Honey-pot constrained searching with local sensory information

Nonlinear Analysis, Theory, Methods and Applications

Volumn 65, Issue 9, 2006, Pages 1773-1793

  DasGupta, Bhaskar ^a   Hespanha, João P ^b   Riehl, James ^b   Sontag, Eduardo ^c  

^a 1120 Science and Engineering Offices   (United States)

^b University of California   (United States)

^c RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATA PROCESSING; INTELLIGENT ROBOTS; POLYNOMIAL APPROXIMATION; PROBLEM SOLVING; RANDOM PROCESSES;

APPROXIMATION ALGORITHMS; DISCRETE SEARCH PROBLEM; GREEDY ALGORITHM; HONEY-POT CONSTRAINED SEARCHING;

SENSORY PERCEPTION;

EID: 33746619648     PISSN: 0362546X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.na.2005.10.049     Document Type: Article

References (30)

1. Alur R., Henzinger T., Lafferriere G., and Pappas G.J. Discrete abstractions of hybrid systems. Proceedings of IEEE 88 2 (2000) 971-984
2. S. Arora, G. Karakostas, A 2 + ε approximation for the k-MST problem, in: 11th ACM-SIAM Symposium on Discrete Algorithms, 2000, pp. 754-759
3. In: Chudnovksy D.V., and Chudnovksy G.V. (Eds). Search Theory: some Recent Developments. Lecture Notes in Pure and Applied Mathematics vol. 112 (1989), Marcel Dekker, New York
4. Eagle J.N. Optimal search for a moving target when the search path is constrained. Operations Research 32 5 (1984) 1107-1115
5. Eagle J.N., and Yee J.R. An optimal branch and bound procedure for the constrained path, moving target search problem. Operations Research 38 1 (1990) 110-114
6. J.R. Frost, L.D Stone, Review of search theory: advances and applications to search and rescue decision support, Technical Report CG-D-15-01, US Coast Guard Research and Development Center, Gronton, CT, URL http://www.rdc.uscg.gov/reports/2001/cgd1501dpexsum.pdf, September 2001
7. Shmuel Gal, Continuous Search Games, in Chudnovksy and Chudnovksy [3], pp. 33-53
8. Hershberger J., and Suri S. An optimal algorithm for euclidean shortest paths in the plane. SIAM Journal on Computing, 0097-5397 28 6 (1999) 2215-2256
9. J.P. Hespanha, An efficient MATLAB Algorithm for graph partitioning, Technical Report, University of California, Santa Barbara, CA, URL http://www.ece.ucsb.edu/~hespanha/techreps.html, October 2004
10. J.P. Hespanha, H. Kizilocak, Efficient computation of dynamic probabilistic maps, in: 10th Mediterranean Conf. on Control and Automata, July 2002
11. J.P. Hespanha, M. Prandini, S. Sastry, Probabilistic pursuit-evasion games: a one-step Nash approach, in: 39th Conf. on Decision and Control, vol. 3, December 2000, pp. 2272-2277
12. N. Garg, A 3-approximation for the minimum tree spanning k vertices, in: 37th Annual Symposium on Foundations of Computer Science, 1996, pp. 302-309
13. In: Haley K.B., and Stone L.D. (Eds). Search theory and applications. NATO Conference Series vol. 8 (1980), Plenum Press, New York
14. Itai A., Papadimitriou C.H., and Szwarcfiter J.L. Hamiltonian paths in grid graphs. SIAM Journal on Computing 11 4 (1982) 676-686
15. D.S. Johnson, M. Minkoff, S. Phillips, The prize collecting Steiner tree problem: theory and practice, in: 11th ACM-SIAM Symposium on Discrete Algorithms, 2000, pp. 760-769
16. Kapoor S., Maheshwari S.N., and Mitchell J.S.B. An efficient algorithm for euclidean shortest paths among polygonal obstacles in the plane. Discrete & Computational Geometry 18 (1997) 377-383
17. J. Kim, J.P. Hespanha, Discrete approximations to continuous shortest-path: Application to minimum-risk path planning for groups of UAVs, in: Proc. of the 42nd Conf. on Decision and Contr., December 2003
18. B.O. Koopman, Search and Screening, Operations Evaluations Group Report No. 56, Center for Naval Analyses, Alesandria, VA, 1946
19. Koopman B.O. Search and Screening: General Principles and Historical Applications (1980), Pergamon Press, New York
20. Lössner U., and Wegener I. Discrete sequential search with positive switch cost. Mathematics of Operations Research 7 3 (1982) 426-440
21. M. Mangel, Search Theory: A Differential Equations Approach, in Chudnovksy and Chudnovksy [3], pp. 55-101
22. Phillips R., and Kokotović P. A singular perturbation approach to modeling and control of Markov chains. IEEE Transactions on Automatic Control 26 5 (1981) 1087-1094 October
23. In: Sack J.-R., and Urrutia J. (Eds). Handbook of Computational Geometry (2000), Elsevier, Amsterdam
24. Stewart T.J. Search for a Moving Target when Searcher motion is Restricted. Computers and Operations Research 6 (1979) 129-140
25. T.J. Stewart, Experience with a Branch and Bound Algorithm for Constrained Searcher Motion, in Haley and Stone [13], pp. 247-253
26. Stone L.D. Theory of Optimal Search (1975), Academic Press, New York
27. Stone L.D. The Process of Search Planning: Current Approaches and Continuing Problems. Operation Research 31 (1983) 207-233
28. Thrun S. Robotic mapping: a survey. In: Lakemeyer G., and Nebel B. (Eds). Exploring Artificial Intelligence in the New Millennium (2002), Morgan Kaufmann
29. Thrun S., Burgard W., and Fox D. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning and Autonomous Robots (joint issue) 31 5 (1998) 1-25
30. Trummel K.E., and Weisinger J.R. The Complexity of the optimal searcher path problem. Operations Research 34 2 (1986) 324-327