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Correctness-by-Learning of Infinite-State Component-Based Systems

  • Haitham Bou-Ammar
  • Mohamad Jaber
  • Mohamad Nassar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10487)

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Haitham Bou-Ammar
    • 1
  • Mohamad Jaber
    • 1
  • Mohamad Nassar
    • 1
  1. 1.American University of BeirutBeirutLebanon

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