# Multiple-Valued Logic Modelling for Agents Controlled via Optical Networks
## 1 Introduction
### Data Protection of Stationary Agents in Classical and Quantum Network Nodes  
As a whole, data protection methods for agents and MASs are based on the same principles as any other network nodes, but the unsatisfactory level of modern communication networks [20; 21] is reinforced by the complexity of robotic agents. The concept of a trusted network node (or server) and trust modelling [24; 25; 26] is actually for both schemes with QKD lines and for traditional data links. The basic idea of a trusted server is that all its subsystems are well-secured ones, including data storages, data channels, devices for the detection of data leakages, and systems for the monitoring of administrative processes. In any situation, data coming from a trusted node are considered as absolutely correct and reliable for every user, and typically trusted nodes are associated with the expensive networks of government organizations and big corporations, which maintain large security staff with adequate administrative procedures and human monitoring. However, even cloud services and big data centers [45; 46] can be successfully attacked. The modern interpretation of trust problems for agents dates back at least to the document published by the National Institute of Standards and Technologies of the USA (NIST) [44]. Most typical problems of illegal activity included unauthorized access and data disclosure or corruption. Main countermeasures [22; 24; 25; 37; 38; 39; 40; 42; 43] included such methods as the verification of path histories, proof carrying code, partial result encapsulation, mutual itinerary recording with optional replication or voting, execution tracing, environmental key generation, and computing with encrypted functions. Trusted modelling in MASs considers concepts of trust and reputation [47], as well as argumentation [48]. As the intrinsic idea of autonomous agents is their working without permanent human monitoring [15], then some verification procedures should be shifted onto other agents and partner network nodes. Illegal activity can then be revealed by other agents by means of the estimation of an inadequate agent's behavior, contradicting the rules used by the majority of participants [37; 38; 39]. AI algorithms imitating the work of human experts for the coordination of node activity were designed for stock trading bots [28], systems of e-commerce, and e-business [35; 36]. Efforts to raise the trust level were based on trust estimates carried out by neighboring agents [49; 50]. The practical design of collectively secured agents was represented by the smart infrastructure of an energy power grid [37; 38; 39], using smart metering and control of phasor for devices suspected in illegal activity. Such systems have combined the collective interaction of metering devices and gateways, which have involved the blockchain (BC), data integrity control, and insider attack mitigation methods. Respectively, the BC method [37; 38; 39; 51; 52] and cryptocurrencies mining [53; 54] demonstrate quite useful schemes of collective network data protection. Ledger (or registry) schemes in the BC [37; 38; 39; 53; 54] are based on the principle of linked data lists and have become the popular trend in modern computer technologies. The main idea here is that the distributed ledger of linked entries [53; 54] is supported by the decentralized consortium of users, whose integral voting guarantees the correctness of data. However, attempts to apply trust estimation to mobile robotic systems have led to the design of complicated deep-learning schemes based on pre-trained learning with supervisor and acceptance observation history [55]. As modern fiber optics lines have obtained impressive throughput parameters of 115 Tb/s for single-mode fiber and 10 Pb/s for multi-core fiber [56; 57; 58; 59], they seem to be excellent platforms for the secured distant control of robotic agents.