Advertisement

Robot Ethics, Value Systems and Decision Theoretic Behaviors

  • Tod S. Levitt

Abstract

Robot labor is desirable for many mundane tasks, yet there are also numerous potential robot services that are not currently commercially available, including road-based delivery; industry and residential cleaning; and building and grounds maintenance. Robots can be built that can physically perform the actions necessary to do these services, but the requisite robot vision capabilities are not adequate to perform these tasks safely and reliably in open, uncontrolled environments.

Keywords

Industrial Robot World State Robot Behavior Robot Action Robot Vision 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aleksander, I. (1983), Artificial Vision for Robots, New York: Chapman and Hall.CrossRefGoogle Scholar
  2. Aleliunas, R. (1990). “A Summary of a New Normative Theory of Probabilistic Logic”, in Uncertainty in Artificial Intelligence 4, R.D. Shachter, T.S. Levitt, L.N. Kanal and J.F. Lemmer, [Eds.], Elsevier Science Publishers B.V. (North-Holland).Google Scholar
  3. Automated Imaging Association, http://www.industry.net/aia.Google Scholar
  4. Basu, K.S. (1995). “Android Epistemology: An Essay on Interpretation and Intentionality”, in Android Epistemology, K.M. Ford, C. Glymour and P.J. Hayes [Eds.], MIT Press, pp. 123–140.Google Scholar
  5. Bernardo, J.M. & Smith, A.F.M. (1994). Bayesian Theory, John Wiley & Sons, Inc., pp. 13–104.Google Scholar
  6. Binford, T.O. & Levitt, T.S. (1993). “Model-Based Recognition of Objects in Complex Scenes”, Proc. ARPA Image Understanding Workshop, Morgan Kaufmann Publishers, San Mateo, California.Google Scholar
  7. Binford, T.O., Levitt, T.S. & Mann, W.B. (1989). “Bayesian Inference in Model-Based Machine Vision”, in Uncertainty in Artificial Intelligence 3, L.N. Kanal, T.S. Levitt, and J.F. Lemmer, [Eds.], Elsevier Science Publishers B.V. (North-Holland).Google Scholar
  8. Charles, J. (1996). “Rosie: a worker for the Nuclear Age”, IEEE Expert, February, pp. 82–83.Google Scholar
  9. Chrisley, R.L. (1995). “Taking Embodiment Seriously: Nonceptual Content and Robotics”, in Android Epistemology, K.M. Ford, C. Glymour and P.J. Hayes [Eds.], MIT Press, pp. 141–166.Google Scholar
  10. Colbaugh, R. & Seraji, H. (1994). “Adaptive Tracking Control of Manipulators: Theory and Experiments,” Proc. IEEE Intern. Conf. on Robotics and Automation, San Diego, California, Vol. 4, pp. 2992–2999.Google Scholar
  11. Cox, R. (1946). “Probability, frequency, and reasonable expectation”, American J. of Physics, 14, 1, pp. 1–13.CrossRefGoogle Scholar
  12. De Landa, M. (1991). War in the Age of Intelligent Machines, MIT Press, and p. 1.Google Scholar
  13. Dhillon, B.S. & Anude, O.C. (1993). “Robot Safety and Reliability: A Review”, Microelectronic Reliability, Vol. 33, No. 3, pp. 413–429.CrossRefGoogle Scholar
  14. Dubois, D. & Prade, H., (1989). “Representation and combination of uncertainty with belief functions and possibility measures”, Computational Intelligence, 4(4), pp. 244–264.Google Scholar
  15. Dubois, D. & Prade H. (1990). “The logical view of conditioning and its application to possibility and evidence theories”, Intl. J. of Approximate Reasoning, 4, pp. 23–46.CrossRefGoogle Scholar
  16. Edwards, W. (1977) “How to use multiattribute utility measurement for social decision making”, IEEE Trans. Systems, Man and Cybernetics, SMC-7, pp. 326–340.CrossRefGoogle Scholar
  17. Epstein, R.G. (1997). The Case of the Killer Robot, John Wiley and Sons, Inc.Google Scholar
  18. Fishburn, P.C. (1986). “The axioms of subjective probability”, Statistical Science, 1, pp. 335–358.CrossRefGoogle Scholar
  19. Fishburn, P.C. & Roberts, F.S. (1989). “Axioms for unique subjective probability on finite sets”, J. of Mathematical Psychology, 33, pp. 117–130.CrossRefGoogle Scholar
  20. Forester, T. & Morrison, P. (1995). Computer Ethics, MIT Press, pp. 202, 223.Google Scholar
  21. Franklin, J.E., Carmody, C.L., Keller, K. Levitt, T.S. & Buteau, B.L. (1988). “Expert System Technology for the Military: Selected Samples”, Proceedings of the IEEE.Google Scholar
  22. Gips, J. (1995). “Towards the Ethical Robot”, in Android Epistemology, K.M. Ford, C. Glymour and PJ. Hayes [Eds.], MIT Press, p. 247Google Scholar
  23. Grimson, W.E.L. (1990). Object Recognition By Computer, Mass: MIT Press, pp. 477–484.Google Scholar
  24. Horvitz, E. & Rutledge, G. (1991). “Time-Dependent Utility and Action Under Uncertainty”, Proc. Uncertainty in Artificial Intelligence, Morgan-Kaufmann Publishers, San Mateo, California, pp. 151–158.Google Scholar
  25. Horvitz, E.J., Heckerman, D.E. & Langlotz, C.P. (1986). “A framework for comparing alternate formalisms for plausible reasoning”, Proceedings of AAAI, pp. 210–214.Google Scholar
  26. Howard, R. (1989). “Microrisks for medical decision analysis”, Intl. J. of Technology Assessment in Health Care, No. 5, pp. 357–370.Google Scholar
  27. Howson, C. & Urbach, P. (1993). Scientific Reasoning: The Bayesian Approach, Open Court Publishing Company.Google Scholar
  28. Jochem, T., Pomerleau, D., Kumar, B. and Armstrong, J. (1995) “PANS: A portable navigation platform”, 1995 IEEE Symposium on Intelligent Vehicles, IEEE Press.Google Scholar
  29. Kallman, E.A. & Grillo, J.P. (1996). Ethical Decision Making and Information Technology, McGraw Hill Co.Google Scholar
  30. Klein, D.A. (1994). Decision-Analytic Intelligent Systems — Automated Explanation and Knowledge Acquisition, Lawrence Erlbaum Associates, Publishers, Hillsdale, New Jersey.Google Scholar
  31. Lemonick, M.D. (1994) “Dante Tours the Inferno”, Time Magazine, Vol. 144, No. 7.Google Scholar
  32. Levitt, T.S., Binford, T.O, & Ettinger, G.J. (1990). “Utility-Based Control for Computer Vision”, in Uncertainty in Artificial Intelligence 4, R.D. Shachter, T.S. Levitt, L.N. Kanal and J.F. Lemmer, [Eds.], Elsevier Science Publishers B.V. (North-Holland).Google Scholar
  33. Levitt, T.S., Winter, C.L. Turner, C.J., Chestek, R.A., Ettinger, G.J. & Sayre, S.M. (1995). “Bayesian Inference-Based Fusion of Radar Imagery, Military Forces and Tactical Terrain Models in the Image Exploitation System/Balanced Technology Initiative”, Intl. J. of Human-Computer Studies, No. 42.Google Scholar
  34. Lindley, D.V., (1982). “Scoring rules and the inevitability of probability”, Intl. Statistical Review, 50, pp. 1–26.CrossRefGoogle Scholar
  35. Meng, M. & Kak, A.C. (1993). “Mobile Robot Navigation using Neural Networks and Nonmetrical Environment Models”, IEEE Control Systems, October, pp. 30–39.Google Scholar
  36. OSHA, Occupational Safety and Health Administration, U.S. Department of Labor, http://www.osha.gov.Google Scholar
  37. Parnas, D.L. (1987). “Computers in Weapons: The Limits of Confidence”, in Computers in Battle — Will They Work?, D. Bellin and G. Chapman, [Eds.], Harcourt, Brace, Jovanovich, Publishers, New York, p. 227.Google Scholar
  38. Rimey, R.D. (1992). “Where to Look Next Using a Bayes Net: An Overview”, in Proc. DARPA Image Understanding Workshop, Morgan-Kaufmann Publishers, Inc., San Mateo, California, pp. 927–932.Google Scholar
  39. Robotic Institute of America, http://BizServe.com/ria.Google Scholar
  40. Robotic Institute of America (1989). “American National Standard for Automated Vision Systems — Performance Test — Measures of Relative Position of Target Features in Two Dimensional Space”, ANSI/AVA-A15.05/1-1989.Google Scholar
  41. “Robotics in Surgery” (1995). special issue of IEEE Engineering in Medicine and Biology, 14, 3.Google Scholar
  42. Saffiotti, A., Ruspini, E.H. & Konolige, K. (1997). “Using Fuzzy Logic for Mobile Robot Control”, in D. Dubois, H. Prade and H.J. Zimmermann, [Eds.], Handbook of Fuzzy Sets and Possibility Theory, Kluwer Academic Publishers.Google Scholar
  43. Savage, L.J. (1972). The Foundations of Statistics, Dover Publishers, New York, p. 32.Google Scholar
  44. Slovic, P., Fischhoff, B., & Lichtenstein, S. (1985). “Characterizing Perceived Risk” in Perilous Progress: Managing the Hazards in Technology, R.W. Kates, C. Hohenemser, and J.X. Kasperson, [Eds.], Westview Press, Boulder, Colorado, pp. 91–125.Google Scholar
  45. Snow, P. (1992). “Intuitions about Ordered Beliefs Leading to Probabilistic Models”, Uncertainty in Artificial Intelligence 3, D. Dubois, M.P. Wellman, B. D’Ambrosio and P. Smets, [Eds.], Morgan Kaufmann Publishers, San Mateo, California.Google Scholar
  46. Stone, H.W. & Edmonds, G. (1992). “HAZBOT: A Hazardous Materials Emergency Response Mobile Robot”, Proc. IEEE Intl. Conf. on Robotics and Automation, Nice, France, pp. 67–73.Google Scholar
  47. Taylor, R. H., Lavallee, S., Burdea, G.C. & Mosges, R. (1995). [Eds.], Computer-Integrated Surgery: Technology and Clinical Applications, MIT Press.Google Scholar
  48. Tversky, A. & Kahneman, D. (1982). “Judgment under uncertainty: Heuristics and biases”, in Judgment under uncertainty: Heuristics and biases, D. Kahneman, P. Slovic and A. Tversky, [Eds.], Cambridge University Press, pp. 1–20.Google Scholar
  49. Umar Kahn, A.F. (1995). “The Ethics of Autonomous Learning Systems”, in Android Epistemology, K.M. Ford, C. Glymour and P.J. Hayes [Eds.], MIT Press, pp. 259–261.Google Scholar
  50. Underwriters Laboratories, (1995). “Standard for Industrial Robots and Robotic Equipment”, UL Standard UL1740, in The Code Authority, Vol. 3. No. 3., http://www.ul.com/auth/tca/auth/tca.
  51. U.S. Department of Labor (1997). “Industrial Robots and Robot System Safety”, in OSHA Technical Manual, Section III, Chapter 4, http://www.osha-slc.gov/cgi-bin/mwais.pl/cgi-bin/mwais.pl.
  52. U.S. Department of Labor (1987). “Guidelines For Robotics Safety”, OSHA Directive Number PUB 8-1.3, http://www.osha-slc.gov/OshDoc/Directive_data/DIRECT_19870921.html.
  53. Von Neumann, J. & Morgenstern, O. (1953). The Theory of Games and Economic Behavior, Princeton University Press, Princeton, New Jersey, p. 28.Google Scholar
  54. Waldrop, M.M. (1987). “A Question of Responsibility”, AI Magazine, Spring, p. 38Google Scholar
  55. Wellman, M. P. (1990). Formulation of Tradeoffs in Planning Under Uncertainty, Pitman Publishing Co., London.Google Scholar
  56. Zhang, M. & Buehler, M. (1994). “Sensor-Based Online Trajectory Generation for Smoothly Grasping Moving Objects,” In Proc. IEEE Intl. Symp. Intelligent Control, Columbus, OH, pp. 141–146.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Tod S. Levitt
    • 1
  1. 1.Information Extraction & Transport, Inc. and Stanford UniversityUSA

Personalised recommendations