Abstract
Recent approaches to goal recognition have progressively relaxed the assumptions about the amount and correctness of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume completeness and correctness of the domain theory against which their algorithms match observations: this is too strong for most real-world domains. In this paper, we develop goal recognition techniques that are capable of recognizing goals using incomplete domain theories by considering different notions of planning landmarks in such domains. We evaluate the resulting techniques empirically in a large dataset of incomplete domains, and perform an ablation study to understand their effect on recognition performance.
| Original language | English |
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| Title of host publication | Proceedings of the 29th International Conference on Automated Planning and Scheduling, ICAPS 2019 |
| Editors | J. Benton, Nir Lipovetzky, Eva Onaindia, David E. Smith, Siddharth Srivastava |
| Publisher | AAAI Press |
| Pages | 329-337 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781577358077 |
| Publication status | Published - 2019 |
| Event | 29th International Conference on Automated Planning and Scheduling, ICAPS 2019 - Berkeley, United States Duration: 11 Jul 2019 → 15 Jul 2019 |
Conference
| Conference | 29th International Conference on Automated Planning and Scheduling, ICAPS 2019 |
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| Country/Territory | United States |
| City | Berkeley |
| Period | 11/07/19 → 15/07/19 |
Bibliographical note
Funding Information:We thank Miquel Ramírez for the invaluable discussions about previous versions of this paper. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001. Felipe acknowledges support from CNPq under project numbers 407058/2018-4 and 305969/2016-1.
Publisher Copyright:
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.