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 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 |
Editors | Sarit Kraus |
Publisher | International Joint Conferences on Artificial Intelligence Organization |
Pages | 5547-5553 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241141 |
DOIs | |
Publication status | Published - 2019 |
Event | 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China Duration: 10 Aug 2019 → 16 Aug 2019 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Volume | 2019-August |
ISSN (Print) | 1045-0823 |
Conference
Conference | 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 |
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Country/Territory | China |
City | Macao |
Period | 10/08/19 → 16/08/19 |
Bibliographical note
Funding Information:We want to thank Prof. Judea Pearl and Prof. Benjamin Recht for their lively exchanges on Twitter which have provided significant inspiration to write this paper. We also thank João Paulo Aires for the invaluable discussions about DNNs. This material is based upon work partially supported by the Australian DST Group, ID8332. This work is also financed by the Coordenac¸ão de Aperfeic¸oamento de Pessoal de Nivel Superior (Brazil, Finance Code 001). Felipe acknowledges support from CNPq under project numbers 407058/2018-4 and 305969/2016-1.
Publisher Copyright:
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.