Abstract
We introduce a novel framework for runtime enforcement of safe executions in component-based systems with multi-party interactions modeled using BIP. Our technique frames runtime enforcement as a sequential decision making problem and presents two alternatives for learning optimal strategies that ensure fairness between correct traces. We target both finite and infinite state-spaces. In the finite case, we guarantee that the system avoids bad-states by casting the learning process as a one of determining a fixed point solution that converges to the optimal strategy. Though successful, this technique fails to generalize to the infinite case due to need for building a dictionary, which quantifies the performance of each state-interaction pair. As such, we further contribute by generalizing our framework to support the infinite setting. Here, we adapt ideas from function approximators and machine learning to encode each state-interaction pairs’ performance. In essence, we autonomously learn to abstract similar performing states in a relevant continuous space through the usage of deep learning. We assess our method empirically by presenting a fully implemented tool, so called RERL. Particularly, we use RERL to: (1) enforce deadlock freedom on a dining philosophers benchmark, and (2) allow for pair-wise synchronized robots to autonomously achieve consensus within a cooperative multi-agent setting.
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Notes
- 1.
In practice, a bias term is added to increase the expressiveness of the functions learnt by the NN.
- 2.
Note that \(\mathbb {P}\) is indexed by \({\varvec{\varTheta }}\) as its output depends on \({\varvec{\varTheta }}\).
- 3.
One epoch consists of one full training cycle on the training set.
- 4.
We use fine-tuning technique to select these parameters.
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Bou-Ammar, H., Jaber, M., Nassar, M. (2017). Correctness-by-Learning of Infinite-State Component-Based Systems. In: Proença, J., Lumpe, M. (eds) Formal Aspects of Component Software. FACS 2017. Lecture Notes in Computer Science(), vol 10487. Springer, Cham. https://doi.org/10.1007/978-3-319-68034-7_10
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