Unmanned Aerial Vehicles (UAVs) are playing an increasingly critical role in Internet of Things (IoT), and with AI advancements, they are evolving into intelligent, multi-functional platforms supporting crowdsensing applications in modern IoT ecosystems. Given the dynamic nature of UAV-based IoT services, it is crucial to evaluate whether UAVs meet essential performance characteristics like flexibility, robustness, and stability. Flight data of UAVs is fundamental for assessing these characteristics, and effective data management is key to enabling timely, accurate performance evaluations. In this paper, we propose a Hierarchical Tree Model (HTM) specifically designed to accommodate the characteristics of UAVs-generated data. This model is supported by a hierarchical tree-based storage structure that optimizes the organization and retrieval of time-series data. To enable characteristic evaluation, we use tags to store characteristic information. To enable characteristic evaluation, we use tags to store characteristic information and apply encoding/decoding algorithms for flexible operation. We extend the SQL syntax tree of IoTDB with new syntax and semantics, enhancing the IoTDB parser and optimizer for tag integration and data operation. Experimental results show that our framework enables more efficient real-time evaluation of UAVs performance and better scalability for large deployments. Our findings highlight the potential of this time-series data management approach to support the real-time evaluation of UAVs characteristics, facilitating more informed decision-making and resource allocation in IoT-driven environments.

The performance in multi-agent reinforcement learning (MARL) scenarios has usually been analysed in homogeneous teams with a few choices for the sociality regime (selfish, egalitarian, or altruistic). In this paper we analyse both homogeneous and heterogeneous teams in a variation of sociality regimes in the predator-prey game, using a novel normalisation of the weights so that the sum of all rewards is independent of the sociality regime. We find that the selfish regime is advantageous for both predator and prey teams, and for both homogeneous and heterogeneous teams. In particular, rewards are about 100% higher for the predator team when switching from the egalitarian to selfish regime and more than 400% higher from the altruistic regime. For the prey, the increase is around 40% and 100% respectively. The results are similar for homogeneous and heterogeneous situations. The takeaway message is that any study of homogeneous and heterogeneous cooperative-competitive multi-agent reinforcement learning teams should also take into account the sociality regimes before making conclusions on the preference of any algorithm.

Multi-agent AI research promises a path to develop human-like and human-compatible intelligent technologies that complement the solipsistic view of other approaches, which mostly do not consider interactions between agents. Aiming to make progress in this direction, the Melting Pot contest 2023 focused on the problem of cooperation among interacting agents and challenged researchers to push the boundaries of multi-agent reinforcement learning (MARL) for mixed-motive games. The contest leveraged the Melting Pot environment suite to rigorously evaluate how well agents can adapt their cooperative skills to interact with novel partners in unforeseen situations. Unlike other reinforcement learning challenges, this challenge focused on social rather than environmental generalization. In particular, a population of agents performs well in Melting Pot when its component individuals are adept at finding ways to cooperate both with others in their population and with strangers. Thus Melting Pot measures cooperative intelligence.The contest attracted over 600 participants across 100+ teams globally and was a success on multiple fronts: (i) it contributed to our goal of pushing the frontiers of MARL towards building more cooperatively intelligent agents, evidenced by several submissions that outperformed established baselines; (ii) it attracted a diverse range of participants, from independent researchers to industry affiliates and academic labs, both with strong background and new interest in the area alike, broadening the field’s demographic and intellectual diversity; and (iii) analyzing the submitted agents provided important insights, highlighting areas for improvement in evaluating agents' cooperative intelligence. This paper summarizes the design aspects and results of the contest and explores the potential of Melting Pot as a benchmark for studying Cooperative AI. We further analyze the top solutions and conclude with a discussion on promising directions for future research.