Multi-agent Cyber-physical Systems Research Articles

SafeMAP: Safe Multi-Agent Planning framework based on Dynamic Probabilistic Risk Assessment

This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies heterogeneous models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. The results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.

Security of Multi-Agent Cyber-Physical Systems: A Survey

Multi-agent systems are becoming increasingly popular due to their successful implementation in several sectors. However, there are a variety of threats that might undermine the agent’s security and imperil system security. As a result, security concerns should be addressed during the design of multi-agent systems. This survey reviews different models for securing multi-agent systems, which were developed based on the concepts regarding the agent’s role and communications. This paper presents and categorizes the most common attacks on MASs. Then, we study and analyze numerous security strategies in the literature, classifying them as prevention, detection, and resiliency approaches based on reputation and trust. Finally, we recommend which security approach is the best countermeasure for specific types of attacks based on recent advances in the research field.

A game-theoretic method for resilient control design in industrial multi-agent CPSs with Markovian and coupled dynamics

Multi-agent cyber-physical systems (CPSs) are ubiquitous in various modern critical infrastructures, especially in industrial automation control involved in the petrochemical, metallurgy, textile, and pharmaceutical, which are vulnerable for their hierarchically and horizontally interconnected architecture. In this paper, we investigate a resilient control design problem for industrial multi-agent CPSs of centralised information structure, with Markovian and physically coupled dynamics from a game-theoretic perspective. First, we investigate a worst-case design problem and solve for worst-case disturbance strategy and optimal disturbance attenuation level parameter through an equivalent two-person zero-sum differential game in both finite- and infinite-horizon. Second, we study a corresponding N-person worst-case nonzero-sum differential game and derive state-feedback Nash equilibrium solution of robustness property as resilient control strategy for each agent. The experimental case study is given to demonstrate that using resilient control strategy, each agent could maintain its physical plant operationally normal under cyber attack, malicious disturbance input and physical state interdependency.

Resilience through Learning in Multi-Agent Cyber-Physical Systems

The paper contributes to the design of secure and resilient supervisory Cyber-Physical Systems (CPS) through learning. The reported approach involves the inclusion of learning modules in each of the supervised agents, and considers a scenario where the system's coordinator privately transmits to individual agents their action plans in the form of symbolic strings. Each agent's plans belong in some particular class of (subregular) languages which is identifiable in the limit from positive data. With knowledge of the class of languages their plans belong to, agents can observe their coordinator's instructions and utilize the Grammatical Inference modules to identify the behavior specified for them. From that, they can then work collectively to infer the executive function of their supervisor. The paper proves that in cases where the coordinator fails, or communication to subordinates is disrupted, agents are able not only to maintain functional capacity but also to recover normalcy of operation by reconstructing their coordinator. Guaranteeing normalcy recovery in supervisory CPSs is critical in cases of a catastrophic failure or malicious attack, and is important for the design of next-generation Cyber-Physical Systems.