control. In: Proceedings of the 2007 International Symposium on Unmanned Untethered Submersible Technology (UUST'07). Durham, NH (2007)Google Scholar
  4. Arritt, R.P., Turner, R.M.: Context-specific weights for a neural network. In: Proceedings of the Fourth International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT'03), Stanford, CA, pp. 29–39. Springer, New York (2003)Google Scholar
  5. Barrett, G.C., Gonzalez, A.J.: Effective agent collaboration through improved communication by means of contextual reasoning. Int. J. Intell. Syst. 26(2), 129–157 (2010)CrossRefGoogle Scholar
  6. Brézillon, J., Brézillon, P.: Context modeling: context as a dressing of focus. In: Proceedings of the Sixth International and Interdisciplinary Conference on Modeling and Using Context. Springer, Berlin (2007)Google Scholar
  7. Brézillon, P., Gentile, C., Saker, I., Secron, M.: SART: a system for supporting operators with contextual knowledge. In: Proceedings of the 1997 International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT–97), Rio de Janerio, pp. 209–222 (1997)Google Scholar
  8. Brézillon , P., Pasquier, L., Pomerol, J.C.: Reasoning with contextual graphs. Eur. J. Operat. Res. 136(2), 290–298 (2002)CrossRefMATHGoogle Scholar
  9. Chandrasekaran, B., Gomez, F., Mittal, S., Smith, J.: An approach to medical diagnosis based on conceptual structures. In: Proceedings of the Sixth International Joint Conference on Artificial Intelligence. Stanford, CA (1979)Google Scholar
  10. Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)CrossRefGoogle Scholar
  11. Feltovich, P.J., Johnson, P.E., Moller, J.A., Swanson, D.B.: LCS: the role and development of medical knowledge and diagnostic expertise. In: Clancey, W.J., Shortliffe, E.H. (eds.) Readings in Medical Artificial Intelligence, pp. 275–319. Addison–Wesley, Reading (1984)Google Scholar
  12. Giunchiglia, F.: Contextual reasoning. Epistemologia (special issue on I Linguaggi e le Macchine) 16, 345–364 (1993)Google Scholar
  13. Glass, A.N., Holyoak, K.J.: Cognition, 2nd edn. Random House, New York (1986)Google Scholar
  14. Gonzalez, A.J., Brézillon, P.: Comparing two context-driven approaches for representation of human tactical behavior. Knowl. Eng. Rev. 23(3), 29–5 (2008)CrossRefGoogle Scholar
  15. Gonzalez, A.J., Stensrud, B.S., Barrett, G.: Formalizing context-based reasoning: A modeling paradigm for representing tactical human behavior. Int. J. Intell. Syst. 23(7), 822–847 (2008)CrossRefGoogle Scholar
  16. Guha, R.: Contexts: a formalization and some applications. Ph.D. thesis, Stanford University (1991)Google Scholar
  17. Kolodner, J.L.: Retrieval and Organizational Strategies in Conceptual Memory. Lawrence Erlbaum Associates, Hillsdale (1984)Google Scholar
  18. Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufman, San Mateo (1993)Google Scholar
  19. Lawton, J.H., Turner, R.M., Turner, E.H.: A unified long-term memory system. In: Proceedings of the International Conference on Case-Based Reasoning (ICCBR'99). Monastery Seeon, Munich, Germany (1999)Google Scholar
  20. Leake, D., Maguitman, A., Reichherzer, T.: Exploiting rich context: an incremental approach to context-based Web search. In: International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT'05, pp. 254–267. Springer, Berlin (2005)Google Scholar
  21. Mantovani, G.: Social context in HCI: a new framework for mental models, cooperation, and communication. Cogn. Sci. 20, 237–269 (1996)CrossRefGoogle Scholar
  22. McCarthy, J.: Notes on formalizing contexts. In: Bajcsy, R. (ed.) Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 555–560. Morgan Kaufmann, San Mateo (1993)Google Scholar
  23. McCarthy, J.: A logical AI approach to context (1996).http://steam.stanford.edu/jmc/logical.pdf. Accessed 14 Oct 2014.
  24. McCarthy, J., Buvac, S., Costello, T., Fikes, R., Genesereth, M., Giunchiglia, F.: Formalizing context (expanded notes), Technical Report. Stanford University, Stanford, CA (1995)Google Scholar
  25. Miller, R.A., Pople, H.E., Myers, J.D.: INTERNIST–1, an experimental computer-based diagnostic consultant for general internal medicine. New Engl. J. Med. 307, 468–476 (1982)CrossRefGoogle Scholar
  26. Reggia, J.A., Nau, D.S., Peng, Y.: A formal model of diagnostic inference. I. Problem formulation and decomposition. Inf. Sci. 37, 227–256 (1985)CrossRefMATHGoogle Scholar
  27. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2003)Google Scholar
  28. Schank, R.C.: Dynamic Memory. Cambridge University Press, New York (1982)Google Scholar
  29. Tahir, H., Brézillon, P.: Contextual graphs platform as a basis for designing a context-based intelligent assistant system. In: Akman, V., Bouquet, P., Thomason, R., Young, R.A. (Eds.)Modeling and Using Context, pp. 259–273. Springer, Berlin (2013)CrossRefGoogle Scholar
  30. Turner, R.M.: Adaptive Reasoning for Real-World Problems: A Schema-Based Approach. Lawrence Erlbaum Associates, Hillsdale (1994)Google Scholar
  31. Turner, R.M.: Intelligent control of autonomous underwater vehicles: the Orca project. In: Proceedings of the 1995 IEEE International Conference on Systems, Man, and Cybernetics. Vancouver, Canada (1995)Google Scholar
  32. Turner, R.M.: Determining the context-dependent meaning of fuzzy subsets. In: Proceedings of the 1997 International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-97), Rio de Janeiro (1997)Google Scholar
  33. Turner, R.M.: Context-mediated behavior for intelligent agents. Int. J. Hum. Comput. Stud. 48(3), 307–330 (1998)CrossRefGoogle Scholar
  34. Turner, R.M., Turner, E.H.: A two-level, protocol-based approach to controlling autonomous oceanographic sampling networks. IEEE J. Oceanic Eng. 26(4), 654–666 (2001)CrossRefGoogle Scholar
  35. Turner, R.M., Rode, S., Gagne, D.: Distributed, context-based organization and reorganization of multi-AUV systems. J. Unmanned Syst. Technol. (JUST) 2(1), 1–9 (2014)CrossRefGoogle Scholar
  36. van Wissen, A., Kamphorst, B., van Eijk, R.: A constraint-based approach to context. In: Brézillon, P., Blackburn, P., Dapoigny, R. (eds.) Modeling and Using Context, pp. 171–184. Springer, Berlin (2013)CrossRefGoogle Scholar
  37. Whitsel, L.T.: A context-based approach to detecting miscreant agent behavior in open multagent systems. Ph.D. thesis, School of Computing and Information Science, University of Maine, 346 Boardman Hall, University of Maine, Orono, ME (2013)Google Scholar
  38. Whitsel, L., Turner, R.M.: A context-based approach to detecting miscreant behavior and collusion in open multiagent systems. In: Proceedings of the Seventh International and Interdisciplinary Conference on Modeling and Using Context CONTEXT'11, Karlruhe, Germany (2011)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Computing and Information ScienceUniversity of MaineOronoUSA

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