[2307.06399] Designing Behavior Trees from Goal-Oriented LTLf Formulas
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Computer Science > Artificial Intelligence

arXiv:2307.06399 (cs)
[Submitted on 12 Jul 2023 (v1), last revised 19 Dec 2023 (this version, v2)]

Title:Designing Behavior Trees from Goal-Oriented LTLf Formulas

Authors:Aadesh Neupane, Eric G Mercer, Michael A. Goodrich
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Abstract:Temporal logic can be used to formally specify autonomous agent goals, but synthesizing planners that guarantee goal satisfaction can be computationally prohibitive. This paper shows how to turn goals specified using a subset of finite trace Linear Temporal Logic (LTL) into a behavior tree (BT) that guarantees that successful traces satisfy the LTL goal. Useful LTL formulas for achievement goals can be derived using achievement-oriented task mission grammars, leading to missions made up of tasks combined using LTL operators. Constructing BTs from LTL formulas leads to a relaxed behavior synthesis problem in which a wide range of planners can implement the action nodes in the BT. Importantly, any successful trace induced by the planners satisfies the corresponding LTL formula. The usefulness of the approach is demonstrated in two ways: a) exploring the alignment between two planners and LTL goals, and b) solving a sequential key-door problem for a Fetch robot.
Comments: Accepted as "Most Visionary Paper" in Autonomous Robots and Multirobot Systems (ARMS) 2023 workshop affiliated with the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2307.06399 [cs.AI]
  (or arXiv:2307.06399v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2307.06399

Submission history

From: Aadesh Neupane [view email]
[v1] Wed, 12 Jul 2023 18:29:37 UTC (763 KB)
[v2] Tue, 19 Dec 2023 16:11:05 UTC (763 KB)
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