Actions you can handle: dependent types for AI plans
Actions you can handle: dependent types for AI plans
Verification of AI is a challenge that has engineering, algorithmic and programming language components. For example, AI planners are deployed to model actions of autonomous agents. They comprise a number of searching algorithms that, given a set of specified properties, find a sequence of actions that satisfy these properties. Although AI planners are mature tools from the algorithmic and engineering points of view, they have limitations as programming languages. Decidable and efficient automated search entails restrictions on the syntax of the language, prohibiting use of higher-order properties or recursion. This paper proposes a methodology for embedding plans produced by AI planners into the dependently-typed language Agda, which enables users to reason about and verify more general and abstract properties of plans, and also provides a more holistic programming language infrastructure for modelling plan execution.
AI Planners, Dependent Types, Verification
1-13
Association for Computing Machinery
Hill, Alasdair
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Komendantskaya, Ekaterina
f12d9c23-5589-40b8-bcf9-a04fe9dedf61
Daggitt, Matthew L.
7788a0b1-f07e-4b37-b34a-77b7d6ad4005
Petrick, Ronald P.A.
031c87fc-f916-4e61-a77e-7002e5f53ad1
18 August 2021
Hill, Alasdair
e7a0d7d4-3106-4ab3-94bf-9fc8702bf42e
Komendantskaya, Ekaterina
f12d9c23-5589-40b8-bcf9-a04fe9dedf61
Daggitt, Matthew L.
7788a0b1-f07e-4b37-b34a-77b7d6ad4005
Petrick, Ronald P.A.
031c87fc-f916-4e61-a77e-7002e5f53ad1
Hill, Alasdair, Komendantskaya, Ekaterina, Daggitt, Matthew L. and Petrick, Ronald P.A.
(2021)
Actions you can handle: dependent types for AI plans.
Ko, Hsiang-Shang and Orchard, Dominic
(eds.)
In TyDe 2021 - Proceedings of the 6th ACM SIGPLAN International Workshop on Type-Driven Development, co-located with ICFP 2021.
Association for Computing Machinery.
.
(doi:10.1145/3471875.3472990).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Verification of AI is a challenge that has engineering, algorithmic and programming language components. For example, AI planners are deployed to model actions of autonomous agents. They comprise a number of searching algorithms that, given a set of specified properties, find a sequence of actions that satisfy these properties. Although AI planners are mature tools from the algorithmic and engineering points of view, they have limitations as programming languages. Decidable and efficient automated search entails restrictions on the syntax of the language, prohibiting use of higher-order properties or recursion. This paper proposes a methodology for embedding plans produced by AI planners into the dependently-typed language Agda, which enables users to reason about and verify more general and abstract properties of plans, and also provides a more holistic programming language infrastructure for modelling plan execution.
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Published date: 18 August 2021
Additional Information:
Funding Information:
The first author acknowledges support of the EPSRC Doctoral Training scheme; the second and third author acknowledge generous support of the EPSRC grant AISEC: AI Secure and Explainable by Construction, EP/T026952/1, https: //www.macs.hw.ac.uk/aisec/. The authors also thank the TyDe’21 reviewers for their valuable input, which lead to substantial clarification and simplifaction of some of the code accompanying this paper.
Publisher Copyright:
© 2021 ACM.
Venue - Dates:
6th ACM SIGPLAN International Workshop on Type-Driven Development, TyDe 2021, co-located with the International Conference on Functional Programming, ICFP 2021, , Virtual, Online, Korea, Republic of, 2021-08-22
Keywords:
AI Planners, Dependent Types, Verification
Identifiers
Local EPrints ID: 482778
URI: http://eprints.soton.ac.uk/id/eprint/482778
PURE UUID: 0bafce00-d613-4273-a883-f6e544d4c15d
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Date deposited: 12 Oct 2023 16:43
Last modified: 17 Mar 2024 13:32
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Contributors
Author:
Alasdair Hill
Author:
Ekaterina Komendantskaya
Author:
Matthew L. Daggitt
Author:
Ronald P.A. Petrick
Editor:
Hsiang-Shang Ko
Editor:
Dominic Orchard
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