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This article presents the Cogni-CISMeF project, which aims at improving the health information search engine CISMeF, by including a conversational agent that interacts with the user in natural language. To study the cognitive processes…

Cooperative Adaptive Cruise Control (CACC) is a technology that allows groups of vehicles to form in automated, tightly-coupled platoons. CACC schemes exploit Vehicle-to-Vehicle (V2V) wireless communications to exchange information between…

Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving. Most existing work focuses on predicting marginal trajectories for each agent independently. However, it has rarely been explored in…

Communications standards are designed via committees of humans holding repeated meetings over months or even years until consensus is achieved. This includes decisions regarding the modulation and coding schemes to be supported over an air…

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing (SC)…

This paper presents the Adaptive Personalized Control System (APECS) architecture, a novel framework for human-in-the-loop control. An architecture is developed which defines appropriate constraints for the system objectives. A method for…

Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree…

This paper considers model predictive control of Hammerstein systems, where the linear dynamics are a priori unknown and the input nonlinearity is known. Predictive cost adaptive control (PCAC) is applied to this system using recursive…

Cooperative systems are systems in which the forces among agents are non-repulsive. The free evolution of such systems can tend to the formation of patterns, such as consensus or clustering, depending on the properties and intensity of the…

Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge…

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many…

It is likely that AI systems driven by pre-trained language models (PLMs) will increasingly be used to assist humans in high-stakes interactions with other agents, such as negotiation or conflict resolution. Consistent with the goals of…

Hyper-parameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested…

Conventional affine formation control (AFC) empowers a network of agents with flexible but collective motions - a potential which has not yet been exploited for large-scale swarms. One of the key bottlenecks lies in the design of an…

An adaptive randomized distributed space-time coding (DSTC) scheme and algorithms are proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receivers and an amplify-and-forward (AF) cooperation strategy are…

As large language model (LLM)-based multi-agent systems scale to handle increasingly complex tasks, balancing structural stability and dynamic adaptability becomes increasingly challenging. Existing systems typically adopt either…

In this paper, we investigate a novel control architecture and algorithm for incorporating preadaption functions. We propose a preadaptation mechanism that can augment any adaptive control scheme and improve its resilience. Through…

Model predictive control (MPC) is an optimal control strategy where control input calculation is based on minimizing the predicted tracking error over a finite horizon that moves with time. This strategy has an advantage over conventional…

Embedded systems are becoming more in demand to work in dynamic and uncertain environments, and being confined to the strong requirements of real-time. Conventional static scheduling models usually cannot cope with runtime modification in…

Most of us must have been fascinated by the eye catching displays of collectively moving animals. Schools of fish can move in a rather orderly fashion and then change direction amazingly abruptly. There are a huge number of further examples…