Experiential Solving: Towards a Unified Autonomous Search Constraint Solving Approach

  • Broderick CrawfordEmail author
  • Ricardo Soto
  • Kathleen Crawford
  • Franklin Johnson
  • Claudio León de la Barra
  • Sergio Galdames
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 528)


To solve many problems modeled as Constraint Satisfaction Problems there are no known efficient algorithms. The specialized literature offers a variety of solvers, which have shown good performance. Nevertheless, despite the efforts of the scientific community in developing new strategies, there is no algorithm that is the best for all possible situations. This paper analyses recent developments of Autonomous Search Constraint Solving Systems. Showing that the design of the most efficient and recent solvers is very close to the Experiential Learning Cycle from organizational psychology.


Experiential learning Problem solving Metaheuristics Autonomous search 



Broderick Crawford is supported by Grant CONICYT / FONDECYT / REGULAR / 1140897. Ricardo Soto is supported by Grant CONICYT / FONDECYT / INICIACION / 11130459.


  1. 1.
    Castro, C., Monfroy, E., Figueroa, C., Meneses, R.: An approach for dynamic split strategies in constraint solving. In: Gelbukh, A., de Albornoz, A., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 162–174. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Crawford, B., Soto, R., de la Barra, C.L., Crawford, K., Paredes, F., Johnson, F.: A better understanding of the behaviour of metaheuristics: a psychological view. In: Stephanidis [10], pp. 515–518Google Scholar
  3. 3.
    Crawford, B., Soto, R., Monfroy, E., Palma, W., Castro, C., Paredes, F.: Parameter tuning of a choice-function based hyperheuristic using particle swarm optimization. Expert Syst. Appl. 40(5), 1690–1695 (2013)CrossRefGoogle Scholar
  4. 4.
    Crawford, B., Soto, R., Olivares, R., Herrera, R., Monfroy, E., Paredes, F.: Autonomous search: towards the easy tuning of constraint programming solvers. In: Stephanidis [10], pp. 165–168Google Scholar
  5. 5.
    Hamadi, Y., Monfroy, E., Saubion, F.: Autonomous Search. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Kolb, D.: Chapter 15 - the process of experiential learning. In: Cross, R.L., Israelit, S.B. (eds.) Strategic Learning in a Knowledge Economy, pp. 313–331. Butterworth-Heinemann, Boston (2000)CrossRefGoogle Scholar
  7. 7.
    Kolb, D.A.: Experiential Learning: Experience as the Source of Learning and Development. Prentice-Hall P T R, Englewood Cliffs (1984)Google Scholar
  8. 8.
    Monfroy, E., Castro, C., Crawford, B., Soto, R., Paredes, F., Figueroa, C.: A reactive and hybrid constraint solver. J. Exp. Theor. Artif. Intell. 25(1), 1–22 (2013)CrossRefGoogle Scholar
  9. 9.
    Soto, R., Crawford, B., Palma, W., Galleguillos, K., Castro, C., Monfroy, E., Johnson, F., Paredes, F.: Boosting autonomous search for CSPs via skylines. Inf. Sci. 308, 38–48 (2015)CrossRefGoogle Scholar
  10. 10.
    Stephanidis, C. (ed.): HCI 2014, Part I. CCIS, vol. 434. Springer, Heidelberg (2014)Google Scholar
  11. 11.
    Robert, J.: Sternberg. A triarchic approach to the understanding and assessment of intelligence in multicultural populations. J. Sch. Psychol. 37(2), 145–159 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Broderick Crawford
    • 1
    • 2
    • 3
    Email author
  • Ricardo Soto
    • 1
    • 4
    • 5
  • Kathleen Crawford
    • 1
  • Franklin Johnson
    • 1
    • 6
  • Claudio León de la Barra
    • 1
  • Sergio Galdames
    • 1
  1. 1.Pontificia Universidad Católica de ValparaísoValparaisoChile
  2. 2.Universidad Central de ChileSantiagoChile
  3. 3.Universidad San SebastiánSantiagoChile
  4. 4.Universidad Autónoma de ChileSantiagoChile
  5. 5.Universidad Cientifica del SurLimaPeru
  6. 6.Universidad de Playa AnchaValparaisoChile

Personalised recommendations