Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models

Ramon Fraga Pereira; Andre Grahl Pereira; Felipe Meneguzzi

doi:10.1609/icaps.v29i1.3495

Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models

Ramon Fraga Pereira, Andre Grahl Pereira, Felipe Meneguzzi

Autonomy and Verification

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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
Title of host publication	Proceedings of the International Conference on Automated Planning and Scheduling
Editors	J. Benton, Nir Lipovetzky, Eva Onaindia, David E. Smith, Siddharth Srivastava
Place of Publication	Washington DC
Publisher	AAAI Press
Pages	329-337
Number of pages	9
ISBN (Print)	9781577358077
DOIs	https://doi.org/10.1609/icaps.v29i1.3495
Publication status	Published - 5 Jul 2019
Event	29th International Conference on Automated Planning and Scheduling - Berkeley, CA, United States Duration: 11 Jul 2019 → 15 Jul 2019 https://icaps19.icaps-conference.org/index.html

Publication series

Name	Proceedings of the International Conference on Automated Planning and Scheduling
Publisher	Association for the Advancement of Artificial Intelligence
Number	1
Volume	29
ISSN (Print)	2334-0835
ISSN (Electronic)	2334-0843

Conference

Conference	29th International Conference on Automated Planning and Scheduling
Abbreviated title	ICAPS2019
Country/Territory	United States
City	Berkeley, CA
Period	11/07/19 → 15/07/19
Internet address	https://icaps19.icaps-conference.org/index.html

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10.1609/icaps.v29i1.3495

Cite this

Pereira, R. F., Pereira, A. G., & Meneguzzi, F. (2019). Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models. In J. Benton, N. Lipovetzky, E. Onaindia, D. E. Smith, & S. Srivastava (Eds.), Proceedings of the International Conference on Automated Planning and Scheduling (pp. 329-337). (Proceedings of the International Conference on Automated Planning and Scheduling; Vol. 29, No. 1). AAAI Press. https://doi.org/10.1609/icaps.v29i1.3495

Pereira, Ramon Fraga ; Pereira, Andre Grahl ; Meneguzzi, Felipe. / Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models. Proceedings of the International Conference on Automated Planning and Scheduling. editor / J. Benton ; Nir Lipovetzky ; Eva Onaindia ; David E. Smith ; Siddharth Srivastava. Washington DC : AAAI Press, 2019. pp. 329-337 (Proceedings of the International Conference on Automated Planning and Scheduling; 1).

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title = "Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models",

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.",

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year = "2019",

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booktitle = "Proceedings of the International Conference on Automated Planning and Scheduling",

address = "United States",

note = "29th International Conference on Automated Planning and Scheduling, ICAPS2019 ; Conference date: 11-07-2019 Through 15-07-2019",

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Pereira, RF, Pereira, AG & Meneguzzi, F 2019, Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models. in J Benton, N Lipovetzky, E Onaindia, DE Smith & S Srivastava (eds), Proceedings of the International Conference on Automated Planning and Scheduling. Proceedings of the International Conference on Automated Planning and Scheduling, no. 1, vol. 29, AAAI Press, Washington DC, pp. 329-337, 29th International Conference on Automated Planning and Scheduling, Berkeley, CA, United States, 11/07/19. https://doi.org/10.1609/icaps.v29i1.3495

Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models. / Pereira, Ramon Fraga; Pereira, Andre Grahl; Meneguzzi, Felipe.
Proceedings of the International Conference on Automated Planning and Scheduling. ed. / J. Benton; Nir Lipovetzky; Eva Onaindia; David E. Smith; Siddharth Srivastava. Washington DC: AAAI Press, 2019. p. 329-337 (Proceedings of the International Conference on Automated Planning and Scheduling; Vol. 29, No. 1).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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U2 - 10.1609/icaps.v29i1.3495

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M3 - Conference contribution

SN - 9781577358077

T3 - Proceedings of the International Conference on Automated Planning and Scheduling

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BT - Proceedings of the International Conference on Automated Planning and Scheduling

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Pereira RF, Pereira AG, Meneguzzi F. Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models. In Benton J, Lipovetzky N, Onaindia E, Smith DE, Srivastava S, editors, Proceedings of the International Conference on Automated Planning and Scheduling. Washington DC: AAAI Press. 2019. p. 329-337. (Proceedings of the International Conference on Automated Planning and Scheduling; 1). doi: 10.1609/icaps.v29i1.3495

Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models

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