TY - GEN
T1 - Online probabilistic goal recognition over nominal models
AU - Pereira, Ramon Fraga
AU - Vered, Mor
AU - Meneguzzi, Felipe
AU - Ramírez, Miquel
N1 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85074907219&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2019/770
DO - 10.24963/ijcai.2019/770
M3 - Published conference contribution
AN - SCOPUS:85074907219
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 5547
EP - 5553
BT - Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
A2 - Kraus, Sarit
PB - International Joint Conferences on Artificial Intelligence Organization
T2 - 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Y2 - 10 August 2019 through 16 August 2019
ER -