Abstract
This paper presents an anytime algorithm for incremental intention recognition in a changing world. The algorithm is performed by dynamically constructing the intention recognition model on top of a prior domain knowledge base. The model is occasionally reconfigured by situating itself in the changing world and removing newly found out irrelevant intentions. We also discuss some approaches to knowledge base representation for supporting situation-dependent model construction. Reconfigurable Bayesian Networks are employed to produce the intention recognition model.
| Original language | English |
|---|---|
| Title of host publication | Proactive Assistant Agents |
| Subtitle of host publication | Papers from the AAAI Fall Symposium, Technical Report |
| Editors | Felipe Meneguzzi, Jean Oh |
| Place of Publication | Menlo Park |
| Publisher | AI Access Foundation |
| Pages | 20-25 |
| Number of pages | 6 |
| ISBN (Print) | 9781577354895 |
| Publication status | Published - 2010 |
| Event | 2010 AAAI Fall Symposium - Arlington, United States Duration: 11 Nov 2010 → 13 Nov 2010 http://www.cs.cmu.edu/afs/cs/project/ita-proj-10/www/pia-2010/about/about.html |
Publication series
| Name | AAAI Fall Symposium - Technical Report |
|---|---|
| Volume | FS-10-07 |
Conference
| Conference | 2010 AAAI Fall Symposium |
|---|---|
| Country/Territory | United States |
| City | Arlington |
| Period | 11/11/10 → 13/11/10 |
| Internet address |