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Bio-inspired Architecture for Active Sensorimotor Localization

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Spatial Cognition VII (Spatial Cognition 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6222))

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Abstract

Determining one’s position within the environment is a basic feature of spatial behavior and spatial cognition. This task is of inherently sensorimotor nature in that it results from a combination of sensory features and motor actions, where the latter comprise exploratory movements to different positions in the environment. Biological agents achieve this in a robust and effortless fashion, which prompted us to investigate a bio-inspired architecture to study the localization process of an artificial agent which operates in virtual spatial environments. The spatial representation in this architecture is based on sensorimotor features that comprise sensory sensory features as well as motor actions. It is hierarchically organized and its structure can be learned in an unsupervised fashion by an appropriate clustering rule. In addition, the architecture has a temporal belief update mechanism which explicitly utilizes the statistical correlations of actions and locations. The architecture is hybrid in integrating bottom-up processing of sensorimotor features with top-down reasoning which is able to select optimal motor actions based on the principle of maximum information gain. The architecture operates on two sensorimotor levels, a macro-level, which controls the movements of the agent in space, and on a micro-level, which controls its eye movements. As a result, the virtual mobile agent is able to localize itself within an environment using a minimum number of exploratory actions.

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References

  1. Aloimonos, Y. (ed.): Special Issue: Purposive, Qualitative and Active Vision, Image Understanding, vol. 56 (1992)

    Google Scholar 

  2. Ballard, D.: Animate vision. Artificial Intelligence 48, 57–86 (1991)

    Article  Google Scholar 

  3. Byun, Y.T., Kuipers, B.: A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. IEEE Journal of Robotics and Autonomous Systems 8, 47–63 (1991)

    Article  Google Scholar 

  4. Delmotte, F., Smets, P.: Target identification based on the transferable belief model interpretation of Dempster-Shafer model. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 34(4), 457–471 (2004)

    Article  Google Scholar 

  5. Dubois, D., Prade, H.: On the unicity of Dempster’s rule of combination. International Journal of Intelligent Systems 1(2), 133–142 (1986)

    Article  MATH  Google Scholar 

  6. Elfes, A.: Sonar-based real-world mapping and navigation. IEEE Journal of robotics and automation 3(3), 249–265 (1987)

    Article  Google Scholar 

  7. Foo, P., Warren, W.H., Duchon, A., Tarr, M.J.: Do humans integrate routes into a cognitive map? map- versus landmark-based navigation of novel shortcuts. Journal of Experimental Psychology 31(2), 195–215 (2005)

    Google Scholar 

  8. Frank, L., Brown, E., Wilson, M.: Trajectory encoding in the hippocampus and entorhinal cortex. Neuron 27(1), 169–178 (2000)

    Article  Google Scholar 

  9. Gadzicki, K.: Hierarchical clustering of sensorimotor features. In: Mertsching, B., Hund, M., Aziz, Z. (eds.) KI 2009. LNCS (LNAI), vol. 5803, pp. 331–338. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  10. Gibson, J.J.: The ecological approach to visual perception. Houghton Mifflin, Boston (1979)

    Google Scholar 

  11. Gillner, S., Mallot, H.A.: Navigation and acquisition of spatial knowledge in a virtual maze. Journal of Cognitive Neuroscience 10(4), 445–463 (1998)

    Article  Google Scholar 

  12. Gordon, J., Shortliffe, E.H.: A method for managing evidential reasoning in a hierarchical hypothesis space. Artif. Intell. 26(3), 323–357 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  13. Hirtle, S.C., Jonides, J.: Evidence of hierarchies in cognitive maps. Memory and Cognition 13(3), 208–217 (1985)

    Google Scholar 

  14. Hommel, B., Muesseler, J., Aschersleben, G., Prinz, W.: The theory of event coding (tec): A framework for perception and action planning. Behavioral and Brain Sciences 24, 849–878 (2001)

    Article  Google Scholar 

  15. Kalman, R.: A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82(1), 35–45 (1960)

    Google Scholar 

  16. Kohonen, T.: Self-organizing maps. Springer series in information sciences, 3rd edn., vol. 30. Springer, Heidelberg (2001)

    Google Scholar 

  17. Kuipers, B.: The map in the head metaphor. Environment and Behavior 14(2), 202–220 (1982)

    Article  Google Scholar 

  18. Kuipers, B.: The spatial semantic hierarchy. Artificial Intelligence 119, 191–233 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  19. Mataric, M.: Integration of representation into goal-driven behavior-basedrobots. IEEE Transactions on Robotics and Automation 8(3), 304–312 (1992)

    Article  Google Scholar 

  20. Moore, T.: Shape representations and visual guidance of saccadic eye movements. Science 285(5435), 1914 (1999)

    Article  Google Scholar 

  21. Nene, S., Nayar, S., Murase, H.: Columbia object image library (COIL-20). Tech. rep., Dept. Comput. Sci., Columbia Univ., New York (1996)

    Google Scholar 

  22. O’Keefe, J., Nadel, L.: The hippocampus as a cognitive map. Clarendon Press, Oxford (1978)

    Google Scholar 

  23. O’Regan, J.K., Noë, A.: A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24, 939–973 (2001)

    Article  Google Scholar 

  24. Orponen, P.: Dempster’s rule of combination is #P-complete. Artificial Intelligence 44(1-2), 245–253 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  25. Pal, N., Bezdek, J., Hemasinha, R.: Uncertainty measures for evidential reasoning II: A new measure of total uncertainty. International Journal of Approximate Reasoning 8(1), 1–16 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  26. Prinz, W.: A common coding approach to perception and action, relationships between perception and action: current approaches edn., pp. 167–203. Springer, Berlin (1990)

    Google Scholar 

  27. Reineking, T.: Particle filtering in the Dempster-Shafer theory. International Journal of Approximate Reasoning (2010) (in revision) (submitted) (Febuary 17, 2009)

    Google Scholar 

  28. Reineking, T., Kohlhagen, C., Zetzsche, C.: Efficient wayfinding in hierarchically regionalized spatial environments. In: Freksa, C. (ed.) Spatial Cognition VI. LNCS (LNAI), vol. 5248, pp. 56–70. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  29. Reineking, T., Schult, N., Hois, J.: Combining statistical and symbolic reasoning for active scene categorization. In: Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2009, Revised Selected Papers. Communications in Computer and Information Science. Springer, Heidelberg (2010) (in press)

    Google Scholar 

  30. Rizzolatti, G., Craighero, L.: The mirror-neuron system 27, 169–192 (2004)

    Google Scholar 

  31. Rizzolatti, G., Matelli, M.: Two different streams form the dorsal visual system: anatomy and functions. Experimental Brain Research 153(2), 146–157 (2003)

    Article  Google Scholar 

  32. Salton, G.: The SMART Retrieval System—Experiments in Automatic Document Processing. Prentice-Hall, Inc., Upper Saddle River (1971)

    Google Scholar 

  33. Schill, K.: Decision Support Systems with Adaptive Reasoning Strategies. In: Freksa, C., Jantzen, M., Valk, R. (eds.) Foundations of Computer Science. LNCS, vol. 1337, pp. 417–427. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  34. Schill, K., Umkehrer, E., Beinlich, S., Krieger, G., Zetzsche, C.: Scene analysis with saccadic eye movements: Top-down and bottom-up modeling. Journal of Electronic Imaging 10(1), 152–160 (2001)

    Article  Google Scholar 

  35. Schill, K., Zetzsche, C., Hois, J.: A belief-based architecture for scene analysis: From sensorimotor features to knowledge and ontology. Fuzzy Sets and Systems 160(10), 1507–1516 (2009)

    Article  Google Scholar 

  36. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  37. Siegel, A., White, S.: The development of spatial representations of large-scale environments. Advances in child development and behavior 10, 9 (1975)

    Google Scholar 

  38. Smets, P.: Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate Reasoning 9, 1–35 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  39. Tanimoto, T.: IBM interal report. Tech. rep., IBM (November 1957)

    Google Scholar 

  40. Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust Monte Carlo localization for mobile robots. Artificial Intelligence 128(1-2), 99–141 (2001)

    Article  MATH  Google Scholar 

  41. Thrun, S.: Learning occupancy grids with forward sensor models. Autonomous Robots 15, 111–127 (2003)

    Article  Google Scholar 

  42. Tversky, B.: Distortions in cognitive maps. Geoforum 23(2), 131–138 (1992)

    Article  Google Scholar 

  43. Wang, R.F., Spelke, E.S.: Updating egocentric representations in human navigation. Cognition 77, 215–250 (2000)

    Article  Google Scholar 

  44. Ward, J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244 (1963)

    Article  MathSciNet  Google Scholar 

  45. Wiener, J., Mallot, H.: ’Fine-to-coarse’ route planning and navigation in regionalized environments. Spatial Cognition and Computation 3(4), 331–358 (2003)

    Article  Google Scholar 

  46. Wiener, J., Mallot, H.: ’Fine-to-coarse’ route planning and navigation in regionalized environments. Spatial Cognition and Computation 3(4), 331–358 (2003)

    Article  Google Scholar 

  47. Yarbus, A.L.: Eye Movements and Vision. Plenum Press, New York (1967)

    Google Scholar 

  48. Zetzsche, C., Galbraith, C., Wolter, J., Schill, K.: Navigation based on a sensorimotor representation: a virtual reality study. In: Rogowitz, B.E., Pappas, T.N., Daly, S.J. (eds.) Proceedings of SPIE. Human Vision and Electronic Imaging XII, February 2007, vol. 6492 (2007)

    Google Scholar 

  49. Zetzsche, C., Krieger, G.: Nonlinear operators and higher-order statistics in image processing and analysis. In: Proc. ISPA 2001 - 2nd International Symposium on Image and Signal Processing and Analysis, pp. 119–124 (2001)

    Google Scholar 

  50. Zetzsche, C., Wolter, J., Galbraith, C., Schill, K.: Representation of space: image-like or sensorimotor. Spatial Vision 22(5), 409–424 (2009)

    Article  Google Scholar 

  51. Zetzsche, C., Wolter, J., Schill, K.: Sensorimotor representation and knowledge-based reasoning for spatial exploration and localisation. Cognitive Processing 9, 283–297 (2008)

    Article  Google Scholar 

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Reineking, T., Wolter, J., Gadzicki, K., Zetzsche, C. (2010). Bio-inspired Architecture for Active Sensorimotor Localization. In: Hölscher, C., Shipley, T.F., Olivetti Belardinelli, M., Bateman, J.A., Newcombe, N.S. (eds) Spatial Cognition VII. Spatial Cognition 2010. Lecture Notes in Computer Science(), vol 6222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14749-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-14749-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14748-7

  • Online ISBN: 978-3-642-14749-4

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