Hyperproperties for Time Window Temporal Logic (HyperTWTL) is a domain-specific formal specification language known for its effectiveness in compactly representing security, opacity, and concurrency properties for robotics applications. This paper focuses on HyperTWTL-constrained secure reinforcement learning (SecRL). Although temporal logic-constrained safe reinforcement learning (SRL) is an evolving research problem with several existing literature, there is a significant research gap in exploring security-aware reinforcement learning (RL) using hyperproperties. Given the dynamics of an agent as a Markov Decision Process (MDP) and opacity/security constraints formalized as HyperTWTL, we propose an approach for learning security-aware optimal policies using dynamic Boltzmann softmax RL while satisfying the HyperTWTL constraints. The effectiveness and scalability of our proposed approach are demonstrated using a pick-up and delivery robotic mission case study. We also compare our results with two other baseline RL algorithms, showing that our proposed method outperforms them.

Insurance claims processing involves multi-domain entities and multi-source data, along with a number of human-agent interactions. Use of Blockchain technology-based platform can significantly improve scalability and response time for processing of claims which are otherwise manually-intensive and time-consuming. However, the chaincodes involved within the processes that issue claims, approve or deny them as required, need to be formally verified to ensure secure and reliable processing of transactions in Blockchain. In this paper, we use a formal modeling approach to verify various processes and their underlying chaincodes relating to different stages in insurance claims processing viz., issuance, approval, denial, and flagging for fraud investigation by using linear temporal logic (LTL). We simulate the formalism on the chaincodes and analyze the breach of chaincodes via model checking.

Multi-leader comprehensive learning particle swarm optimiser possesses strong exploitation ability, by randomly selecting and assigning best-ranked particles as leaders during optimisation. However, it lacks the ability to preserve diversity by mainly focusing on exploitation, and adopting random selection to choose leaders also hinders its performance. To overcome these deficiencies, an improved multi-leader comprehensive learning particle swarm optimiser is proposed based on Karush-Kuhn-Tucker proximity measure and Gravitational Search Algorithm. Karush-Kuhn-Tucker proximity measure is employed to determine the best-ranked particles’ contribution to the swarm’s convergence to influence their selection as guides for other particles. Gravitational Search Algorithm is introduced to preserve the algorithm’s ability to maintain diversity. To curb premature convergence and particles getting trapped in a local optimum, an adaptive reset velocity strategy is incorporated to activate stagnated particles. Some benchmark test functions are employed to compare the proposed algorithm with seven other peer algorithms. The results verify that our proposed algorithm possesses a better capability to elude local optima with faster convergence than other algorithms. Furthermore, to prove the efficacy of the application of our proposed algorithm in real-life, the algorithms are used to train a Feedforward neural network for epilepsy detection, of which our proposed algorithm outperforms the others.