SEARCH · Search NASA
Results for “multi-agent platform”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
3. Motion Platforms and Kinematic Arrangements
Within a machine, mechanisms and motion are organized in what is known as a “kinematic arrangement,” which helps classify machines based on how they move. The most common kinematic arrangements for additive manufacturing systems are Cartesian, followed by delta, and then six-degrees-of-freedom robotic arms. However, there are a multitude of less common systems, such as the SCARA, polar robots, cable driven parallel robots, mobile platforms, and multi-agent systems. This chapter surveys these various kinematic arrangements to give a broad understanding of the mechanisms underlying motion within additive manufacturing systems. Understanding these mechanisms and their resulting motion provides a framework for discussing path planning for all scales and families of additive manufacturing.
Development and application of a real-time testbed for multiagent system interoperability: A case study on hierarchical microgrid control
This article presents the development and application of a real-time testbed for multiagent system interoperability. As utility independent private microgrids are installed constantly, standardized interoperability frameworks are required to define behavioral models of the individual agents for expandability and plug-and-play operation. In this paper, we propose a comprehensive hybrid agent framework combining the foundation for intelligent physical agents (FIPA), IEC 61850, and data distribution service (DDS) standards. The IEC 61850 logical node concept is extended using FIPA based agent communication language (ACL) with application specific attributes and deliberative behavior modeling capability. The DDS middleware is adopted to enable a real-time publisher-subscriber interoperability mechanism between platforms. The proposed multi-agent framework was validated in a laboratory based testbed involving developed intelligent electronic device (IED) prototypes and actual microgrid setups. Experimental results were demonstrated for both decentralized and distributed control approaches. Secondary and tertiary control levels of a microgrid were demonstrated for decentralized hierarchical control case study. A consensus-based economic dispatch case study was demonstrated as a distributed control example. Finally, it was shown that the developed agent platform is industrially applicable for actual smart grid field deployment.
Scalable/Secure Cooperative Algorithms and Framework for Extremely-high Penetration Solar Integration (SolarExPert) (Final Technical Report)
This SolarExPert project has developed a Sustainable Grid Platform (SGP) with scalable architecture of distributed control and optimization. The SGP consists of the following major functions: 1) an advanced grid architecture with hierarchical and distributed communication and control, combined with the OpenFMB standard and implemented on the Multi-Agent OpenDSS (MA-OpenDSS) platform; 2) an online distributed stochastic optimal power flow; 3) an online distributed system state estimation algorithm; 4) the distributed Volt/VAR optimization and frequency control algorithms; 5) the distributed distribution system restoration strategy. The developed SGP together with advanced functions are tested in 1 million (1M)-node distribution system on the MA-OpenDSS platform. Furthermore, the models and algorithms are tested in 100,000-node system HiL simulation, and also in P-HiL implementation with 100 physical devices. The developed functions haven been validated and tested on the selected actual distribution feeder with the data collected from the field of Maui Meadows in Hawaii. The distributed PV hosting capacities with cooperative Volt/VAR and Volt/VAR/Watt control are estimated and compared to provide recommendations for customers and the utility company.
A Local Electricity Market for Transactive Energy with Privacy
Presentation in PESGM Panel on "Multi-Agent Systems for Transactive Energy: Theory, Platforms, and Demonstrations"
ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning
ProtAgents is a de novo protein design platform based on multimodal LLMs, where distinct AI agents with expertise in knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis tackle tasks in a dynamic setting.
Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins
Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.
Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation
Molecular-level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real-time operation. Digital off-axis holography (DOAH) provides high-throughput, label-free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence offers a powerful route to address these limitations by uniting physics-based reconstruction with data-driven interpretation. In this Perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI-driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi-agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with true paramagnetic and diamagnetic behavior. This demonstration shows how AI-enabled reasoning can deliver real-time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next-generation interfacial science.
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to inject sufficient resiliency into the operations of the network of microgrids. We consider adversarial actions to the voltage and power control loop reference signals at the grid forming (GFM) inverters in the microgrids which are essential to integrate renewable resources. Therefore, we formulate a resilient reinforcement learning training setup that uses these adversarial injections to generate episodic trajectories and train the RL agents to alleviate their impact on performance. To circumvent the concerns about data-sharing and privacy for different owners of the microgrids in the networked setting, we bring in the aspects of the federated operation to propose novel Fed-RL algorithms. As the dynamics of each microgrid are coupled due to electrical interlinks, the conventional federated RL approaches using decoupled independent environments are not applicable, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC). We have performed numerical simulations on an IEEE 123-bus benchmark test feeder with three microgrids by creating a customized simulation setup by encapsulating the microgrid dynamic simulations in GridLAB-D/HELICS co-simulation platform with the OpenAI Gym environment and validated the proposed resilient and secured learning methodology.
A multi-agent approach to distribution system fault section estimation in smart grid environment
We report that Multi-Agent Systems (MAS) are seen from different areas as one of the paramount trends for the next generation of power systems. Numerous published studies about MAS discuss its utilization in power distribution networks but none focuses on the prior step to restoration and self-healing that is fault section estimation. This paper aims to show how MAS can improve the utilities’ reliability indexes and consumer satisfaction by overcoming the multiple fault section estimation problem. In order to do this, the authors considered using MAS as a means of communication between smart meters. The purpose of smart meters usage is to employ devices that are already present in smart grids, mainly because of their reading and saving data capacity. The proposed method was tested on a radial feeder generated by the authors. The network was built on HYPERSIM, a software platform of OPAL-RT Technologies. The simulation results show that this MAS provides speed, efficiency, and automation for the process of fault section estimation.
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Here, experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based control method, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.
Agent-Based Distributed Energy Resources for Supporting Intelligence at the Grid Edge
This article proposes a novel multi-agent framework that can link various forms of resources and power electronic systems into distributed energy resources (DER). The proposed multiagent architecture can also integrate DERs to a central controller for optimization and control to support the grid. To demonstrate the flexibility of this novel framework, the developed agent system is applied to a set of end-use systems. Furthermore, the agent framework is validated in hardware using controller-hardware-in-the-loop simulation platform.
matsim-agents v1.0
matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.
Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap
One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.