Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.

Helping behavior in effective teams is achieved via some overlapping "shared mental models" that are developed and maintained by members of the team. In this paper, we take the perspective that multiparty "proactive" communication is critical for establishing and maintaining such a shared mental model among teammates, which is the basis for agents to offer proactive help and to achieve coherent teamwork. We first provide formal semantics for multiparty proactive performatives within a team setting. We then examine how such performatives result in updates to mental model of teammates, and how such updates can trigger helpful behaviors from other teammates. We also provide conversation policies for multiparty proactive performatives.

Effective human teams use overlapping shared mental models for anticipating information needs of teammates and for offering relevant information proactively. The long-term goal of our research is to empower agents with such "shared mental models" so that they can be used to better simulate, train, or support human teams for their information fusion, interpretation, and decisions. Toward this goal, we have developed a team agent architecture called CAST that enables agents to infer information needs of teammates, which further enables agents to assist teammates by proactively delivering needed information to them. In this paper, we focus on two key issues related to proactive information delivery behavior. First, we model the semantics of proactive information delivery as an attempt (called ProAssert), which extends the performative Assert in Joint Intention Theory. Second, we introduce a decision-theoretic approach for reasoning about whether to act on a potential proactive assert. Experimental results suggested that the decision-theoretic communication strategy enhances the team performance. The formal semantics and the decision-theoretic communication strategies together provide a sound and practical framework that enables further studies regarding proactive information delivery for supporting the decision making of a team involving human and agents.

In traditional optimal control and design problems, the control gains and design parameters are usually derived to minimize a cost function reflecting the system performance and control effort. One major challenge of such approaches is the selection of weighting matrices in the cost function, which are usually determined via trial and error and human intuition. While various techniques have been proposed to automate the weight selection process, they either can not address complex design problems or suffer from slow convergence rate and high computational costs. We propose a layered approach based on Q-learning, a reinforcement learning technique, on top of genetic algorithms (GA) to determine the best weightings for optimal control and design problems. The layered approach allows for reuse of knowledge. Knowledge obtained via Q-learning in a design problem can be used to speed up the convergence rate of a similar design problem. Moreover, the layered approach allows for solving optimizations that cannot be solved by GA alone. To test the proposed method, we perform numerical experiments on a sample active-passive hybrid vibration control problem, namely adaptive structures with active-passive hybrid piezoelectric networks (APPN). These numerical experiments show that the proposed Q-learning scheme is a promising approach for.

Task decomposition in a multi-agent environment is often performed online. This paper proposes a method for subtask allocation that can be performed before the agents are deployed, reducing the need for communication among agents during their mission. The proposed method uses a Voronoi diagram to partition the task-space among team members and includes two phases: static and dynamic. Static decomposition (performed in simulation before the start of the mission) repeatedly partitions the task-space by generating random diagrams and measuring the efficacy of the corresponding subtask allocation. If necessary, dynamic decomposition (performed in simulation after the start of a mission) modifies the result of a static decomposition (i.e. in case of resource limitations for some agents). Empirical results are reported for the problem of surveillance of an arbitrary region by a team of agents. 1 Introduction Multiagent teamwork is an active area of research and task decomposition among team members remains a challenge in multiagent environments. Some of the previous methods proposed for teamwork and task decomposition require periodic negotiations among team members (