On terrain coverage optimization by using a network approach for universal graph-based data mining and knowledge discovery

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8609 LNAI, Issue , 2014, Pages 564-573

  Preuß, Michael ^a   Dehmer, Matthias ^a   Pickl, Stefan ^a   Holzinger, Andreas ^b,c  

^a BUNDESWEHR UNIVERSITY MUNICH   (Germany)

^b MEDICAL UNIVERSITY OF GRAZ   (Austria)

^c GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPH THEORY; INFORMATION SCIENCE;

COVERAGE OPTIMIZATIONS; GRAPH-BASED; GRAPH-BASED DATA MININGS; MULTI AGENT; PARAMETER SET; POTENTIAL RESEARCHES; ROUTE PLANNING;

GRAPHIC METHODS;

EID: 84905251987     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-09891-3_51     Document Type: Conference Paper

References (57)

1. van Evert, F.K., van der Heijden, G.W.A.M., Lotz, L.A.P., Polder, G., Lamaker, A., de Jong, A., Kuyper, M.C., Groendijk, E.J.K., Neeteson, J.J., van der Zalm, T.: A mobile field robot with vision-based detection of volunteer potato plants in a corn crop. Weed Technology 20, 853-861 (2006)
2. Kumar, V., Rus, D., Singh, S.: Robot and sensor networks for first responders. IEEE Pervasive Computing 3, 24-33 (2004)
3. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26, 29-41 (1996)
4. Senthilkumar, K.S., Bharadwaj, K.K.: Spanning tree based terrain coverage by multi robots in unknown environments. In: IEEE Annual IEEE INDICON Conference, pp. 120-125 (2008)
5. Holzinger, A., Ofner, B., Dehmer, M.: Multi-touch graph-based interaction for knowledge discovery on mobile devices: State-of-the-art and future challenges. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 241-254. Springer, Heidelberg (2014)
6. Holzinger, A., Dehmer, M., Jurisica, I.: Knowledge discovery and interactive data mining in bioinformatics - state-of-the-art, future challenges and research directions. BMC Bioinformatics 15, I1 (2014)
7. Zheng, X., Koenig, S., Kempe, D., Jain, S.: Multirobot forest coverage for weighted and unweighted terrain. Transactions on Robotics 26, 1018-1031 (2010)
8. Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE 95, 215-233 (2007)
9. Arkin, R., Balch, T.: Cooperative multiagent robotic systems. In: Artificial Intelligence and Mobile Robots. MIT/AAAI Press (1998)
10. Wagner, I., Bruckstein, A.: From ants to a(ge)nts: A special issue on ant-robotics. Annals of Mathematics and Artificial Intelligence 31, 1-5 (2001)
11. Chevallier, D., Payandeh, S.: On kinematic geometry of multi-agent manipulating system based on the contact force information. In: Proceedings of the 6th International Conference on Intelligent Autonomous Systems (2000)
12. Gerkey, B., Mataric, M.: Sold!: auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation 18, 758-768 (2002)
13. Zlot, R., Stentz, A., Dias, M., Thayer, S.: Multi-robot exploration controlled by a market economy. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, pp. 3016-3023 (2002)
14. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269-271 (1959)
15. Dorigo, M.: Optimization, Learning and Natural Algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy (1992) (in Italian)
16. Floyd, R.W.: Algorithm 97: Shortest path. Communications of the ACM 5, 345 (1962)
17. Gen, M., Cheng, R., Wang, Q.: Genetic algorithms for solving shortest path problems. In: IEEE International Conference on Evolutionary Computation, pp. 401-406 (1997)
18. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4, 100-107 (1968)
19. Preuß, M.: A multi-objective online terrain coverage approach. In: Proceedings of the International Conference on Operations Research. Springer (in print, 2014)
20. Hoog, J., Cameron, S., Visser, A.: Role-based autonomous multi-robot exploration. In: Proceedings of the International Conference on Advanced Cognitive Technologies and Applications (2009)
21. Ghoul, S., Hussein, A., Abdel-Wahab, M., Witkowski, U., Rückert, U.: A modified multiple depth first search algorithm for grid mapping using mini-robots khepera. Journal of Computing Science and Engineering 2, 321-338 (2008)
22. Preuß, M.: Terrain Coverage - Modelle und Algorithmen. Master's thesis, University of the German Federal Armed Forces Munich (2011)
23. Alaya, I., Solnon, C., Ghédira, K.: Ant colony optimization for multi-objective optimization problems. In: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, pp. 450-457 (2007)
24. Karasan, O., Pinar, M., Yaman, H.: The robust shortest path problem with interval data. Technical report, Bilkent University, Department of Industrial Engineering, Ankara (2001)
25. Bertsekas, D., Tsitsiklis, J.: An Analysis of Stochastic Shortest Path Problems. Mathematics of Operations Research 16 (1991)
26. Yao, J.S., Lin, F.T.: Fuzzy shortest-path network problems with uncertain edge weights. Journal of Information Science and Engineering 19, 329-351 (2003)
27. Sahinidis, N.: Optimization under uncertainty: state-of-the-art and opportunities. Computers & Chemical Engineering 28, 971-983 (2004); FOCAPO 2003 Special issue
28. Adamic, L., Huberman, B.: Power-law distribution of the world wide web. Science 287, 2115a (2000)
29. Chakrabarti, S.: Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann, San Francisco (2002)
30. Barabási, A.L., Oltvai, Z.N.: Network biology: Understanding the cell's functional organization. Nature Reviews. Genetics 5, 101-113 (2004)
31. Dehmer, M., Emmert-Streib, F., Graber, A., Salvador, A. (eds.): Applied Statistics for Network Biology. Quantitative and Network Biology. Wiley-Blackwell (2011)
32. Emmert-Streib, F., Dehmer, M. (eds.): Analysis of Microarray Data: A Network-based Approach. Wiley VCH Publishing (2010)
33. Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of Networks. From Biological Networks to the Internet and WWW. Oxford University Press (2003)
34. Erdös, P., Rényi, P.: On the evolution of random graphs. Magyar Tud. Akad. Mat. Kutató Int. Közl 5, 17-61 (1960)
35. Watts, D.J., Strogatz, S.H.: Collective dynamics of 'small-world' networks. Nature 393, 440-442 (1998)
36. Estrada, E.: The Structure of Complex Networks. Theory and Applications. Oxford University Press (2011)
37. Dehmer, M., Emmert-Streib, F.: Quantitative Graph Theory. Theory and Applications. CRC Press (in press, 2014)
38. Mehler, A.: A quantitative graph model of social ontologies by example of wikipedia. In: Mehler, A., Sharoff, S., Rehm, G., Santini, M. (eds.) Genres on the Web: Computational Models and Empirical Studies. Springer (2009) (to appear)
39. Mehler, A.: Social ontologies as generalized nearly acyclic directed graphs: A quantitative graph model of social tagging. In: Dehmer, M., Emmert-Streib, F., Mehler, A. (eds.) Towards an Information Theory of Complex Networks: Statistical Methods and Applications, pp. 259-319. Birkhäuser, Boston/Basel (2011)
40. Halin, R.: Graphentheorie, Berlin, Germany. Akademie Verlag (1989)
41. Harary, F.: Graph Theory, Reading, MA, USA. Addison Wesley Publishing Company (1969)
42. Bonchev, D., Rouvray, D.H.: Complexity in Chemistry, Biology, and Ecology, New York, NY, USA. Mathematical and Computational Chemistry. Springer (2005)
43. Mowshowitz, A.: Entropy and the complexity of the graphs I: An index of the relative complexity of a graph. Bull. Math. Biophys. 30, 175-204 (1968)
44. Todeschini, R., Consonni, V., Mannhold, R.: Handbook of Molecular Descriptors, Weinheim, Germany. Wiley-VCH (2002)
45. Bonchev, D., Mekenyan, O., Trinajstić, N.: Isomer discrimination by topological information approach. J. Comp. Chem. 2, 127-148 (1981)
46. Dehmer, M., Emmert-Streib, F., Grabner, M.: A computational approach to construct a multivariate complete graph invariant. Inf. Sci. 260, 200-208 (2014)
47. Dehmer, M., Grabner, M., Varmuza, K.: Information indices with high discriminative power for graphs. PLoS One 7, e31214 (2012)
48. Konstantinova, E.V., Skorobogatov, V.A., Vidyuk, M.V.: Applications of information theory in chemical graph theory. Indian Journal of Chemistry 42, 1227-1240 (2002)
49. Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River (1988)
50. Bunke, H.: Graph matching: Theoretical foundations, algorithms, and applications. In: Proceedings of Vision Interface 2000, pp. 82-88 (2000)
51. Sobik, F.: Graphmetriken und Klassifikation strukturierter Objekte. ZKI-Informationen, Akad. Wiss. DDR 2, 63-122 (1982)
52. Zelinka, B.: On a certain distance between isomorphism classes of graphs. Časopis pro p̌est. Mathematiky 100, 371-373 (1975)
53. Dehmer, M., Emmert-Streib, F.: Comparing large graphs efficiently by margins of feature vectors. Applied Mathematics and Computation 188, 1699-1710 (2007)
54. Dehmer, M., Mehler, A.: A new method of measuring similarity for a special class of directed graphs. Tatra Mountains Mathematical Publications 36, 39-59 (2007)
55. Holzinger, A., Malle, B., Bloice, M., Wiltgen, M., Ferri, M., Stanganelli, I., Hofmann-Wellenhof, R.: On the generation of point cloud data sets: the first step in the knowledge discovery process. In: Holzinger, A., Jurisica, I. (eds.) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. LNCS, vol. 8401, pp. 57-80. Springer, Heidelberg (2014)
56. Kasaiezadeh, A., Khajepour, A.: Multi-agent stochastic level set method in image segmentation. Computer Vision and Image Understanding 117, 1147-1162 (2013)
57. Holzinger, K., Palade, V., Rabadan, R., Holzinger, A.: Darwin or lamarck? future challenges in evolutionary algorithms for knowledge discovery and data mining. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 35-56. Springer, Heidelberg (2014)