The trembling-hand problem for LTLf planning

Consider an agent acting to achieve its temporal goal, but with a "trembling hand". In this case, the agent may mistakenly instruct, with a certain (typically small) probability, actions that are not intended due to faults or imprecision in its action selection mechanism, thereby leading t...

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Principais autores: Yu, P, Zhu, S, De Giacomo, G, Vardi, M
Formato: Conference item
Idioma:English
Publicado em: International Joint Conference on Artificial Intelligence 2024
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author Yu, P
Zhu, S
De Giacomo, G
Vardi, M
author_facet Yu, P
Zhu, S
De Giacomo, G
Vardi, M
author_sort Yu, P
collection OXFORD
description Consider an agent acting to achieve its temporal goal, but with a "trembling hand". In this case, the agent may mistakenly instruct, with a certain (typically small) probability, actions that are not intended due to faults or imprecision in its action selection mechanism, thereby leading to possible goal failure. We study the trembling-hand problem in the context of reasoning about actions and planning for temporally extended goals expressed in Linear Temporal Logic on finite traces (LTLf), where we want to synthesize a strategy (aka plan) that maximizes the probability of satisfying the LTLf goal in spite of the trembling hand. We consider both deterministic and nondeterministic (adversarial) domains. We propose solution techniques for both cases by relying respectively on Markov Decision Processes and on Markov Decision Processes with Set-valued Transitions with LTLf objectives, where the set-valued probabilistic transitions capture both the nondeterminism from the environment and the possible action instruction errors from the agent. We formally show the correctness of our solution techniques and demonstrate their effectiveness experimentally through a proof-of-concept implementation.
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spelling oxford-uuid:cd89eb3a-4265-4e6d-a0e2-db6ee01f25ea2024-12-10T12:46:00ZThe trembling-hand problem for LTLf planningConference itemhttp://purl.org/coar/resource_type/c_5794uuid:cd89eb3a-4265-4e6d-a0e2-db6ee01f25eaEnglishSymplectic ElementsInternational Joint Conference on Artificial Intelligence2024Yu, PZhu, SDe Giacomo, GVardi, MConsider an agent acting to achieve its temporal goal, but with a "trembling hand". In this case, the agent may mistakenly instruct, with a certain (typically small) probability, actions that are not intended due to faults or imprecision in its action selection mechanism, thereby leading to possible goal failure. We study the trembling-hand problem in the context of reasoning about actions and planning for temporally extended goals expressed in Linear Temporal Logic on finite traces (LTLf), where we want to synthesize a strategy (aka plan) that maximizes the probability of satisfying the LTLf goal in spite of the trembling hand. We consider both deterministic and nondeterministic (adversarial) domains. We propose solution techniques for both cases by relying respectively on Markov Decision Processes and on Markov Decision Processes with Set-valued Transitions with LTLf objectives, where the set-valued probabilistic transitions capture both the nondeterminism from the environment and the possible action instruction errors from the agent. We formally show the correctness of our solution techniques and demonstrate their effectiveness experimentally through a proof-of-concept implementation.
spellingShingle Yu, P
Zhu, S
De Giacomo, G
Vardi, M
The trembling-hand problem for LTLf planning
title The trembling-hand problem for LTLf planning
title_full The trembling-hand problem for LTLf planning
title_fullStr The trembling-hand problem for LTLf planning
title_full_unstemmed The trembling-hand problem for LTLf planning
title_short The trembling-hand problem for LTLf planning
title_sort trembling hand problem for ltlf planning
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