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Context-Mediated Behavior

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Context in Computing

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.

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Notes

  1. 1.

    In some work related to CMB, a third type, predictive knowledge, was identified (Whitsel 2013); however this can be viewed as a subtype of descriptive knowledge.

  2. 2.

    Term thanks to D.R. Blidberg.

  3. 3.

    In the original dynamic memories, the internal nodes were memory organization packages (MOPs), of which c-schemas are one kind.

  4. 4.

    The process is more complex than this; see Kolodner (1984) or Lawton et al. (1999).

  5. 5.

    International and Interdisciplinary Conference on Modeling and Using Context.

References

  • 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)

    Chapter  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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)

    Article  Google Scholar 

  • BrĂ©zillon, J., BrĂ©zillon, 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 

  • BrĂ©zillon, 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 

  • BrĂ©zillon , P., Pasquier, L., Pomerol, J.C.: Reasoning with contextual graphs. Eur. J. Operat. Res. 136(2), 290–298 (2002)

    Article  MATH  Google Scholar 

  • 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 

  • Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)

    Article  Google Scholar 

  • 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 

  • Giunchiglia, F.: Contextual reasoning. Epistemologia (special issue on I Linguaggi e le Macchine) 16, 345–364 (1993)

    Google Scholar 

  • Glass, A.N., Holyoak, K.J.: Cognition, 2nd edn. Random House, New York (1986)

    Google Scholar 

  • Gonzalez, A.J., BrĂ©zillon, P.: Comparing two context-driven approaches for representation of human tactical behavior. Knowl. Eng. Rev. 23(3), 29–5 (2008)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Guha, R.: Contexts: a formalization and some applications. Ph.D. thesis, Stanford University (1991)

    Google Scholar 

  • Kolodner, J.L.: Retrieval and Organizational Strategies in Conceptual Memory. Lawrence Erlbaum Associates, Hillsdale (1984)

    Google Scholar 

  • Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufman, San Mateo (1993)

    Google Scholar 

  • 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 

  • 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 

  • Mantovani, G.: Social context in HCI: a new framework for mental models, cooperation, and communication. Cogn. Sci. 20, 237–269 (1996)

    Article  Google Scholar 

  • 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 

  • McCarthy, J.: A logical AI approach to context (1996).http://steam.stanford.edu/jmc/logical.pdf. Accessed 14 Oct 2014.

  • 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 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  MATH  Google Scholar 

  • Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2003)

    Google Scholar 

  • Schank, R.C.: Dynamic Memory. Cambridge University Press, New York (1982)

    Google Scholar 

  • Tahir, H., BrĂ©zillon, 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)

    Chapter  Google Scholar 

  • Turner, R.M.: Adaptive Reasoning for Real-World Problems: A Schema-Based Approach. Lawrence Erlbaum Associates, Hillsdale (1994)

    Google Scholar 

  • 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 

  • 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 

  • Turner, R.M.: Context-mediated behavior for intelligent agents. Int. J. Hum. Comput. Stud. 48(3), 307–330 (1998)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • van Wissen, A., Kamphorst, B., van Eijk, R.: A constraint-based approach to context. In: BrĂ©zillon, P., Blackburn, P., Dapoigny, R. (eds.) Modeling and Using Context, pp. 171–184. Springer, Berlin (2013)

    Chapter  Google Scholar 

  • 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 

  • 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 

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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.

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Authors and Affiliations

  1. School of Computing and Information Science, University of Maine, Orono, ME 04469, USA

    Roy M. Turner

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  1. Roy M. Turner
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Correspondence to Roy M. Turner .

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  1. University Pierre and Marie Curie (UPMC), Paris, France

    Patrick Brézillon

  2. University of Central Florida, Orlando, Florida, USA

    Avelino J. Gonzalez

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Turner, R. (2014). Context-Mediated Behavior. In: Brézillon, P., Gonzalez, A. (eds) Context in Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1887-4_32

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  • DOI: https://doi.org/10.1007/978-1-4939-1887-4_32

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