Towards a Cyber-physical Systems Resilience Approach based on Artificial Emotions and Multi-agent Systems

Abstract

The concept of resilience is popular and studied in different domains like human and social sciences (psychology, psychiatry, sociology etc.) and recently in cognitive science, biology, ecology and computer science. The objective of this article is to present our research that aims to incorporate knowledge from human and social sciences in computer science to solve cyber-physical systems resilience problems. For us humans, emotions are considered as an important process in responding to unanticipated events, for that, emotions are important for our resilience. Our work aims to inspire from emotional processes to create an agent model that increases the resilience of cyber-physical systems. This agent model will integrate individual and collective processes. In addition, one of our principal hypotheses in our research is that the multi-agent paradigm is suitable to integrate emotion-like processes into cyber-physical systems.

Emotional Processes for Cyber-Physical Systems Resilience

“…We evaluated the ability of the multi-agent system approach and emotional processes to enhance CPSs with resilience-related properties. Based on this study, we estimated the correspondence between resilience phases features with MAS properties and resilience phases with emotional processes as in [9], [10]. The decision-making architecture presented in Section IV is based on this evaluation.…”

“…The main objective of this work is to provide cyberphysical systems with functionalities and processes that make them more resilient. To do this, we have adopted the multiagent paradigm and aim at transposing knowledge from the psychology of emotions to artificial systems [10].…”

Secure Consensus via Objective Coding: Robustness Analysis to Channel Tampering

“…Lastly, it is worth noticing that the second inequality in ( 21) can be generalized for any admissible δ θ α , with |δ θ α | < K −1 α α, so that any δ θ K P such that |δ θ K P | < K −1 K P K P be acceptable, implying that any self-loop value α > 0 contributes to increase robust agreement. 3 Note that nominal dynamics ( 13) can be obtained from ( 17) when δ θ α = 0, Fig. 2: Numerical results obtained from the application of the SBDC approach to the DPIA.…”

“…Since the maximum attainable node degree d M = max i∈{1,...,n} |N i (t)| in G α over time is d M = 3, one has Ψ Gα = d M α = 3α and, according to (28), inequality Ψ Gα < (t) −1 can be reduced to Ψ Gα < −1 , yielding the design constraint α ∈ (0, 1/3) ⊂ Q α . Assuming, once again, that edge (3,4) is subject to an attack δ θ 34 , parameter α can be selected to maximize the r.h.s. of guarantee (33), by imposing 1 − 3α = 4α/3 and obtaining α = 3/13 ∈ (0, 1/3).…”

“…Robustness refers to the ability of a system to tolerate the failure of one or more agents, while scalability originates from system modularity. Because of these advantages, networked architectures based on MASs have become popular in several cutting-edge research areas as the Internet-of-Things [2] and Cyber-Physical Systems [3]. As stated in [4], within such networks of agents, "consensus" means to reach an agreement w.r.t.…”