This research focuses on the development of a system that employs genetic algorithms (GA) that generate increasingly challenging terrain for a player to navigate. Our system is developed within a video game, where a player is required to get from a start point to an endpoint, with gameplay mechanisms based on the terrain acting as obstacles for the player. The results from a game experience survey for the ingame section show that players rated gameplay more negatively when the terrain was randomly generated than when it was generated by the GA. While players did not feel more challenged using the GA-generated terrain, they gave an overall more positive gameplay experience. A quantitative data analysis of data collected during gameplay indicates an increase as the rounds progressed, in the number of deaths, the time taken, and levels skipped when using the GA. In contrast, the randomly generated terrain shows no significant difference.
In recent years, swarm robotics has proven to be a promising approach to develop collective behaviors that permit to extend the capabilities of a single robot in solving complex tasks. Here, the major challenge consists of designing individual behaviors that lead to the emergence of the desired collective behavior. In this regard, several automatic design methods have been proposed over the last decades. In this work, we present a hybrid approach that combines two of the main techniques of such design methods: multi-agent reinforcement learning and neuro-evolution. The idea is to evolve a pre-trained individual policy, in such a way that we can learn an effective collective behavior for vision-based navigation tasks. Specifically, we want a homogeneous swarm of low-cost robots to autonomously explore a simulated indoor corridor, while avoiding static and dynamic obstacles in a flexible and intelligent way. Through several experiments, we show how the learned collective policy outperforms the individual one in solving the previous task. Furthermore, our approach becomes highly memory-efficient, thanks primarily to a particular autoencoder able to compress input images into very compact visual representations.
Maintaining optimal blood glucose levels is paramount in diabetes management. Currently, patients are tasked with deciding when and how much insulin to administer. In contrast, reinforcement learning offers a closed-loop control system that automates this process. In this study, we present a reinforcement learning framework tailored for personalized blood glucose management, employing in silico patients. The agent dynamically adjusts insulin dosages in response to real-time glucose levels and patient-specific characteristics. We introduce HypoTreat, an innovative extension to the model that incorporates the 15-15 rule. This addition simulates the real-life patient’s behavior by temporarily opening the closed-loop, permitting virtual patients to consume food when their blood glucose level falls below 70, thus, preventing hypoglycemia.While the scenario of opening the closed-loop is undesirable, our findings reveal its efficacy in averting catastrophic hypoglycemia without increasing time spent in hyperglycemia. By introducing HypoTreat in training the average time spent in severe hypoglycemia has been reduced by 97.4%. Meanwhile, the in silico patients remain over 80% of their time within the target blood glucose range. This demonstrates the potential of our simulation-driven approach to enhance blood glucose control by intelligently incorporating real-life behavior, providing a safer and more effective solution for diabetes management.
The rapid increase in vehicular sensor data and advances in Internet of Things (IoT) technologies pose a dual challenge and opportunity for real-time traffic management and driver behavior analysis in the domain of Intelligent Transportation Systems (ITS). The current gap lies in effectively processing this data at the network edge, which is critical for timely and efficient decision-making in ITS. Our study proposes a novel, multi-layered, stream-oriented data processing methodology specifically designed for edge computing environments to address this challenge. This approach integrates soft sensors, the Typicality and Eccentricity Data Analytics (TEDA) framework, and an incremental clustering algorithm to detect and classify driver behavior patterns. The emphasis on using low-energy hardware and TinyML techniques is crucial, aiming to optimize processing efficiency while minimizing the environmental impact. To substantiate the efficacy of our methodology, we conducted a practical case study in Natal-RN, Brazil, utilizing the Freematics One + OBD-II microcontroller device for real-world application and validation. This involved two participants and focused on real-time data analysis for driver profile detection. The preliminary results demonstrate a significant potential of our approach in accurately classifying driving behaviors and patterns, offering insights for enhancing vehicle efficiency and reducing fuel consumption. This study fills a critical gap in ITS. It sets the stage for future research in sustainable and adaptive transportation systems, leveraging the power of edge computing and incremental algorithms in real-time data stream processing.
Large Language Models represent a transformative technology at the forefront of artificial intelligence and natural language processing, with applications spanning diverse domains. This study conducts a comprehensive science mapping analysis of the LLMs research field, leveraging bibliometric techniques to uncover its thematic structure, trends, and global actors involved. Utilizing data from the Web of Science, a corpus of 1303 research documents from 2010 to 2023 is analyzed, revealing a notable surge in research activity, particularly in recent years. Key thematic areas driving research within the field are identified, including chatbot, code generation, augmented reality, transformers, and machine learning paradigms. Foundational technologies such as transformers are pivotal in shaping the research landscape, while emerging themes like prompt learning hint at future directions. This study offers valuable insights for researchers, practitioners, and policymakers seeking to navigate the dynamic landscape of LLMs research and harness its full potential for societal benefit.
