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Proof-carrying plans: a resource logic for AI planning

Proof-carrying plans: a resource logic for AI planning
Proof-carrying plans: a resource logic for AI planning

Planning languages have been used successfully in AI for several decades. Recent trends in AI verification and Explainable AI have raised the question of whether AI planning techniques can be verified. In this paper, we present a novel resource logic, the Proof Carrying Plans (PCP) logic that can be used to verify plans produced by AI planners. The PCP logic takes inspiration from existing resource logics (such as Linear logic and Separation logic) as well as Hoare logic when it comes to modelling states and resource-aware plan execution. It also capitalises on the Curry-Howard approach to logics, in its treatment of plans as functions and plan pre- and post-conditions as types. This paper presents two main results. From the theoretical perspective, we show that the PCP logic is sound relative to the standard possible world semantics used in AI planning. From the practical perspective, we present a complete Agda formalisation of the PCP logic and of its soundness proof. Moreover, we showcase the Curry-Howard, or functional, value of this implementation by supplementing it with the library that parses AI plans into Agda's proofs automatically. We provide evaluation of this library and the resulting Agda functions. Keywords: AI planning, Verification, Resource Logics, Theorem Proving, Dependent Types.

Association for Computing Machinery
Hill, Alasdair
e7a0d7d4-3106-4ab3-94bf-9fc8702bf42e
Komendantskaya, Ekaterina
f12d9c23-5589-40b8-bcf9-a04fe9dedf61
Petrick, Ronald P.A.
031c87fc-f916-4e61-a77e-7002e5f53ad1
Hill, Alasdair
e7a0d7d4-3106-4ab3-94bf-9fc8702bf42e
Komendantskaya, Ekaterina
f12d9c23-5589-40b8-bcf9-a04fe9dedf61
Petrick, Ronald P.A.
031c87fc-f916-4e61-a77e-7002e5f53ad1

Hill, Alasdair, Komendantskaya, Ekaterina and Petrick, Ronald P.A. (2020) Proof-carrying plans: a resource logic for AI planning. In Proceedings of the 22nd International Symposium on Principles and Practice of Declarative Programming, PPDP 2020 - Part of BOPL 2020 - Bologna Federated Conference on Programming Languages 2020. Association for Computing Machinery.. (doi:10.1145/3414080.3414094).

Record type: Conference or Workshop Item (Paper)

Abstract

Planning languages have been used successfully in AI for several decades. Recent trends in AI verification and Explainable AI have raised the question of whether AI planning techniques can be verified. In this paper, we present a novel resource logic, the Proof Carrying Plans (PCP) logic that can be used to verify plans produced by AI planners. The PCP logic takes inspiration from existing resource logics (such as Linear logic and Separation logic) as well as Hoare logic when it comes to modelling states and resource-aware plan execution. It also capitalises on the Curry-Howard approach to logics, in its treatment of plans as functions and plan pre- and post-conditions as types. This paper presents two main results. From the theoretical perspective, we show that the PCP logic is sound relative to the standard possible world semantics used in AI planning. From the practical perspective, we present a complete Agda formalisation of the PCP logic and of its soundness proof. Moreover, we showcase the Curry-Howard, or functional, value of this implementation by supplementing it with the library that parses AI plans into Agda's proofs automatically. We provide evaluation of this library and the resulting Agda functions. Keywords: AI planning, Verification, Resource Logics, Theorem Proving, Dependent Types.

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More information

Published date: 8 September 2020
Additional Information: Funding Information: We thank EPSRC DTA PhD Scheme for funding the first author. Funding Information: The second author acknowledges support of the UK Nationaly Cyber Security Center grant SecCon-NN: Neural Networks with Security Contracts - towards lightweight, modular security for neural networks and the UK Research Institute in Verified Trustworthy Software Systems (VETSS)-funded research project CONVENER: Continuous Verification of Neural Networks. Publisher Copyright: © 2020 ACM.
Venue - Dates: 22nd International Symposium on Principles and Practice of Declarative Programming, PPDP 2020 - Part of 2020 Bologna Federated Conference on Programming Languages, BOPL 2020, , Bologna, Online, Italy, 2020-09-08 - 2020-09-10

Identifiers

Local EPrints ID: 482783
URI: http://eprints.soton.ac.uk/id/eprint/482783
PURE UUID: a243e646-ee74-441a-817f-2153092e6691

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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: Ronald P.A. Petrick

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