Recovering from errors during programming by demonstration Export

@inproceedings{chenWeld2008:recovering-pbd, abstract = {Many end-users wish to customize their applications, automating common tasks and routines. Unfortunately, this automation is difficult today --- users must choose between brittle macros and complex scripting languages. Programming by demonstration (PBD) offers a middle ground, allowing users to demonstrate a procedure multiple times and generalizing the requisite behavior with machine learning. Unfortunately, many PBD systems are almost as brittle as macro recorders, offering few ways for a user to control the learning process or correct the demonstrations used as training examples. This paper presents CHINLE, a system which automatically constructs PBD systems for applications based on their interface specification. The resulting PBD systems have novel interaction and visualization methods, which allow the user to easily monitor and guide the learning process, facilitating error recovery during training. CHINLE-constructed PBD systems learn procedures with conditionals and perform partial learning if the procedure is too complex to learn completely.}, address = {New York, NY, USA}, author = {Chen, Jiun H. and Weld, Daniel S.}, booktitle = {IUI '08: Proceedings of the 13th international conference on Intelligent user interfaces}, citeulike-article-id = {4295490}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1378794}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1378773.1378794}, doi = {10.1145/1378773.1378794}, isbn = {978-1-59593-987-6}, keywords = {ai, algorithms, iui08, pbd, version\_spaces}, location = {Gran Canaria, Spain}, pages = {159--168}, posted-at = {2009-04-09 22:49:21}, priority = {2}, publisher = {ACM}, title = {Recovering from errors during programming by demonstration}, url = {http://dx.doi.org/10.1145/1378773.1378794}, year = {2008} }

TY - CONF ID - chenWeld2008:recovering-pbd L3 - citeulike-article-id:4295490 N2 - Many end-users wish to customize their applications, automating common tasks and routines. Unfortunately, this automation is difficult today --- users must choose between brittle macros and complex scripting languages. Programming by demonstration (PBD) offers a middle ground, allowing users to demonstrate a procedure multiple times and generalizing the requisite behavior with machine learning. Unfortunately, many PBD systems are almost as brittle as macro recorders, offering few ways for a user to control the learning process or correct the demonstrations used as training examples. This paper presents CHINLE, a system which automatically constructs PBD systems for applications based on their interface specification. The resulting PBD systems have novel interaction and visualization methods, which allow the user to easily monitor and guide the learning process, facilitating error recovery during training. CHINLE-constructed PBD systems learn procedures with conditionals and perform partial learning if the procedure is too complex to learn completely. CY - Gran Canaria, Spain SN - 978-1-59593-987-6 AD - New York, NY, USA EP - 168 TI - Recovering from errors during programming by demonstration PB - ACM T2 - IUI '08: Proceedings of the 13th international conference on Intelligent user interfaces SP - 159 KW - ai KW - algorithms KW - iui08 KW - pbd KW - version_spaces AU - Chen, Jiun AU - Weld, Daniel PY - 2008/// UR - http://dx.doi.org/10.1145/1378773.1378794 ER -