Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications. Because of the curse of dimensionality, the popular "centralized training decentralized execution" framework requires a long time in training, yet still cannot converge efficiently. In this paper, we propose a general training framework, MARL-LNS, to algorithmically address these issues by training on alternating subsets of agents using existing deep MARL algorithms as low-level trainers, while not involving any additional parameters to be trained. Based on this framework, we provide three algorithm variants based on the framework: random large neighborhood search (RLNS), batch large neighborhood search (BLNS), and adaptive large neighborhood search (ALNS), which alternate the subsets of agents differently. We test our algorithms on both the StarCraft Multi-Agent Challenge and Google Research Football, showing that our algorithms can automatically reduce at least 10% of training time while reaching the same final skill level as the original algorithm.

This paper presents a novel adversary model specifically tailored to distributed systems, aiming to assess the security of blockchain networks. Building upon concepts such as adversarial assumptions, goals, and capabilities, our proposed adversary model classifies and constrains the use of adversarial actions based on classical distributed system models, defined by both failure and communication models. The objective is to study the effects of these allowed actions on the properties of distributed protocols under various system models. A significant aspect of our research involves integrating this adversary model into the Multi-Agent eXperimenter (MAX) framework. This integration enables fine-grained simulations of adversarial attacks on blockchain networks. In this paper, we particularly study four distinct fairness properties on Hyperledger Fabric with the Byzantine Fault Tolerant Tendermint consensus algorithm being selected for its ordering service. We define novel attacks that combine adversarial actions on both protocols, with the aim of violating a specific client-fairness property. Simulations confirm our ability to violate this property and allow us to evaluate the impact of these attacks on several order-fairness properties that relate orders of transaction reception and delivery.

Interior design allows us to be who we are and live how we want - each design is as unique as our distinct personality. However, it is not trivial for non-professionals to express and materialize this since it requires aligning functional and visual expectations with the constraints of physical space; this renders interior design a luxury. To make it more accessible, we present I-Design, a personalized interior designer that allows users to generate and visualize their design goals through natural language communication. I-Design starts with a team of large language model agents that engage in dialogues and logical reasoning with one another, transforming textual user input into feasible scene graph designs with relative object relationships. Subsequently, an effective placement algorithm determines optimal locations for each object within the scene. The final design is then constructed in 3D by retrieving and integrating assets from an existing object database. Additionally, we propose a new evaluation protocol that utilizes a vision-language model and complements the design pipeline. Extensive quantitative and qualitative experiments show that I-Design outperforms existing methods in delivering high-quality 3D design solutions and aligning with abstract concepts that match user input, showcasing its advantages across detailed 3D arrangement and conceptual fidelity.

Autonomous and learning systems based on Deep Reinforcement Learning have firmly established themselves as a foundation for approaches to creating resilient and efficient Cyber-Physical Energy Systems. However, most current approaches suffer from two distinct problems: Modern model-free algorithms such as Soft Actor Critic need a high number of samples to learn a meaningful policy, as well as a fallback to ward against concept drifts (e. g., catastrophic forgetting). In this paper, we present the work in progress towards a hybrid agent architecture that combines model-based Deep Reinforcement Learning with imitation learning to overcome both problems.

In multi-agent robotic exploration, managing and effectively utilizing the vast, heterogeneous data generated from dynamic environments poses a significant challenge. Federated learning (FL) is a promising approach for distributed mapping, addressing the challenges of decentralized data in collaborative learning. FL enables joint model training across multiple agents without requiring the centralization or sharing of raw data, overcoming bandwidth and storage constraints. Our approach leverages implicit neural mapping, representing maps as continuous functions learned by neural networks, for compact and adaptable representations. We further enhance this approach with meta-initialization on Earth datasets, pre-training the network to quickly learn new map structures. This combination demonstrates strong generalization to diverse domains like Martian terrain and glaciers. We rigorously evaluate this approach, demonstrating its effectiveness for real-world deployment in multi-agent exploration scenarios.

Constructing personalized and anthropomorphic agents holds significant importance in the simulation of social networks. However, there are still two key problems in existing works: the agent possesses world knowledge that does not belong to its personas, and it cannot eliminate the interference of diverse persona information on current actions, which reduces the personalization and anthropomorphism of the agent. To solve the above problems, we construct the social media agent based on personalized knowledge and dynamic persona information. For personalized knowledge, we add external knowledge sources and match them with the persona information of agents, thereby giving the agent personalized world knowledge. For dynamic persona information, we use current action information to internally retrieve the persona information of the agent, thereby reducing the interference of diverse persona information on the current action. To make the agent suitable for social media, we design five basic modules for it: persona, planning, action, memory and reflection. To provide an interaction and verification environment for the agent, we build a social media simulation sandbox. In the experimental verification, automatic and human evaluations demonstrated the effectiveness of the agent we constructed.

We investigate the effectiveness of ChatGPT in extracting norms from contracts. Norms provide a natural way to engineer multiagent systems by capturing how to govern the interactions between two or more autonomous parties. We extract norms of commitment, prohibition, authorization, and power, along with associated norm elements (the parties involved, antecedents, and consequents) from contracts. Our investigation reveals ChatGPT's effectiveness and limitations in norm extraction from contracts. ChatGPT demonstrates promising performance in norm extraction without requiring training or fine-tuning, thus obviating the need for annotated data, which is not generally available in this domain. However, we found some limitations of ChatGPT in extracting these norms that lead to incorrect norm extractions. The limitations include oversight of crucial details, hallucination, incorrect parsing of conjunctions, and empty norm elements. Enhanced norm extraction from contracts can foster the development of more transparent and trustworthy formal agent interaction specifications, thereby contributing to the improvement of multiagent systems.

Recent advancements in automatic code generation using large language model (LLM) agent have brought us closer to the future of automated software development. However, existing single-agent approaches face limitations in generating and improving large-scale, complex codebases due to constraints in context length. To tackle this challenge, we propose Self-Organized multi-Agent framework (SoA), a novel multi-agent framework that enables the scalable and efficient generation and optimization of large-scale code. In SoA, self-organized agents operate independently to generate and modify code components while seamlessly collaborating to construct the overall codebase. A key feature of our framework is the automatic multiplication of agents based on problem complexity, allowing for dynamic scalability. This enables the overall code volume to be increased indefinitely according to the number of agents, while the amount of code managed by each agent remains constant. We evaluate SoA on the HumanEval benchmark and demonstrate that, compared to a single-agent system, each agent in SoA handles significantly less code, yet the overall generated code is substantially greater. Moreover, SoA surpasses the powerful single-agent baseline by 5% in terms of Pass@1 accuracy.

Imagine the dream employee: They don't take breaks, go on vacation or request meetings. For some industries, this type of worker could soon be hired. In recent months several companies have announced they are building AI agents, or'synthetic employees.' These digital workers could upend the workplace as we know it - answering emails, organizing invoices, responding to customer service inquiries and managing a calendar - possibly doing away with admin employees or pricey third-party technology. Mr Broussard, whose company works with Sam Altman's OpenAI, told DailyMail.com the next two years will see leaps and bounds of progress with these types of workers.

Effective operation and seamless cooperation of robotic systems are a fundamental component of next-generation technologies and applications. In contexts such as disaster response, swarm operations require coordinated behavior and mobility control to be handled in a distributed manner, with the quality of the agents' actions heavily relying on the communication between them and the underlying network. In this paper, we formulate the problem of dynamic network bridging in a novel Decentralized Partially Observable Markov Decision Process (Dec-POMDP), where a swarm of agents cooperates to form a link between two distant moving targets. Furthermore, we propose a Multi-Agent Reinforcement Learning (MARL) approach for the problem based on Graph Convolutional Reinforcement Learning (DGN) which naturally applies to the networked, distributed nature of the task. The proposed method is evaluated in a simulated environment and compared to a centralized heuristic baseline showing promising results. Moreover, a further step in the direction of sim-to-real transfer is presented, by additionally evaluating the proposed approach in a near Live Virtual Constructive (LVC) UAV framework.