Name
Abstract | ||
|---|---|---|
It has long been recognized that users can have complex pref- erences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attractive idea. Unfortunately, the executed plans are often not a true representation of user preferences, as they result from the in- teraction between user preferences and feasibility constr aints. In the travel planning scenario, a user whose true preference is to travel by a plane may well be frequently observed travel- ing by car because of feasibility constraints (perhaps the u ser is a poor graduate student). In this work, we describe a novel method for learning true user preferences obfuscated by such feasibility constraints. Our base learner induces probabi lis- tic hierarchical task networks (pHTNs) from sets of training plans. Our approach is to rescale the input so that it repre- sents the user's preference distribution on plans rather th an the observed distribution on plans. |
Year | Venue | Keywords |
|---|---|---|
2009 | ICAPS | hierarchical task network |
Field | DocType | Citations |
Computer science,Artificial intelligence,Probabilistic logic,Obfuscation,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.35 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Li | 1 | 75 | 7.50 |
| William Cushing | 2 | 132 | 7.18 |
| Subbarao Kambhampati | 3 | 3453 | 450.74 |
| Sung Wook Yoon | 4 | 170 | 9.62 |