Explaining autonomy is becoming a crucial factor in the design of trustworthy autonomous platforms in both transport and smart living sectors. Interpretable reinforcement learning (RL) is an emerging research area that aims to explain why an autonomous platform adopts an action or set of actions. However, the state-of-the-art has focused on the design of explainable tools as independent modules that are not involved in the decision-making process of the RL agent. In this paper, we propose a novel belief-desire-intention RL (BDI-RL) approach that incorporates the explainable module as a belief model that enhances the learning capabilities of the RL as well as actions interpretability. To this end, we combine the merits of Dyna-Q algorithm as backbone RL model and belief maps as explainable element. The combined contribution of these models provides a robust model that emulates better the reasoning process of humans by leveraging beliefs and online agent-environment interactions. Simulations experiments are conducted in a grid environment of different sizes and obstacles. Comparisons are also provided to show the benefits of the proposed methodology.

Increased dynamic drone usage has increased complexity in aerial navigation and often demands distributed local deconfliction. Due to the high velocities and few landmarks, robust deconfliction relies on precise positioning and synchronization. However, intentional spoofing attacks aimed at inducing navigation conflicts threaten the reliability of conventional techniques. Here, we address these concerns by establishing a baseline on the impact of novel conflict-inducing spoofing attacks on existing geometric navigation methods. Based on the impact of the attacks on the navigation, reinforcement learning (RL) strategy is used to counter the effects of spoofing attacks. In order to counter the effect of spoofing in randomized dynamic airspace conditions, a zero-sum action-robust (ZSAR) RL based on mixed Nash equilibrium objective is used. The proposed methodology yields an improved number of conflict-free paths while reducing average conflicts compared to existing state of the art RL strategies, thus making it suitable for deploying autonomous aircrafts.

The increasing utilization of unmanned aerial vehicles (UAVs) in advanced air mobility (AAM) necessitates highly automated conflict resolution and collision avoidance strategies. Consequently, reinforcement learning (RL) algorithms have gained popularity in addressing conflict resolution strategies among UAVs. However, increasing digitization introduces challenges related to packet drop constraints and various adversarial cyber threats, rendering AAM fragile. Adversaries can introduce perturbations into the system states, reducing the efficacy of learning algorithms. Therefore, it is crucial to systematically investigate the impact of increased digitization, including adversarial cyber-threats and packet drop constraints to study the fragile characteristics of AAM infrastructure. This study examines the performance of artificial intelligence(AI) based path planning and conflict resolution strategies under different adversarial and stochastic packet drop constraints in UAV systems. The fragility analysis focuses on the number of conflicts, collisions and fuel consumption of the UAVs with respect to its mission, considering various adversarial attacks and packet drop constraint scenarios. The safe deep q-networks (DQN) architecture is utilized to navigate the UAVs, mitigating the adversarial threats and is benchmarked with vanilla DQN using the necessary metrics. The findings are a foundation for investigating the necessary modification of learning paradigms to develop antifragile strategies against emerging adversarial threats.

Energy consumer locations are required for framing effective energy policies. However, due to privacy concerns, it is becoming increasingly difficult to obtain the locational data of the consumers. Machine learning (ML) based classification strategies can be used to find the locational information of the consumers based on their historical energy consumption patterns. The ML methods in this paper are applied to the Residential Energy Consumption Survey 2009 dataset. In this dataset, the number of consumers in the urban area is higher than the rural area, thus making the classification problem unbalanced. The unbalanced classification problem has been solved in original and transformed or reduced feature space using Monte Carlo based under-sampling of the majority class datapoints. The hyperparameters for each classification algorithm family is represented as an optimized pipeline, obtained using the genetic programming (GP) optimizer. The classification performance metrics are then obtained for different algorithm families on the original and transformed feature spaces. Performance comparisons have been reported using univariate and bivariate distributions of the classification metrics viz. accuracy, geometric mean score (GMS), F1 score, precision, area under the curve (AUC) of receiver operator characteristics (ROC). The energy policy aspects for the urban and rural residential consumers based on the classification results have also been discussed.

This paper explores the role of AI and database management in organizational transformation using insights from Twitter data. By analyzing 30,000 English-language tweets with methods such as word analysis, topic modeling, network analysis, sentiment analysis, and emotion analysis, the study reveals a strong correlation between AI and digital transformation. The findings show positive sentiment and optimism about AI's potential. This research highlights the importance of social influence, perceived trust, and awareness in AI adoption, offering valuable insights for researchers and practitioners. Despite relying on Twitter data, the study provides practical guidance for leveraging AI in digital transformation efforts.

With COVID-19-led business disruption and businesses expediting their move towards digital transformation, Industry experts observed a phenomenon of increased adoption of Open Source Software and database management systems to digitalize the business while keeping costs low. This research has studied this phenomenon using a three-step approach: using the collective intelligence of Twitter without the context of COVID-19, Twitter data with the context of COVID-19, and empirical validation with actual monthly downloads data of open source projects from SourceForge in Pre-COVID and COVID-19 time periods. The research finds that although the COVID-19 pandemic has triggered a digital transformation and the use of database management systems in many organizations, but there is no statistically significant increased use that can be attributed to a crisis response due to the pandemic.

The load frequency control (LFC) aims to keep the frequency fluctuation of the power grids within certain specified limits, under various load disturbances. However, with the increased usage of renewable energy sources (RESs) in smart grids, it is essential to regulate the conventional power plants, based on renewable energy penetration levels. Moreover, with the decentralized nature of the control operation in smart grids, the communication network between the control center and actuator faces the challenge of random communication delays and packet drops in the form of cyberattacks. In this article, the conventional thermal power plant operations within an LFC have been modified using energy storage elements with an emphasis on maximizing the RES utilization while tackling the problems associated with cyber-physical systems, such as packet drops and random time delays. A filtered proportional–integral–derivative (PID) controller is tuned in the LFC using the particle swarm optimization (PSO) algorithm, including random time delays and cyberattacks modeled as random packet drops. The tuned PID control performance in the LFC scheme is tested with synthetic stochastic as well as real profiles of RES and load demands. The numerical analysis has been conducted on two-area LFC model with Monte Carlo simulations of stochastic demand and generation profiles.Note to Practitioners—We are moving toward more renewable energy-based cyber-physical power grids, and it is becoming increasingly important to understand the limits of the balance between more renewable energy integration versus changing the prime-mover in thermal power generation units to meet uncertain load demands. This article proposes a new load frequency control (LFC) scheme employing a nonlinear dead-zone element between the control signals and the actuators (prime movers), which allows the utilization of the available renewable energy sources (RESs) to meet the load and then send a set-point change command in the thermal power generators if the RES does not meet the load. The robustness of the smart grid stability is also verified with the denial-of-service (DoS)-type cyberattack, which is represented in the form of high probability of packet drops and stochastic time delays in the communication channels between the control center and the generation units. It is also essential to understand how different stochastic profiles may affect the stability and performance of the tuned LFC loops, under random time delays and packet dropouts. These are quantified using the uncertainty bounds of grid frequency, its rate of change, control inputs, and power exchange between the two areas that are analyzed using Monte Carlo simulations with different types of nonstationary load and RES profiles and also using real data to show the effectiveness of the LFC scheme with communication constraints and the resulting imperfections.

Background subtraction (BS) is a fundamental step for moving object detection in various video surveillance applications. Gaussian mixture model (GMM) is a widely used BS technique which provides a good compromise between robustness to the background variations and real‐time constraints. However, GMM does not support the spatial relationship among neighbouring pixels and it uses a fixed learning rate for every pixel during the parameter update. On the other hand, Wronskian change detection model (WM) is a spatial‐domain BS technique which solves misclassification of pixels but fails in the presence of dynamic background. In this study, a novel spatio‐temporal BS technique is proposed that exploits spatial relation of Wronskian function and employs it with a new fuzzy adaptive learning rate in a GMM framework. Instead of using WM directly, an improved WM is proposed by adaptively finding out the ratio of the current pixel to the background pixel or its reciprocal, and a weighted Wronskian is developed to mitigate the effect of dynamic background pixels. Additionally, a new fuzzy adaptive learning rate is employed in the GMM framework. Experimental results of the proposed framework yield better silhouette of the moving objects as compared with the state‐of‐the‐art techniques.

Detection of moving objects in the presence of complex scenes such as dynamic background (e.g, swaying vegetation, ripples in water, spouting fountain), illumination variation, and camouflage is a very challenging task. In this context, we propose a robust background subtraction technique with three contributions. First, we present the use of color difference histogram (CDH) in the background subtraction algorithm. This is done by measuring the color difference between a pixel and its neighbors in a small local neighborhood. The use of CDH reduces the number of false errors due to the non-stationary background, illumination variation and camouflage. Secondly, the color difference is fuzzified with a Gaussian membership function. Finally, a novel fuzzy color difference histogram (FCDH) is proposed by using fuzzy c-means (FCM) clustering and exploiting the CDH. The use of FCM clustering algorithm in CDH reduces the large dimensionality of the histogram bins in the computation and also lessens the effect of intensity variation generated due to the fake motion or change in illumination of the background. The proposed algorithm is tested with various complex scenes of some benchmark publicly available video sequences. It exhibits better performance over the state-of-the-art background subtraction techniques available in the literature in terms of classification accuracy metrics like MCC and PCC.