Tools of the Trade: A Survey of Various Agent Based Modeling Platforms

Agent Based Modeling (ABM) toolkits are as diverse as the community of people who use them. With so many toolkits available, the choice of which one is best suited for a project is left to word of mouth, past experiences in using particular toolkits and toolkit publicity. This is especially troublesome for projects that require specialization. Rather than using toolkits that are the most publicized but are designed for general projects, using this paper, one will be able to choose a toolkit that already exists and that may be built especially for one's particular domain and specialized needs. In this paper, we examine the entire continuum of agent based toolkits. We characterize each based on 5 important characteristics users consider when choosing a toolkit, and then we categorize the characteristics into user-friendly taxonomies that aid in rapid indexing and easy reference.Agent Based Modeling, Individual Based Model, Multi Agent Systems

Forum Session at the First International Conference on Service Oriented Computing (ICSOC03)

The First International Conference on Service Oriented Computing (ICSOC) was held in Trento, December 15-18, 2003. The focus of the conference ---Service Oriented Computing (SOC)--- is the new emerging paradigm for distributed computing and e-business processing that has evolved from object-oriented and component computing to enable building agile networks of collaborating business applications distributed within and across organizational boundaries. Of the 181 papers submitted to the ICSOC conference, 10 were selected for the forum session which took place on December the 16th, 2003. The papers were chosen based on their technical quality, originality, relevance to SOC and for their nature of being best suited for a poster presentation or a demonstration. This technical report contains the 10 papers presented during the forum session at the ICSOC conference. In particular, the last two papers in the report ere submitted as industrial papers

Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization

Offline reinforcement learning (RL) has received considerable attention in recent years due to its attractive capability of learning policies from offline datasets without environmental interactions. Despite some success in the single-agent setting, offline multi-agent RL (MARL) remains to be a challenge. The large joint state-action space and the coupled multi-agent behaviors pose extra complexities for offline policy optimization. Most existing offline MARL studies simply apply offline data-related regularizations on individual agents, without fully considering the multi-agent system at the global level. In this work, we present OMIGA, a new offline m ulti-agent RL algorithm with implicit global-to-local v alue regularization. OMIGA provides a principled framework to convert global-level value regularization into equivalent implicit local value regularizations and simultaneously enables in-sample learning, thus elegantly bridging multi-agent value decomposition and policy learning with offline regularizations. Based on comprehensive experiments on the offline multi-agent MuJoCo and StarCraft II micro-management tasks, we show that OMIGA achieves superior performance over the state-of-the-art offline MARL methods in almost all tasks

MADiff: Offline Multi-agent Learning with Diffusion Models

Diffusion model (DM), as a powerful generative model, recently achieved huge success in various scenarios including offline reinforcement learning, where the policy learns to conduct planning by generating trajectory in the online evaluation. However, despite the effectiveness shown for single-agent learning, it remains unclear how DMs can operate in multi-agent problems, where agents can hardly complete teamwork without good coordination by independently modeling each agent's trajectories. In this paper, we propose MADiff, a novel generative multi-agent learning framework to tackle this problem. MADiff is realized with an attention-based diffusion model to model the complex coordination among behaviors of multiple diffusion agents. To the best of our knowledge, MADiff is the first diffusion-based multi-agent offline RL framework, which behaves as both a decentralized policy and a centralized controller, which includes opponent modeling and can be used for multi-agent trajectory prediction. MADiff takes advantage of the powerful generative ability of diffusion while well-suited in modeling complex multi-agent interactions. Our experiments show the superior performance of MADiff compared to baseline algorithms in a range of multi-agent learning tasks.Comment: 17 pages, 7 figures, 4 table

A mobile agent and message ferry mechanism based routing for delay tolerant network

Delay Tolerant Network (DTN) is a class of networks characterized by long delays, frequent disconnections and partitioning of communication paths between network nodes. Due to the frequent disconnection and network partitioning, the overall performance of the network will be deteriorated sharply. The problem is how to make the network fairly connected to optimize data routing and enhance the performance of a network. The aim of this study is to improve the performance of DTN by minimizing end-to-end delivery time and increasing message delivery ratio. Therefore, this research tackles the problem of intermittent connectivity and network partitioning by introducing Agents and Ferry Mechanism based Routing (AFMR). The AFMR comprises of two stages by applying two schemes: mobile agents and ferry mechanism. The agents' scheme is proposed to deal with intermittent connectivity and network partitioning by collecting the basic information about network connection such as signal strength, nodes position in the network and distance to the destination nodes to minimize end-to-end delivery time. The second stage is to increase the message delivery ratio by moving the nodes towards the path with available network connectivity based on agents' feedback. The AFMR is evaluated through simulations and the results are compared with those of Epidemic, PRoPHET and Message Ferry (MF). The findings demonstrate that AFMR is superior to all three, with respect to the average end-to-end delivery time, message delivery ratio, network load and message drop ratio, which are regarded as extremely important metrics for the evaluation of DTN routing protocols. The AFMR achieves improved network performance in terms of end-to-end delivery time (56.3%); enhanced message delivery ratio (60.0%); mitigation of message drop (63.5%) and reduced network load (26.1 %). The contributions of this thesis are to enhance the performance of DTN by significantly overcoming the intermittent connectivity and network partitioning problems in the network