Logical and mathematical interpretation of decisions of intelligent agents

Modern cybersecurity systems are faced with increasingly complex network architectures and a growing diversity of attack vectors. In this context, the ability of intelligent systems not only to effectively detect threats but also to rationalize their decisions is becoming increasingly important.

Аim. The work is to develop and experimentally verify a model of an RL agent capable of making decisions in a network environment, interpreted in terms of temporal and epistemic logic.

Results. This paper presents a formal approach to developing explainable reinforcement learning (XRL) for cybersecurity problems. This approach includes developing a mathematical model of an intelligent agent capable of detecting anomalies in network traffic and making decisions under uncertainty. A method for interpreting agent strategies is proposed, based on the use of linear temporal logic (LTL) and epistemic logic (EL), which ensures transparency, formal verifiability, and explainability of system behavior. It is demonstrated that the logical and mathematical interpretation of learned policies enables a transition from empirical dependencies to formalizable properties of security, liveness, and causality, thereby increasing the trust and reliability of cybersecurity systems. A computational experiment confirms the effectiveness of the proposed approach: anomaly detection accuracy reaches 94–96%, and the average response latency is less than 0.3 seconds.

Conclusion. The obtained results demonstrate the model'shigh applicability for constructing explainable, verifiable, and resilient cybersecurity systems, and also demonstrate that logical interpretation of strategies contributes to increased decision transparency and strengthens trust in intelligent information security systems. The experiment demonstrate that the agent is capable of achieving high threat detection accuracy with short response times, and the resulting logical formulas successfully pass specification feasibility checks. This confirms that logical interpretation of strategies increases the transparency and trust in the decisions of intelligent systems.