This paper presents KMagent, a multi-agent Large Language Model-based knowledge management platform designed to accelerate product innovation within industrial Research Development environments. The system integrates structured Knowledge Graph construction, LLM-driven Question Answering, and a comprehensive evaluation framework under a Retrieval-Augmented Generation architecture. By combining LLM reasoning with domain-specific KGs, KMagent produces accurate and context-aware responses to complex queries. To assess performance, we introduce a dual-layered evaluation framework covering both the structural integrity of the KG and the factual quality of generated responses using reference-based metrics such as BLEU, ROUGE, METEOR, BERTScore and reference-free metrics such as UniEval and G-Eval. Using polymer degradation as a case study, we demonstrate the system’s effectiveness across knowledge extraction, reasoning, and evaluation.
The complexity of combinatorial optimization problems typically leads to a steep performance decrease of solving approaches with increasing problem size. We explore the usage of machine learning (ML) to substantially reduce the search space size of large problem instances by predicting and removing unpromising parts in order to accelerate the subsequent solving process. More specifically, we explore graph sparsening techniques for the electric autonomous dial-a-ride problem (E-ADARP), where self-driving electric vehicles are used to provide an efficient and sustainable ride-sharing service by serving customer transportation requests between pickup and drop-off locations within specified time windows. Approaches utilizing support vector machines as well as gradient boosted trees are compared to and also combined with a common k-nearest neighbor heuristic. Our goal is to boost the performance of a state-of-the-art large neighborhood search for the E-ADARP to make it well applicable to instances with up to 5200 requests and 260 vehicles. Our ML models are trained on representative instances and close-to-optimal solutions obtained from excessively long runs in a weakly supervised fashion. We uncover challenges, a fundamental limitation, and benefits of this approach and are able to achieve the intended scalability.
This paper proposes a smart solution for on-street parking based on reinforcement learning. As part of a collaborative parking system, we show the ability of reinforcement learning (RL) techniques to find optimal strategies for guiding drivers toward available spots, especially in areas where free parking is scarce. We implement a proof of concept by first training an agent using a Q-learning method in a reduced-dimensional virtual environment to validate the feasibility of our approach. To simulate realistic urban dynamics, we design a multi-agent system comprising a city sub-model based on the Manhattan grid, partitioned in several areas, and a mobility model that reflects typical traffic flows and parking behaviors. The agent-based simulations are conducted using the NetLogo platform. From the resulting synthetic data, which capture the collaborative interactions between agents in search of parking, we build a dataset to train a deep reinforcement learning (DRL) model. Our experiments demonstrate the potential of offline DRL methods to learn efficient policies from simulated collaborative parking scenarios, contributing to the development of intelligent, data-driven urban mobility solutions.
To reduce the environmental impact of civil aviation, future aircraft configurations will be equipped with Very High Aspect Ratio Wings. Such wings enable drag reduction but their structural sizing aiming at weight minimization is a challenge due to high structural constraints. To reduce these constraints, different load alleviation strategies can be employed to reduce the loads for critical sizing cases. Positive and negative gusts are two of these critical cases but they require the measurement of the flow field in front of the aircraft in order to apply relevant load alleviation strategies. This can be done using a wind lidar. To determine the 3D wind field, the lidar is addressed along different axes to obtain the projections of the wind along each of them. In this paper, we present a methodology to incorporate a priori knowledge of the turbulence structure into the estimation procedure.
This paper proposes an advanced approach focused on the automatic tuning of fuzzy rules in a Self-Tunable Fuzzy Inference System (STFIS) network. By combining neural network principles with a zero-order Takagi-Sugeno-type FIS (Sugeno FIS), the approach enables continuous adaptation to external disturbances and system changes. The mini-JEAN control architecture optimizes the STFIS rule base in real-time, enabling efficient control of an underactuated, coupled system: the inverted pendulum. A backpropagation gradient descent learning algorithm is proposed to determine the STFIS network's weight values in real-time, supervised by an adaptive mechanism. A clustering algorithm is then applied to organize these values and extract decision rules from the weight data. The performance of the STFIS controller is compared with that of a fixed Rule-Based Fuzzy System (FPID controller). The results demonstrate that the STFIS controller outperforms the FPID controller in terms of efficiency and robustness, particularly when subjected to external disturbances. These findings confirm that the STFIS controller is a promising solution for real-time control applications.
