On the Robustness of Domain-Independent Planning Engines: The Impact of Poorly-Engineered Knowledge

Mauro Vallati, Lukáš Chrpa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent advances in automated planning are leading towards the use of planning engines in a wide range of real-world applications. As the exploitation of planning techniques in applications increases, it
becomes imperative to assess the robustness of planning engines with regards to poorly-engineered (or maliciously modified) knowledge models provided as input for the reasoning process. In this work, to understand the impact of poorly-engineered knowledge on planning engines, we consider the perspective of a hypothetical attacker that is interested in subtly manipulating such knowledge to introduce unnecessary overheads that consequently slow down the planning process. This narrative ploy allows us to describe different types of knowledge engineering issues that cannot be detected via validation of the models, and to measure their impact on the performance of a range of planning engines exploiting very different approaches for steps like pre-processing and search.
LanguageEnglish
Title of host publicationProceedings of the 10th ACM International Conference on Knowledge Capture (K-CAP 2019)
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
Publication statusAccepted/In press - 4 Sep 2019
Event10th International Conference on Knowledge Capture - California, United States
Duration: 19 Nov 201921 Nov 2019
http://www.k-cap.org/2019/index.html

Conference

Conference10th International Conference on Knowledge Capture
Abbreviated titleK-CAP 2019
CountryUnited States
CityCalifornia
Period19/11/1921/11/19
Internet address

Fingerprint

Engines
Planning
Knowledge engineering
Processing

Cite this

Vallati, M., & Chrpa, L. (Accepted/In press). On the Robustness of Domain-Independent Planning Engines: The Impact of Poorly-Engineered Knowledge. In Proceedings of the 10th ACM International Conference on Knowledge Capture (K-CAP 2019) Association for Computing Machinery (ACM).
Vallati, Mauro ; Chrpa, Lukáš. / On the Robustness of Domain-Independent Planning Engines : The Impact of Poorly-Engineered Knowledge. Proceedings of the 10th ACM International Conference on Knowledge Capture (K-CAP 2019). Association for Computing Machinery (ACM), 2019.
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abstract = "Recent advances in automated planning are leading towards the use of planning engines in a wide range of real-world applications. As the exploitation of planning techniques in applications increases, itbecomes imperative to assess the robustness of planning engines with regards to poorly-engineered (or maliciously modified) knowledge models provided as input for the reasoning process. In this work, to understand the impact of poorly-engineered knowledge on planning engines, we consider the perspective of a hypothetical attacker that is interested in subtly manipulating such knowledge to introduce unnecessary overheads that consequently slow down the planning process. This narrative ploy allows us to describe different types of knowledge engineering issues that cannot be detected via validation of the models, and to measure their impact on the performance of a range of planning engines exploiting very different approaches for steps like pre-processing and search.",
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Vallati, M & Chrpa, L 2019, On the Robustness of Domain-Independent Planning Engines: The Impact of Poorly-Engineered Knowledge. in Proceedings of the 10th ACM International Conference on Knowledge Capture (K-CAP 2019). Association for Computing Machinery (ACM), 10th International Conference on Knowledge Capture, California, United States, 19/11/19.

On the Robustness of Domain-Independent Planning Engines : The Impact of Poorly-Engineered Knowledge. / Vallati, Mauro; Chrpa, Lukáš.

Proceedings of the 10th ACM International Conference on Knowledge Capture (K-CAP 2019). Association for Computing Machinery (ACM), 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - On the Robustness of Domain-Independent Planning Engines

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AU - Chrpa, Lukáš

PY - 2019/9/4

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N2 - Recent advances in automated planning are leading towards the use of planning engines in a wide range of real-world applications. As the exploitation of planning techniques in applications increases, itbecomes imperative to assess the robustness of planning engines with regards to poorly-engineered (or maliciously modified) knowledge models provided as input for the reasoning process. In this work, to understand the impact of poorly-engineered knowledge on planning engines, we consider the perspective of a hypothetical attacker that is interested in subtly manipulating such knowledge to introduce unnecessary overheads that consequently slow down the planning process. This narrative ploy allows us to describe different types of knowledge engineering issues that cannot be detected via validation of the models, and to measure their impact on the performance of a range of planning engines exploiting very different approaches for steps like pre-processing and search.

AB - Recent advances in automated planning are leading towards the use of planning engines in a wide range of real-world applications. As the exploitation of planning techniques in applications increases, itbecomes imperative to assess the robustness of planning engines with regards to poorly-engineered (or maliciously modified) knowledge models provided as input for the reasoning process. In this work, to understand the impact of poorly-engineered knowledge on planning engines, we consider the perspective of a hypothetical attacker that is interested in subtly manipulating such knowledge to introduce unnecessary overheads that consequently slow down the planning process. This narrative ploy allows us to describe different types of knowledge engineering issues that cannot be detected via validation of the models, and to measure their impact on the performance of a range of planning engines exploiting very different approaches for steps like pre-processing and search.

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KW - Knowledge Engineering

KW - Domain Model

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Vallati M, Chrpa L. On the Robustness of Domain-Independent Planning Engines: The Impact of Poorly-Engineered Knowledge. In Proceedings of the 10th ACM International Conference on Knowledge Capture (K-CAP 2019). Association for Computing Machinery (ACM). 2019