@article{fdi:010076259,
  title = {{I}s it useful for a robot to visit a museum ? {T}he impact of cumulative learning on a robot population},
  author = {{M}oualla, {A}. and {B}oucenna, {S}. and {K}araouzene, {A}. and {V}idal, {D}enis and {G}aussier, {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n this work, we study how learning in a special environment such as a museum can influence the behavior of robots. {M}ore specifically, we show that online learning based on interaction with people at a museum leads the robots to develop individual preferences. {W}e first developed a humanoid robot ({B}erenson) that has the ability to head toward its preferred object and to make a facial expression that corresponds to its attitude toward said object. {T}he robot is programmed with a biologically-inspired neural network sensory-motor architecture. {T}his architecture allows {B}erenson to learn and to evaluate objects. {D}uring experiments, museum visitors’ emotional responses to artworks were recorded and used to build a database for training. {A} similar database was created in the laboratory with laboratory objects. {W}e use those databases to train two simulated populations of robots. {E}ach simulated robot emulates the {B}erenson sensory-motor architecture. {F}irstly, the results show the good performance of our architecture in artwork recognition in the museum. {S}econdly, they demonstrate the effect of training variability on preference diversity. {T}he response of the two populations in a new unknown environment is different; the museum population of robots shows a greater variance in preferences than the population of robots that have been trained only on laboratory objects. {T}he obtained diversity increases the chances of success in an unknown environment and could favor an accidental discovery.},
  keywords = {{ANTHROPOLOGIE} {CULTURELLE} ; {ROBOTIQUE} ; {MUSEE} ; {ESTHETIQUE} ; {INTELLIGENCE} {ARTIFICIELLE} ; {RECONNAISSANCE} {DE} {FORME} ; {RESEAU} {NEURONAL} ; {FRANCE}},
  booktitle = {},
  journal = {{P}aladyn: {J}ournal of {B}ehavioral {R}obotics},
  volume = {9},
  numero = {1},
  pages = {374--390},
  ISSN = {2080-9778},
  year = {2018},
  DOI = {10.1515/pjbr-2018-0025},
  URL = {https://www.documentation.ird.fr/hor/fdi:010076259},
}