Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence |
Editors | Sarit Kraus |
Place of Publication | Marina del Rey CA USA |
Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
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/ https://www.ijcai.org/proceedings/2019/ (Proceedings) |
Conference
Conference | International Joint Conference on Artificial Intelligence 2019 |
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Abbreviated title | IJCAI 2019 |
Country/Territory | China |
City | Macao |
Period | 10/08/19 → 16/08/19 |
Internet address |
Keywords
- Goal Recognition
- Online