Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

  • Andreas Holzinger
  • Markus Plass
  • Katharina Holzinger
  • Gloria Cerasela Crişan
  • Camelia-M. Pintea
  • Vasile Palade
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9817)


Most Machine Learning (ML) researchers focus on automatic Machine Learning (aML) where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from the availability of “big data”. However, sometimes, for example in health informatics, we are confronted not a small number of data sets or rare events, and with complex problems where aML-approaches fail or deliver unsatisfactory results. Here, interactive Machine Learning (iML) may be of help and the “human-in-the-loop” approach may be beneficial in solving computationally hard problems, where human expertise can help to reduce an exponential search space through heuristics.

In this paper, experiments are discussed which help to evaluate the effectiveness of the iML-“human-in-the-loop” approach, particularly in opening the “black box”, thereby enabling a human to directly and indirectly manipulating and interacting with an algorithm. For this purpose, we selected the Ant Colony Optimization (ACO) framework, and use it on the Traveling Salesman Problem (TSP) which is of high importance in solving many practical problems in health informatics, e.g. in the study of proteins.


interactive Machine Learning Human-in-the-loop Traveling Salesman Problem Ant Colony Optimization 


  1. 1.
    Dong, M., Tao, J., Mak, M.W.: Guest editorial: advances in machine learning for speech processing. J. Sig. Process. Syst. 82, 137–140 (2016)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, C.C.: Ensemble-based and hybrid recommender systems. Recommender Systems: The Textbook, pp. 199–224. Springer International Publishing, Switzerland (2016)CrossRefGoogle Scholar
  3. 3.
    Sofman, B., Lin, E., Bagnell, J.A., Cole, J., Vandapel, N., Stentz, A.: Improving robot navigation through self-supervised online learning. J. Field Robot. 23, 1059–1075 (2006)CrossRefGoogle Scholar
  4. 4.
    Holzinger, A.: Interactive machine learning (iml). Informatik Spektrum 39, 64–68 (2016)CrossRefGoogle Scholar
  5. 5.
    Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inform. 3, 119–131 (2016)CrossRefGoogle Scholar
  6. 6.
    Akgul, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B.: Content-based image retrieval in radiology: current status and future directions. J. Digit. Imaging 24, 208–222 (2011)CrossRefGoogle Scholar
  7. 7.
    Gigerenzer, G., Gaissmaier, W.: Heuristic decision making. Ann. Rev. Psychol. 62, 451–482 (2011)CrossRefGoogle Scholar
  8. 8.
    Atzmueller, M., Baumeister, J., Puppe, F.: Introspective subgroup analysis for interactive knowledge refinement. In: Sutcliffe, G., Goebel, R. (eds.) FLAIRS Nineteenth International Florida Artificial Intelligence Research Society Conference, pp. 402–407. AAAI Press (2006)Google Scholar
  9. 9.
    Papadimitriou, C.H.: Computational Complexity. Encyclopedia of Computer Science, pp. 260–265. Wiley, Chichester (2003)Google Scholar
  10. 10.
    Gigerenzer, G.: Gut Feelings: Short Cuts to Better Decision Making. Penguin, London (2008)Google Scholar
  11. 11.
    Holzinger, A.: iML (2016). Accessed 3 July 2016
  12. 12.
    Kieseberg, P., Malle, B., Frühwirth, P., Weippl, E., Holzinger, A.: A tamper-proof audit and control system for the doctor in the loop. Brain Inform. 1–11 (2016)Google Scholar
  13. 13.
    Kieseberg, P., Weippl, E., Holzinger, A.: Trust for the doctor-in-the-loop. Eur. Res. Consortium Inform. Math. (ERCIM) News: Tackling Big Data Life Sci. 104, 32–33 (2016)Google Scholar
  14. 14.
    Wilson, A.G., Dann, C., Lucas, C., Xing, E.P.: The human kernel. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, NIPS 2015, vol. 28. pp. 2836–2844 (2015)Google Scholar
  15. 15.
    Wilson, A.G., Adams, R.P.: Gaussian process kernels for pattern discovery and extrapolation. In: International Conference on Machine Learning ICML 13. vol. 28, pp. 1067–1075. JMLR (2013)Google Scholar
  16. 16.
    Bernstein, A., Arbuckle, T., Roberts, D.V., M., Belsky, M.: A chess playing program for the IBM 704. In: Proceedings of the 6–8 May 1958 Western Joint Computer Conference: Contrasts in Computers, pp. 157–159. ACM (1958)Google Scholar
  17. 17.
    Holzinger, A.: Human-computer interaction and knowledge discovery (HCI-KDD): what is the benefit of bringing those two fields to work together? In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 319–328. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    Crişan, G.C., Nechita, E., Palade, V.: Ant-based system analysis on the traveling salesman problem under real-world settings. Combinations of Intelligent Methods and Applications. Smart Innovation, Systems and Technologies, vol. 46, pp. 39–59. Springer, Heidelberg (2016)CrossRefGoogle Scholar
  19. 19.
    Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A., Yannakakis, M.: On the complexity of protein folding. J. Comput. Biol. 5, 423–465 (1998)CrossRefMATHGoogle Scholar
  20. 20.
    Macgregor, J.N., Ormerod, T.: Human performance on the traveling salesman problem. Percept. Psychophysics 58, 527–539 (1996)CrossRefGoogle Scholar
  21. 21.
    Crisan, G.C., Pintea, C.-M., Pop, P., Matei, O.: An analysis of the hardness of novel TSP Iberian instances. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds.) HAIS 2016. LNCS, vol. 9648, pp. 353–364. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-32034-2_30 CrossRefGoogle Scholar
  22. 22.
    Cook, W.: TSP (2016). Accessed 3 July 2016
  23. 23.
    Crişan, G.C., Pintea, C.M., Palade, V.: Emergency management using geographic information systems: application to the first romanian traveling salesman problem instance. Knowl. Inf. Syst. 1–21 (2016)Google Scholar
  24. 24.
    Cook, W.: In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation. Princeton University Press, Princeton (2012)MATHGoogle Scholar
  25. 25.
    Dantzig, G.B.: Linear Programming and Extensions. Princeton University Press, Princeton (1998)MATHGoogle Scholar
  26. 26.
    Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Courier Corporation, Mineola (1982)MATHGoogle Scholar
  27. 27.
    Tucker, A.: On directed graphs and integer programs. In: Symposium on Combinatorial Problems, Princeton University (1960)Google Scholar
  28. 28.
    Dorigo, M., Birattari, M., Stuetzle, T.: Ant colony optimization - artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. 1, 28–39 (2006)CrossRefGoogle Scholar
  29. 29.
    Sumpter, D.J.T., Beekman, M.: From nonlinearity to optimality: pheromone trail foraging by ants. Anim. Behav. 66, 273–280 (2003)CrossRefGoogle Scholar
  30. 30.
    Dorigo, M., Sttzle, T.: Ant colony optimization: overview and recent advances. Technical report, IRIDIA, Universite Libre de Bruxelles (2009)Google Scholar
  31. 31.
    Li, L., Peng, H., Kurths, J., Yang, Y., Schellnhuber, H.J.: Chaos-order transition in foraging behavior of ants. Proc. Nat. Acad. Sci. 111, 8392–8397 (2014)CrossRefGoogle Scholar
  32. 32.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. Proc. First Eur. Conf. Artif. Life ECAL 91, 134–142 (1991)Google Scholar
  33. 33.
    Gordon, D.M.: The rewards of restraint in the collective regulation of foraging by harvester ant colonies. Nature 498, 91–93 (2013)CrossRefGoogle Scholar
  34. 34.
    Yang, X.S.: Nature-Inspired Optimization Algorithms. Elsevier, Amsterdam (2014)MATHGoogle Scholar
  35. 35.
    Brownlee, J.: Clever Algorithms: Nature-Inspired Programming Recipes. Jason Brownlee, Melbourne (2011)Google Scholar
  36. 36.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. Trans. Evol. Comput. 1, 53–66 (1997)CrossRefGoogle Scholar
  37. 37.
    Pintea, C., Dumitrescu, D., Pop, P.: Combining heuristics and modifying local information to guide ant-based search. Carpathian J. Math. 24, 94–103 (2008)MathSciNetMATHGoogle Scholar
  38. 38.
    Pintea, C.M., Dumitrescu, D.: Improving ant systems using a local updating rule. In: Proceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pp. 295–299. IEEE Computer Society (2005)Google Scholar
  39. 39.
    Helsgaun, K.: An effective implementation of the Lin-Kernighan traveling salesman heuristic. Eur. J. Oper. Res. 126, 106–130 (2000)MathSciNetCrossRefMATHGoogle Scholar
  40. 40.
    Stützle, T., Hoos, H.: Max-min ant system and local search for the traveling salesman problem. In: IEEE International Conference on Evolutionary Computation, pp. 309–314. IEEE (1997)Google Scholar
  41. 41.
    Gerhard Reinelt, U.H.: TSPLIB - Library of sample instances for the TSP (2008). Accessed 23 June 2016
  42. 42.
    Hund, M., Böhm, D., Sturm, W., Sedlmair, M., Schreck, T., Ullrich, T., Keim, D.A., Majnaric, L., Holzinger, A.: Visual analytics for concept exploration in subspaces of patient groups: making sense of complex datasets with the doctor-in-the-loop. Brain Inform. 1–15 (2016)Google Scholar
  43. 43.
    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.) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. LNCS, vol. 8401, pp. 35–56. Springer, Heidelberg (2014)Google Scholar
  44. 44.
    Holzinger, A.: Trends in interactive knowledge discovery for personalized medicine: cognitive science meets machine learning. IEEE Intell. Inform. Bull. 15, 6–14 (2014)Google Scholar
  45. 45.
    Ebner, M., Holzinger, A.: Successful implementation of user-centered game based learning in higher education: an example from civil engineering. Comput. Educ. 49, 873–890 (2007)CrossRefGoogle Scholar
  46. 46.
    Raykar, V.C., Yu, S., Zhao, L.H., Valadez, G.H., Florin, C., Bogoni, L., Moy, L.: Learning from crowds. J. Mach. Learn. Res. (JMLR) 11, 1297–1322 (2010)MathSciNetGoogle Scholar
  47. 47.
    Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T.: Power to the people: the role of humans in interactive machine learning. AI Mag. 35, 105–120 (2014)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Andreas Holzinger
    • 1
  • Markus Plass
    • 1
  • Katharina Holzinger
    • 1
  • Gloria Cerasela Crişan
    • 2
  • Camelia-M. Pintea
    • 3
  • Vasile Palade
    • 4
  1. 1.Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics & DocumentationMedical University GrazGrazAustria
  2. 2.Vasile Alecsandri University of BacǎuBacǎuRomania
  3. 3.Technical University of Cluj-NapocaCluj-NapocaRomania
  4. 4.Faculty of Engineering, Environment and ComputingCoventry UniversityCoventryUK

Personalised recommendations