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Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 21))

The traveling salesman problem (TSP) is usually studied on a Euclidean plane. When obstacles are placed on the plane, the distances are no longer Euclidean, but they still satisfy the metric axioms. Three experiments are reported in which subjects were tested on the TSP and on the shortest-path problem with obstacles. When the obstacles were simple, and they did not change the global structure of the problem, the subjects were able to produce near-optimal solutions, but the complexity of the mental mechanisms was higher than in the case of the Euclidean TSP. When obstacles were complex and changed the problem's global structure, the solutions were no longer near-optimal. Several computational models are proposed that can account for the psychophysical results.

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References

  1. S. M. Graham, A. Joshi, and Z. Pizlo. The traveling salesman problem: A hierarchical model. Memory and Cognition, 28:1191–1204, 2000.

    Google Scholar 

  2. G. Gutin and A. P. Punnen. The Traveling Salesman Problem and Its Variations. Kluwer, Boston, 2002.

    MATH  Google Scholar 

  3. C. M. Harris. On the optimal control of behavior: A stochastic perspective. Journal of Neuroscience Methods, 83:73–88, 1998.

    Article  Google Scholar 

  4. Human problem solving difficult optimization tasks workshop. http://psych.purdue.edu/tsp/workshop/downloads.html. Last accessed January 2008.

  5. J. M. Jolion and A. Rosenfeld. A Pyramid Framework for Early Vision. Kluwer, Dordrecht, 1994.

    Google Scholar 

  6. D. C. Knill and W. Richards. Perception as Bayesian Inference. Cambridge University Press, Cambridge, UK, 1996.

    MATH  Google Scholar 

  7. K. Koffka. Principles of Gestalt Psychology. Harcourt, Brace, New York, 1935.

    Google Scholar 

  8. E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, and D. B. Shmoys. The Traveling Salesman Problem. Wiley, New York, 1985.

    MATH  Google Scholar 

  9. M. Li and P. Vitanyi. An Introduction to Kolmogorov Complexity and Its Applications. Springer, New York, 1997.

    MATH  Google Scholar 

  10. R. M. Nosofsky. Attention, similarity and the identification-categorization relationship. Journal of Experimental Psychology: General, 115:39–57, 1986.

    Article  Google Scholar 

  11. M. A. Pitt, J. Myung, and S. Zhang. Toward a method of selecting among computational models of cognition. Psychological Review, 109:472–491, 2002.

    Article  Google Scholar 

  12. Z. Pizlo. Perception viewed as an inverse problem. Vision Research, 41:3145–3161, 2001.

    Article  Google Scholar 

  13. Z. Pizlo, A. Rosenfeld, and J. Epelboim. An exponential pyramid model of the time-course of size processing. Vision Research, 33:1089–1107, 1995.

    Article  Google Scholar 

  14. Z. Pizlo, M. Salach-Golyska, and A. Rosenfeld. Curve detection in a noisy image. Vision Research, 37:1217–1241, 1997.

    Article  Google Scholar 

  15. Z. Pizlo, E. Stefanov, J. Saalwaechter, Z. Li, Y. Haxhimusa, and W. G. Kropatsch. Traveling salesman problem: A foveating algorithm. Journal of Problem Solving, 1:83–101, 2006.

    Google Scholar 

  16. W. V. Quine. Word and object. MIT Press, Cambridge, MA, 1960.

    MATH  Google Scholar 

  17. H. A. Simon. The Sciences of the Artificial. MIT Press., Cambridge, MA, 1996.

    Google Scholar 

  18. R. M. Steinman, Z. Pizlo, and F. J. Pizlo. Phi is not beta, and why Wertheimer’s discovery launched the Gestalt revolution. Vision Research, 40:2257–2264, 2000.

    Article  Google Scholar 

  19. The Journal of Problem Solving. http://docs.lib.purdue.edu/jps/. Last accessed January 2008.

  20. J. von Neumann and O. Morgenstern. The Theory of Games and Economic Behavior. Princeton University Press, Princeton, NJ, 1944.

    Google Scholar 

  21. R. J. Watt. Scanning from coarse to fine spatial scales in the human visual system after the onset of a stimulus. Journal of the Optical Society of America, A4:2006–2021, 1987.

    Google Scholar 

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Saalweachter, J., Pizlo, Z. (2008). Non-Euclidean Traveling Salesman Problem. In: Kugler, T., Smith, J.C., Connolly, T., Son, YJ. (eds) Decision Modeling and Behavior in Complex and Uncertain Environments. Springer Optimization and Its Applications, vol 21. Springer, New York, NY. https://doi.org/10.1007/978-0-387-77131-1_14

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