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Bio-informatic activity modeling for human-artifacts symbiosis under resource boundedness

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Abstract

We investigate a biologically inspired design of an interface agent that is embedded inside human-artifact interactions rather than as an external observer, and has to work as an intelligent associate for a human user/operator in a time-critical situation like in an emergency. First, recent paradigmatic shifts of artifact design principles are discussed from an interdisciplinary viewpoint. Then, after the idea of Clancey’s activity modeling, we discuss the design principles of a situated interface agent. That is, different from the conventional supervisory agent’s task of seeking to optimize an isolated control task, such an agent has to be able to maintain its identity as an organism living within multiple contexts and looking inwards to consider the the nature of memory and perception, and looking outwards to consider the nature of social action with a human operator. Initially, our prior work using such a design principle is presented, and then decision-theoretic formulations of an interface agent’s activities are provided.

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References

  1. Wickens C (1994) Designing for situation awareness and trust in automation. Proceedings of IFAC Integrated Systems Engineering, Baden-Baden, Pergamon, Oxford, pp 77–82

    Google Scholar 

  2. Bainbridge L (1983) Ironies of automation. Automatica 19:775–779

    Article  Google Scholar 

  3. Billings CE (1991) Toward a human-centered aircraft automation philosophy. Int J Aviat Psychol 1–4:261–270

    Article  Google Scholar 

  4. Maes P, Kozierok R (1993) Learning interface agents. Proceedings of the Eleventh National Conference on Artificial Intelligence, pp 459–465

  5. Maes P (1994) Agents that reduce work and information overload. Commun ACM 37(7):30–40

    Article  Google Scholar 

  6. Varela FJ (1995) The re-enhancement of the concrete: some biological ingredients for a nouvelle cognitive science. In: Steels L, Books R (eds) The artificial life route to artificial intelligence: building embodied, situated agents. Erlbaum Assoc, Hillsdale, N.J. pp 11–22

    Google Scholar 

  7. Winograd T, Flores F (1986) Understanding computers and cognition: a new foundation for design, Ablex, Norwood

    MATH  Google Scholar 

  8. Lave J (1988) Cognition in practice: mind, mathematics and culture in everyday life. Cambridge University Press, New York

    Google Scholar 

  9. Garfinkel H (1967) What is ethnomethodology. In: Garfinkel H (ed) Studies in ethnomethodology. Polity Press, Englewood Cliffs

    Google Scholar 

  10. Suchman LA (1977) Plans and situated actions: the problem of human-machine com-munication. Cambridge University Press, New York

    Google Scholar 

  11. Geyer F (1995) The challenge of sociocybernetics. Kybernetics 24(4):6–32

    Article  MathSciNet  Google Scholar 

  12. Norman R (1993) Special issue: situated action. Cognit Sci 17:1–133

    Article  MathSciNet  Google Scholar 

  13. Clancey WJ (1997) The conceptual nature of knowledge, situations, and activity. In: Feltovich PJ, Ford KM, Hoffman RR (eds) Expertise in context. AAAI Press, Menlo Park, pp 247–291

    Google Scholar 

  14. De Jong KA, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 13:161–188

    Article  Google Scholar 

  15. Wnek J, Michalski RS (1994) Hypothesis-driven constructive induction in AQ17-HCI: a method and experiments. Mach Learn 14:139–168

    Article  MATH  Google Scholar 

  16. Janikow CZ (1993) A knowledge-intensive genetic algorithm for supervised learning. Mach Learn 13:189–228

    Article  Google Scholar 

  17. Fisher D (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2:139–172

    Google Scholar 

  18. Morris NM, Rouse WB (1985) The effects of type of knowledge upon human problem solving in a process control task, IEEE Trans Syst Man Cybernetics SMC-15-6:698–707

    Google Scholar 

  19. Sawaragi T, Tani N, Katai O (1999) Evolutional concept learning from observations through adaptive feature selection and GA-based feature discovery, to be appeared in J Intelligent Fuzzy Syst 7(3)

  20. Gibson JJ (1979) The ecological approach to visual perception. Houghton Mifflin, Boston

    Google Scholar 

  21. Howard RA, Matheson JE (1983) Influence diagrams. In: Howard RA, Matheson JE (eds) The principles and applications of decision analysis. Strategic Decision Group, Menlo Park

    Google Scholar 

  22. Sawaragi T, Iwai S, Katai O et al. (1994) Dynamic decision-model construction by conceptual clustering. Proceedings of the Second World Congress on Expert Systems, Lisbon, pp 376–384

  23. Horvits E (1991) Time-dependent utility and action under uncertainty. Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, Los Angels, pp 151–158

  24. Poh KL, Fehling MR, Horvitz EJ (1994) Dynamic construction and refinement of utility-based categorization model. IEEE Trans Syst Man Cybernetics 24:1653–1663

    Article  Google Scholar 

  25. Sawaragi T, Katai O (1997) Resource-bounded reasoning for interface agent realizing flexible human-machine collaboration. Proceedings of ROMAN’97, Sendai, pp 484–489

  26. Horvitz E, Barry M (1995) Display of information for time-critical decision making. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal

  27. Sheridan TB (1987) Supervisory control. In: Salvendy G (ed) Handbook of human factors. Wiley, New York, pp 1243–1263

    Google Scholar 

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Correspondence to Tetsuo Sawaragi.

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Sawaragi, T., Katai, O. Bio-informatic activity modeling for human-artifacts symbiosis under resource boundedness. Artif Life Robotics 3, 45–53 (1999). https://doi.org/10.1007/BF02481487

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  • DOI: https://doi.org/10.1007/BF02481487

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