Abstract
Artificial intelligence (AI) applications are often faulted for their brittleness and slowness. In this paper, we argue that both of these problems can be ameliorated if the AI program is context-sensitive, making use of knowledge about the context it is in to guide its perception, understanding, and action. We describe an approach to this problem, context-mediated behavior (CMB). CMB uses contextual schemas (c-schemas) to explicitly represent contexts. Features of the context are used to find the appropriate c-schemas, whose knowledge then guides all aspects of behavior.
This work was funded in part by the United States National Science Foundation under grant BES-9696044.
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References
Aikins, J.S.: A representation scheme using both frames and rules. In: Buchanan, B.G., and Shortliffe, E.H. (eds.): Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley Publishing Company, Reading, Massachusetts (1984) 424–440
Feltovich, P.J., Johnson, P.E., Moller, J.A., and Swanson D.B.: LCS: The role and development of medical knowledge and diagnostic expertise. In: Clancey, W.J., and Shortliffe, E.H. (eds): Readings in Medical Artificial Intelligence. Addison-Wesley Publishing Company, Reading, Massachusetts (1984) 275–319
Guha, R., and Lenat, D.B.: Cyc: A midterm report. AI Magazine 11 (3) (1990) 32–59
Miller, R.A., Pople, H.E., Jr., and Myers, J.D.: INTERNIST-1, an experimental computer-based diagnostic consultant for general internal medicine. New England Journal of Medicine 307 (1982) 468–476
Turner, R.M.: Context-mediated behavior for intelligent agents. International Journal of Human-Computer Studies (in press)
Turner, R.M.: When reactive planning is not enough: Using contextual schemas to react appropriately to environmental change. In: Proceedings of the Eleventh Annual Conference of the Cognitive Science Society, Detroit, MI (1989) 940–947
Turner, R.M.: Adaptive Reasoning for Real-World Problems: A Schema-Based Approach. Lawrence Erlbaum Associates, Hillsdale, NJ (1994)
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)
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)
Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Communications of the ACM 37 (3) (1994) 77–84
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Turner, R.M. (1998). Context-mediated behavior for AI applications. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_785
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DOI: https://doi.org/10.1007/3-540-64582-9_785
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