@article{fdi:010053086,
  title = {{A}utomatic revision of the control knowledge used by trial and error methods : application to cartographic generalisation},
  author = {{T}aillandier, {P}atrick and {D}uch{\^e}ne, {C}. and {D}rogoul, {A}lexis},
  editor = {},
  language = {{ENG}},
  abstract = {{H}umans frequently have to face complex problems. {A} classical approach to solve them is to search the solution by means of a trial and error method. {T}his approach is often used with success by artificial systems. {H}owever, when facing highly complex problems, it becomes necessary to introduce control knowledge ( heuristics) in order to limit the number of trials needed to find the optimal solution. {U}nfortunately, acquiring and maintaining such knowledge can be fastidious. {I}n this paper, we propose an automatic knowledge revision approach for systems based on a trial and error method. {O}ur approach allows to revise the knowledge off-line by means of experiments. {I}t is based on the analysis of solved instances of the considered problem and on the exploration of the knowledge space. {I}ndeed, we formulate the revision problem as a search problem: we search the knowledge set that maximises the performances of the system on a sample of problem instances. {O}ur knowledge revision approach has been implemented for a real-world industrial application: automated cartographic generalisation, a complex task of the cartography domain. {I}n this implementation, we demonstrate that our approach improves the quality of the knowledge and thus the performance of the system.},
  keywords = {{K}nowledge revision ; {P}roblem solving ; {T}rial and error method ; {C}artographic generalisation},
  booktitle = {},
  journal = {{A}pplied {S}oft {C}omputing},
  volume = {11},
  numero = {2},
  pages = {2818--2832},
  ISSN = {1568-4946},
  year = {2011},
  DOI = {10.1016/j.asoc.2010.11.012},
  URL = {https://www.documentation.ird.fr/hor/fdi:010053086},
}