Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence |
| Editors | Sarit Kraus |
| Place of Publication | California USA |
| Publisher | International Joint Conferences on Artificial Intelligence |
| Pages | 5547-5553 |
| Number of pages | 7 |
| ISBN (Electronic) | 9780999241141 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | International Joint Conference on Artificial Intelligence 2019 - Macao, China Duration: 10 Aug 2019 → 16 Aug 2019 Conference number: 28th https://ijcai19.org/ |
Conference
| Conference | International Joint Conference on Artificial Intelligence 2019 |
|---|---|
| Abbreviated title | IJCAI-19 |
| Country | China |
| City | Macao |
| Period | 10/08/19 → 16/08/19 |
| Internet address |
Keywords
- Goal Recognition
- Online
Cite this
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Online probabilistic goal recognition over nominal models. / Pereira, Ramon Fraga; Vered, Mor; Meneguzzi, Felipe; Ramirez, Miquel.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. ed. / Sarit Kraus. California USA : International Joint Conferences on Artificial Intelligence, 2019. p. 5547-5553.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Online probabilistic goal recognition over nominal models
AU - Pereira, Ramon Fraga
AU - Vered, Mor
AU - Meneguzzi, Felipe
AU - Ramirez, Miquel
PY - 2019
Y1 - 2019
N2 - This paper revisits probabilistic, model--based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals. Existing model--based approaches rely on expert knowledge to produce symbolic descriptions of the dynamic constraints domain objects are subject to, and these are assumed to produce correct predictions. We abandon this assumption to consider the use of nominal models that are learnt from observations on transitions of systems with unknown dynamics. Leveraging existing work on the acquisition of domain models via deep learning for hybrid planning we adapt and evaluate existing goal recognition approaches to analyse how prediction errors, inherent to system dynamics identification and model learning techniques have an impact over recognition error rates.
AB - This paper revisits probabilistic, model--based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals. Existing model--based approaches rely on expert knowledge to produce symbolic descriptions of the dynamic constraints domain objects are subject to, and these are assumed to produce correct predictions. We abandon this assumption to consider the use of nominal models that are learnt from observations on transitions of systems with unknown dynamics. Leveraging existing work on the acquisition of domain models via deep learning for hybrid planning we adapt and evaluate existing goal recognition approaches to analyse how prediction errors, inherent to system dynamics identification and model learning techniques have an impact over recognition error rates.
KW - Goal Recognition
KW - Online
U2 - 10.24963/ijcai.2019/770
DO - 10.24963/ijcai.2019/770
M3 - Conference Paper
SP - 5547
EP - 5553
BT - Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
A2 - Kraus, Sarit
PB - International Joint Conferences on Artificial Intelligence
CY - California USA
ER -