Discounting in LTL
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Abstract (arXiv)
In recent years, there is growing need and interest in formalizing and reasoning about the quality of software and hardware systems. As opposed to traditional verification, where one handles the question of whether a system satisfies, or not, a given specification, reasoning about quality addresses the question of how well the system satisfies the specification. One direction in this effort is to refine the "eventually" operators of temporal logic to em discounting operators: the satisfaction value of a specification is a value in [0,1], where the longer it takes to fulfill eventuality requirements, the smaller the satisfaction value is. In this paper we introduce an augmentation by discounting of Linear Temporal Logic (LTL), and study it, as well as its combination with propositional quality operators. We show that one can augment LTL with an arbitrary set of discounting functions, while preserving the decidability of the model-checking problem. Further augmenting the logic with unary propositional quality operators preserves decidability, whereas adding an average-operator makes some problems undecidable. We also discuss the complexity of the problem, as well as various extensions.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jun 2014.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 1; 2025: 1.
6 papers.
- 2024DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RLcited by 20
- 2023Policy Synthesis and Reinforcement Learning for Discounted LTLcited by 14
- 2021Controller Synthesis for Omega-Regular and Steady-State Specificationscited by 2
- 2023Reinforcement Learning with Depreciating Assetscited by 2
- 2023Discounting in Strategy Logiccited by 0
- 2025Efficient Dynamic Shielding for Parametric Safety Specificationscited by 0
- 2025Efficient Dynamic Shielding for Parametric Safety Specifications28 May 2025
- 2024DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL6 Oct 2024
- 2023Policy Synthesis and Reinforcement Learning for Discounted LTL26 May 2023
- 2023Discounting in Strategy Logic24 May 2023
- 2023Reinforcement Learning with Depreciating Assets27 Feb 2023
- 2021Controller Synthesis for Omega-Regular and Steady-State Specifications5 Jun 2021