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Integrating Multi-Level Mixed-Criticality into MCTS for Robust Resource Management

Authors Franco Cordeiro , Samuel Tardieu , Laurent Pautet

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LITES.10.2.1.pdf
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Subject Classification

ACM Subject Classification
  • Computer systems organization → Embedded and cyber-physical systems
  • Computer systems organization → Real-time systems
  • Computing methodologies → Planning and scheduling
Keywords
  • Embedded Systems
  • Safety / Mixed-Critical Systems
  • Real-Time Systems
  • Energy Aware Systems

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Abstract

Managing actions with uncertain resource costs is a complex challenge, particularly in autonomous robot mission planning. Robots are often assigned multiple objectives with varying criticality levels, ranging from catastrophic to minor impacts, where failures can significantly affect system safety. Uncertainties in worst-case costs of resources, such as energy and operating time - the time it takes to carry out an action - further complicate mission planning and execution.
Monte Carlo Tree Search (MCTS) is a powerful tool for online planning, yet it struggles to account for uncertainty in worst-case cost estimations. Optimistic estimates risk resource shortages, while pessimistic ones lead to inefficient allocation.
The Mixed-Criticality (MC) approach, originally developed for real-time systems to schedule critical tasks by allocating processing resources under Worst-Case Execution Time (WCET) uncertainty, provides a framework of rules, models and design principles. We claim this framework can be adapted to autonomous robot mission planning, where critical objectives are met through analogous allocation of different kinds of resources such as energy and operating time despite uncertainties.
We propose enhancing MCTS with MC principles to handle uncertainty in worst-case costs across multiple resources and criticality of objectives. High-critical objectives must always be completed, regardless of resource constraints, while low-critical objectives operate flexibly, consuming resources within optimistic estimates when possible or being discarded when resources become scarce. This ensures efficient resource reallocation and prioritization of high-critical objectives.
To implement this, we present (MC)²TS, a novel variant of MCTS that integrates MC principles for dynamic resource management. It supports more than two criticality levels to ensure that the most critical components meet the most stringent safety and reliability requirements, while also enabling robust resource management. By enabling replanning and mode changes, (MC)²TS improves MCTS’s efficiency and enhances MC systems’ adaptability to both degrading and improving resource conditions.
We evaluate (MC)²TS in an active perception scenario, where a drone retrieves data from distributed sensors under unpredictable environmental conditions. (MC)²TS outperforms MCTS by achieving more objectives, adapting plans when costs drop. It explores more objective sequences, minimizes oversizing, and enhances efficiency. Balancing safety and performance, it monitors robot battery, mission and objective resource constraints such as deadlines. Its robustness ensures low-critical objectives do not compromise high-critical objectives, making it a reliable solution for complex systems characterized by uncertain resource costs and critical objectives.

Cite As Get BibTex

Franco Cordeiro, Samuel Tardieu, and Laurent Pautet. Integrating Multi-Level Mixed-Criticality into MCTS for Robust Resource Management. In LITES, Volume 10, Issue 2 (2025): Special Issue on Industrial Real-Time Systems. Leibniz Transactions on Embedded Systems, Volume 10, Issue 2, pp. 1:1-1:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LITES.10.2.1

Author Details

Franco Cordeiro
  • LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France
Samuel Tardieu
  • LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France
Laurent Pautet
  • LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France

Acknowledgements

This material is based upon work supported by the CIEDS (Interdisciplinary Centre for Defence and Security of Institut Polytechnique de Paris) and by the FARO project (Heuristic Foundations of Robot Swarms).

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