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Computer Science > Software Engineering

arXiv:2311.04569 (cs)
[Submitted on 8 Nov 2023]

Title:GResilience: Trading Off Between the Greenness and the Resilience of Collaborative AI Systems

Authors:Diaeddin Rimawi, Antonio Liotta, Marco Todescato, Barbara Russo
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Abstract:A Collaborative Artificial Intelligence System (CAIS) works with humans in a shared environment to achieve a common goal. To recover from a disruptive event that degrades its performance and ensures its resilience, a CAIS may then need to perform a set of actions either by the system, by the humans, or collaboratively together. As for any other system, recovery actions may cause energy adverse effects due to the additional required energy. Therefore, it is of paramount importance to understand which of the above actions can better trade-off between resilience and greenness. In this in-progress work, we propose an approach to automatically evaluate CAIS recovery actions for their ability to trade-off between the resilience and greenness of the system. We have also designed an experiment protocol and its application to a real CAIS demonstrator. Our approach aims to attack the problem from two perspectives: as a one-agent decision problem through optimization, which takes the decision based on the score of resilience and greenness, and as a two-agent decision problem through game theory, which takes the decision based on the payoff computed for resilience and greenness as two players of a cooperative game.
Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2311.04569 [cs.SE]
  (or arXiv:2311.04569v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2311.04569
Journal reference: Testing Software and Systems. ICTSS 2023. Lecture Notes in Computer Science, vol 14131. Springer, Cham
Related DOI: https://doi.org/10.1007/978-3-031-43240-8_18

Submission history

From: Barbara Russo [view email]
[v1] Wed, 8 Nov 2023 10:01:39 UTC (1,258 KB)
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