Online probabilistic goal recognition over nominal models

Ramon Fraga Pereira, Mor Vered, Felipe Meneguzzi, Miquel Ramírez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

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 languageEnglish
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
EditorsSarit Kraus
PublisherInternational Joint Conferences on Artificial Intelligence
Pages5547-5553
Number of pages7
ISBN (Electronic)9780999241141
DOIs
StatePublished - 2019
Externally publishedYes
Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
Duration: 10 Aug 201916 Aug 2019

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2019-August
ISSN (Print)1045-0823

Conference

Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Country/TerritoryChina
CityMacao
Period10/08/1916/08/19

Bibliographical note

Publisher Copyright:
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

Funding

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.

FundersFunder number
Australian DST GroupID8332
Coordenac¸ão de Aperfeic¸oamento de Pessoal de Nivel Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico407058/2018-4, 305969/2016-1

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