Abstract
The search for information in a complex information space - such as the Web or large digital libraries, or in an unkown robotics environment - requires the design of efficient and intelligent strategies for (1) determining regions of interest, (2) detecting and classifying information of interest, and (3) searching the space by autonomous agents. This paper discusses strategies for directing autonomous search based on spatio-temporal distributions. We discuss a model for search assuming that the environment is static, and where the information that agents have is updated as they pursue their discovery of the environment. Autonomous search algorithms are designed and compared using simulations.
This research was supported by the U.S. Army Research Office, Grant No. DAAH04-96-1-0448.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Reference
A.C. Kamil, F. Lindstrom, J. Peters “The detection of cryptic prey by blue jays (Cyanocitta cristata): I. The effects of travel time”, Animal Behaviour Vol. 33(4) pp. 1068–1079, 1985.
J. McNamara, A. Houston “A simple model of information use in the exploitation of patchily distributed food”, Animal Behaviour Vol. 33(2), pp. 553–560, 1985.
C. Jorgensen, W. Hamel and C. Weisbin. “Autonomous robot navigation”, BYTE, Vol. 11, pp. 223–235, Jan. 1986.
N. Rao “Algorithmic framework for learned robot navigation in unknown terrains” Computer pp. 37–43, 1989.
P.Pf. Spelt, E. Lyness and G. deSaussure. “Developpment and training of a learning expert system in an autonomous mobile robot via simulation”, Simulation, pp. 223–228, 1989.
C.R. Weisbin, G. deSaussure, J.R. Einstein and F.G. Pin. “Autonomous mobile robot navigation and learning”, Computer pp. 29–35, 1989.
E. Gelenbe, “Random neural networks with negative and positive signals and product form solution”, Neural Computation, Vol. 1, No.4, pp 502–511, 1989.
E. Gelenbe, “Stability of the random neural network model”, Neural Computation, Vol. 2, No. 2, pp. 239–247, 1990.
Z. Shiller and Y.R. Gwo. “Dynamic motion planning of autonomous vehicles”, IEEE Transaction On Robotics and Automation, Vol. 7, no. 2, pp. 241–249, 1991.
G. Beni and J. Wang. “Theoretical problems for the realization of Distributed Robotic Systems”, Proc. IEEE Int. Conf. on Robotics and Automation, pp. 1914–1920, 1991.
M.J. Mataric, “Distributed approaches to behavior control”, Proc. SPIE, Vol.1828, Sensor Fusion V, pp. 373–382, 1992.
Y. Shoham and M. Tennenholtz “On the synthesis of useful social laws for artificial robot societies”, Proc. 10th National Conf. on Artificial Intelligence, pp. 276–281, 1992.
S. Gross and J.L. Deneubourg, “Harvesting by a group of robots”, Proc. 1st European Conf. on Artificial Life, pp. 195–204, 1992.
G. Lucarini, M. Varoli, R. Cerutti, and G. Sandini, “Cellular Robotics: simulation and hardware implementation” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 846–852, 1993.
T. Ueyama and T. Fukuda. “Self-organization of Cellular Robots using random walk with simple rules”, Proc. IEEE Int. Conf. on Robotics and Automation, pp. 595–600, 1993.
N. Schmajuk, H.T. Blair “Place learning and the dynamics of spatial navigation: A neural network approach,” Adaptive Behavior Vol. 1, No. 3, pp. 353–385, 1993.
N. Schmajuk, A.D. Thieme, H.T. Blair “Maps, routes, and the Hippocampus: A neural network approach”, Hippocampus, Vol. 3, No.3, pp. 387–400, 1993.
D. Maio, and S. Rizzi. “Map learning and clustering in autonomous systems“, IEEE Transaction On Pattern Analysis And Machine Intelligence, Vol. 15, no. 12, pp. 1286–1297, 1993.
E. Hou and D. Zheng “Mobile robot path planning based on hierarchical hexagonal decomlocation and artificial potential virtual spaces”, Journal of Robotic Systems, pp. 605–614, 1994.
L.E. Parker “Designing control laws for cooperative robot teams”, Proc. IEEE Int. Conf. on Robotics and Automation, pp. 582–587, 1994.
E. Rimon and J.F. Canny “Construction of C-space roadmaps from local sensory data: what should the sensors look for? ”, Proc. IEEE Int. Conf. on Robotics and Automation, pp. 117–123, 1994.
J.A. Meyer, H.L. Roitblat and S.W. Wilson (eds.) “From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (Complex adaptive systems) ” pp. 432–510. MIT Press, Cambridge, MA, 523p., 1992.
J. Reif and H. Wang, “Social potential virtual spaces: A distributed behavioral control for autonomous robots”, Proc. Workshop on the Algorithmic Foundations of Robotics, pp. 331–345, 1994.
S. Benhamou “Spatial memory and searching efficiency,” Animal Behaviour Vol. 47(6), pp. 1423–1433, 1994.
E. Gelenbe, Z.-H. Mao, Y.-D. Li “Function approximation with spiked random networks”, IEEE Trans. on Neural Networks, Vol. 10, No.1, pp. 3–9, 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gelenbe, E. (1999). Autonomous Search for Information in an Unknown Environment. In: Klusch, M., Shehory, O.M., Weiss, G. (eds) Cooperative Information Agents III. CIA 1999. Lecture Notes in Computer Science(), vol 1652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48414-0_3
Download citation
DOI: https://doi.org/10.1007/3-540-48414-0_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66325-6
Online ISBN: 978-3-540-48414-1
eBook Packages: Springer Book Archive