?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Bayesian+robot+Programming&rft.creator=Lebeltel%2C+Olivier&rft.creator=Bessiere%2C+Pierre&rft.creator=Diard%2C+Julien&rft.creator=Mazer%2C+Emmanuel&rft.subject=Artificial+Intelligence&rft.subject=Robotics&rft.subject=Statistical+Models&rft.description=We+propose+a+new+method+to+program+robots+based+on+Bayesian+inference+and+learning.+The+capacities+of+this+programming+method+are+demonstrated+through+a+succession+of+increasingly+complex+experiments.+Starting+from+the+learning+of+simple+reactive+behaviors%2C+we+present+instances+of+behavior+combinations%2C+sensor+fusion%2C+hierarchical+behavior+composition%2C+situation+recognition+and+temporal+sequencing.+This+series+of+experiments+comprises+the+steps+in+the+incremental+development+of+a+complex+robot+program.+The+advantages+and+drawbacks+of+this+approach+are+discussed+along+with+these+different+experiments+and+summed+up+as+a+conclusion.+These+different+robotics+programs+may+be+seen+as+an+illustration+of+probabilistic+programming+applicable+whenever+one+must+deal+with+problems+based+on+uncertain+or+incomplete+knowledge.+The+scope+of+possible+applications+is+obviously+much+broader+than+robotics.&rft.date=2000&rft.type=Departmental+Technical+Report&rft.type=NonPeerReviewed&rft.format=application%2Fpostscript&rft.identifier=http%3A%2F%2Fcogprints.org%2F1670%2F1%2FLebeltel2000.ps&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F1670%2F5%2FLebeltel2000.pdf&rft.identifier=++Lebeltel%2C+Olivier+and+Bessiere%2C+Pierre+and+Diard%2C+Julien+and+Mazer%2C+Emmanuel++(2000)+Bayesian+robot+Programming.++%5BDepartmental+Technical+Report%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F1670%2F