# Green Resilience of Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2311.05201 · HTML: https://mlsanity.com/paper/2311.05201/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [No-regret learning and mixed Nash equilibria: They do not mix](https://mlsanity.com/paper/2010.09514/) (2020)
- [Collaborative Artificial Intelligence Needs Stronger Assurances Driven by Risks](https://mlsanity.com/paper/2112.00740/) (2021)
- [Contract-based Hierarchical Resilience Management for Cyber-Physical Systems](https://mlsanity.com/paper/2004.04441/) (2020)
- [Automatic Generation of Hierarchical Contracts for Resilience in Cyber-Physical Systems](https://mlsanity.com/paper/2004.05761/) (2020)

## Abstract (arXiv)

Cyber-Physical System (CPS) represents systems that join both hardware and software components to perform real-time services. Maintaining the system's reliability is critical to the continuous delivery of these services. However, the CPS running environment is full of uncertainties and can easily lead to performance degradation. As a result, the need for a recovery technique is highly needed to achieve resilience in the system, with keeping in mind that this technique should be as green as possible. This early doctorate proposal, suggests a game theory solution to achieve resilience and green in CPS. Game theory has been known for its fast performance in decision-making, helping the system to choose what maximizes its payoffs. The proposed game model is described over a real-life collaborative artificial intelligence system (CAIS), that involves robots with humans to achieve a common goal. It shows how the expected results of the system will achieve the resilience of CAIS with minimized CO2 footprint.