In this paper, a Priority-based Dynamic REsource Allocation with decentralized Multi-task assignment (P-DREAM) approach is presented to protect a territory from highly manoeuvring intruders. In the first part, static optimization problems are formulated to compute the following parameters of the perimeter defense problem; the number of reserve stations, their locations, the priority region, the monitoring region, and the minimum number of defenders required for the monitoring purpose. The concept of a prioritized intruder is proposed here to identify and handle those critical intruders (computed based on the velocity ratio and location) to be tackled on a priority basis. The computed priority region helps to assign reserve defenders sufficiently earlier such that they can neutralize the prioritized intruders. The monitoring region defines the minimum region to be monitored and is sufficient enough to handle the intruders. In the second part, the earlier developed DREAM approach is modified to incorporate the priority of an intruder. The proposed P-DREAM approach assigns the defenders to the prioritized intruders as the first task. A convex territory protection problem is simulated to illustrate the P-DREAM approach. It involves the computation of static parameters and solving the prioritized task assignments with dynamic resource allocation. Monte-Carlo results were conducted to verify the performance of P-DREAM, and the results clearly show that the P-DREAM approach can protect the territory with consistent performance against highly manoeuvring intruders.

This paper presents a Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) model for maneuver planning. Traditional rule-based maneuver planning approaches often have to improve their abilities to handle the variabilities of real-world driving scenarios. By learning from its experience, a Reinforcement Learning (RL)-based driving agent can adapt to changing driving conditions and improve its performance over time. Our proposed approach combines a predictive model and an RL agent to plan for comfortable and safe maneuvers. The predictive model is trained using historical driving data to predict the future positions of other surrounding vehicles. The surrounding vehicles' past and predicted future positions are embedded in context-aware grid maps. At the same time, the RL agent learns to make maneuvers based on this spatio-temporal context information. Performance evaluation of PMP-DRL has been carried out using simulated environments generated from publicly available NGSIM US101 and I80 datasets. The training sequence shows the continuous improvement in the driving experiences. It shows that proposed PMP-DRL can learn the trade-off between safety and comfortability. The decisions generated by the recent imitation learning-based model are compared with the proposed PMP-DRL for unseen scenarios. The results clearly show that PMP-DRL can handle complex real-world scenarios and make better comfortable and safe maneuver decisions than rule-based and imitative models.

This paper presents a novel method for information interpretability in an MC-SEFRON classifier. To develop a method to extract knowledge stored in a trained classifier, first, the binary-class SEFRON classifier developed earlier is extended to handle multi-class problems. MC-SEFRON uses the population encoding scheme to encode the real-valued input data into spike patterns. MC-SEFRON is trained using the same supervised learning rule used in the SEFRON. After training, the proposed method extracts the knowledge for a given class stored in the classifier by mapping the weighted postsynaptic potential in the time domain to the feature domain as Feature Strength Functions (FSFs). A set of FSFs corresponding to each output class represents the extracted knowledge from the classifier. This knowledge encoding method is derived to maintain consistency between the classification in the time domain and the feature domain. The correctness of the FSF is quantitatively measured by using FSF directly for classification tasks. For a given input, each FSF is sampled at the input value to obtain the corresponding feature strength value (FSV). Then the aggregated FSVs obtained for each class are used to determine the output class labels during classification. FSVs are also used to interpret the predictions during the classification task. Using ten UCI datasets and the MNIST dataset, the knowledge extraction method, interpretation and the reliability of the FSF are demonstrated. Based on the studies, it can be seen that on an average, the difference in the classification accuracies using the FSF directly and those obtained by MC-SEFRON is only around 0.9% & 0.1\% for the UCI datasets and the MNIST dataset respectively. This clearly shows that the knowledge represented by the FSFs has acceptable reliability and the interpretability of classification using the classifier's knowledge has been justified.