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NASA NTRS1999

Autonomous Agents: The Origins and Co-Evolution of Reproducing Molecular Systems

The central aim of this award concerned an investigation into, and adequate formulation of, the concept of an "autonomous agent." If we consider a bacterium swimming upstream in a glucose gradient, we are willing to say of the bacterium that it is going to get food. That is, we are willing, and do, describe the bacterium as acting on its own behalf in an environment. All free living cells are, in this sense, autonomous agents. But the bacterium is "just" a set of molecules. We define an autonomous agent as a physical system able to act on its own behalf in an environment, then ask, "What must a physical system be to be an autonomous agent?" The tentative definition for a molecular autonomous agent is that it must be self-reproducing and carry out at least one thermodynamic work cycle. The work carried out in this grant involved, among other features, the development of a detailed model of a molecular autonomous agent, and study of the kinetics of this system. In particular, a molecular autonomous agent must, by the above tentative definition, not only reproduce, but must carry out at least one work cycle. I took, as a simple example of a self-reproducing molecular system, the single-stranded DNA hexamer 3'CCGCGG5' which can line up and ligate its two complementary trimers, 5'CCG3' and 5'CGG3'. But the two ligated trimers constitute the same molecular sequence in the 3' to 5' direction as the initial hexamer, hence this system is autocatalytic. On the other hand the above system is not yet an autonomous agent. At the minimum, autonomous agents, as I have defined them, are a new class of chemical reaction network. At a maximum, they may constitute a proper definition of life itself.

Kauffman, Stuart↗

NASA NTRS2026

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗

DOE OSTI2025

Facile One-Pot Block Copolymer-Mediated Solvothermal Approach for Synthesis of High-Entropy Alloy with Enhanced OER Activity

Herein, we introduce a straightforward synthesis approach for highly active dendritic multimetallic high-entropy alloy (DMHEA@PtIrPdAgRu) nanoparticles with sufficient entropic mixing, featuring uniform distribution of five noble group metals (Pt, Ir, Pd, Ag, and Ru) via a block copolymer-mediated one-pot solvothermal reduction method for oxygen evolution reaction (OER). In this synthesis, N,N -dimethylformamide (DMF) is used as a reductant as well as solvent and core–shell-corona-type (poly(styrene)- block -poly(vinylpyridine)- block -poly(ethylene oxide)) (PS-PVP-PEO) block copolymer as a structure directing agent. The cooperative effect between the copolymer architecture and the reducing environment of DMF promoted a confined nucleation mechanism for forming a single-phase dendritic structure HEA with high compositional uniformity, thereby mitigating phase segregation, a common challenge in the synthesis of multimetallic nanoparticles. This prepared DMHEA@PtIrPdAgRu catalyst exhibits a low overpotential of 490 mV to attain a high current density of 100 mA cm –2 with a Tafel slope of 442 mV dec –1 for oxygen evolution. The superior OER performance is attributed to the synergistic cooperation among its active and coordinated metal centers as well as the incorporation of corrosion-resistant metal like platinum.

block copolymer↗

NASA NTRS2014

Agent Based Modeling of Air Carrier Behavior for Evaluation of Technology Equipage and Adoption

As part of ongoing research, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework to assist policymakers in identifying impacts on the U.S. air transportation system (ATS) of potential policies and technology related to the implementation of the Next Generation Air Transportation System (NextGen). This framework, called the Air Transportation System Evolutionary Simulation (ATS-EVOS), integrates multiple models into a single process flow to best simulate responses by U.S. commercial airlines and other ATS stakeholders to NextGen-related policies, and in turn, how those responses impact the ATS. Development of this framework required NASA and LMI to create an agent-based model of airline and passenger behavior. This Airline Evolutionary Simulation (AIRLINE-EVOS) models airline decisions about tactical airfare and schedule adjustments, and strategic decisions related to fleet assignments, market prices, and equipage. AIRLINE-EVOS models its own heterogeneous population of passenger agents that interact with airlines; this interaction allows the model to simulate the cycle of action-reaction as airlines compete with each other and engage passengers. We validated a baseline configuration of AIRLINE-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments. These experiments demonstrated AIRLINE-EVOS's capabilities in responding to an input price shock in fuel prices, and to equipage challenges in a series of analyses based on potential incentive policies for best equipped best served, optimal-wind routing, and traffic management initiative exemption concepts..

Horio, Brant M.↗

DOE OSTI2024

One-Pot Self-Assembly of Sequence-Controlled Mesoporous Heterostructures via Structure-Directing Agents

Multimaterial heterostructures have led to characteristics surpassing the individual components. Nature controls the architecture and placement of multiple materials through biomineralization of nanoparticles (NPs); however, synthetic heterostructure formation remains limited and generally departs from the elegance of self-assembly. Here, in this study, a class of block polymer structure-directing agents (SDAs) are developed containing repeat units capable of persistent (covalent) NP interactions that enable the direct fabrication of nanoscale porous heterostructures, where a single material is localized at the pore surface as a continuous layer. This SDA binding motif (design rule 1) enables sequence-controlled heterostructures, where the composition profile and interfaces correspond to the synthetic addition order. This approach is generalized with 5 material sequences using an SDA with only persistent SDA-NP interactions (“P-NP 1 –NP 2 ”; NP i = TiO 2 , Nb 2 O 5 , ZrO 2 ). Expanding these polymer SDA design guidelines, it is shown that the combination of both persistent and dynamic (noncovalent) SDA-NP interactions (“PD-NP 1 –NP 2 ”) improves the production of uniform interconnected porosity (design rule 2). The resulting competitive binding between two segments of the SDA (P- vs D-) requires additional time for the first NP type (NP 1 ) to reach and covalently attach to the SDA (design rule 3). The combination of these three design rules enables the direct self-assembly of heterostructures that localize a single material at the pore surface while preserving continuous porosity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2017

Application of Human-Autonomy Teaming to an Advanced Ground Station for Reduced Crew Operations

Within human factors there is burgeoning interest in the "human-autonomy teaming" (HAT) concept as a way to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous systems as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as "plays." It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations, healthcare and medicine, autonomous vehicles, photography, and aviation. The current paper presents one such effort to apply HAT. It details the design of a HAT agent, developed by Human Automation Teaming Solutions, Inc., to facilitate teamwork between the automation and the human operator of an advanced ground dispatch station. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO); consequently, we have named the agent R-HATS. Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by R-HATS, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, R-HATS determines what tasks need to be completed and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. R-HATS has been designed to both support end users and researchers in RCO and HAT. Additionally, R-HATS and its underlying architecture were developed with generalizability in mind as a modular software applicable outside of RCO/aviation domains. This paper will also discuss future further development and testing of RHATS.

automation↗

NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗

NASA NTRS2001

Flame Synthesis of Single- and Multi-Walled Carbon Nanotubes and Nanofibers

Metal-catalyzed carbon nanotubes are highly sought for a diverse range of applications that include nanoelectronics, battery electrode material, catalysis, hydrogen storage media and reinforcing agents in polymer composites. These latter applications will require vast quantities of nanotubes at competitive prices to be economically feasible. Moreover, reinforcing applications may not require ultrahigh purity nanotubes. Indeed, functionalization of nanotubes to facilitate interfacial bonding within composites will naturally introduce defects into the tube walls, lessening their tensile strength. Current methods of aerosol synthesis of carbon nanotubes include laser ablation of composite targets of carbon and catalyst metal within high temperature furnaces and decomposition of a organometallics in hydrocarbons mixtures within a tube furnace. Common to each approach is the generation of particles in the presence of the reactive hydrocarbon species at elevated temperatures. In the laser-ablation approach, the situation is even more dynamic in that particles and nanotubes are borne during the transient cooling phase of the laser-induced plasma for which the temperature far exceeds that of the surrounding hot gases within the furnace process tube. A shared limitation is that more efficient methods of nanoparticle synthesis are not readily incorporated into these approaches. In contrast, combustion can quite naturally create nanomaterials such as carbon black. Flame synthesis is well known for its commercial scalability and energy efficiency. However, flames do present a complex chemical environment with steep gradients in temperature and species concentrations. Moreover, reaction times are limited within buoyant driven flows to tens of milliseconds. Therein microgravity can greatly lessen temperature and spatial gradients while allowing independent control of flame residence times. In preparation for defining the microgravity experiments, the work presented here focuses on the effect of catalyst particle size and reactant gas in 1g.

VanderWal, R. L.↗

NASA NTRS1994

The problem with multiple robots

The issues that can arise in research associated with multiple, robotic agents are discussed. Two particular multi-robot projects are presented as examples. This paper was written in the hope that it might ease the transition from single to multiple robot research.

Huber, Marcus J.↗

NASA NTRS2017

Modeling Space Radiation with Radiomimetic Agent Bleomycin

Space radiation consists of proton and helium from solar particle events (SPE) and high energy heavy ions from galactic cosmic ray (GCR). This mixture of radiation with particles at different energy levels has different effects on biological systems. Currently, majority studies of radiation effects on human were based on single-source radiation due to the limitation of available method to model effects of space radiation on living organisms. While NASA Space Radiation Laboratory is working on advanced switches to make it possible to have a mixed field radiation with particles of different energies, the radiation source will be limited. Development of an easily available experimental model for studying effects of mixed field radiation could greatly speed up our progress in our understanding the molecular mechanisms of damage and responses from exposure to space radiation, and facilitate the discovery of protection and countermeasures against space radiation, which is critical for the mission to Mars. Bleomycin, a radiomimetic agent, has been widely used to study radiation induced DNA damage and cellular responses. Previously, bleomycin was often compared to low low Linear Energy Transfer (LET) gamma radiation without defined characteristics. Our recent work demonstrated that bleomycin could induce complex clustered DNA damage in human fibroblasts that is similar to DNA damage induced by high LET radiation. These type of DNA damage is difficult to repair and can be visualized by gamma-H2Ax staining weeks after the initial insult. The survival ratio between early and late plating of human fibroblasts after bleomycin treatment is between low LET and high LET radiation. Our results suggest that bleomycin induces DNA damage and other cellular stresses resembling those resulted from mixed field radiation with both low and high LET particles. We hypothesize that bleomycin could be used to mimic space radiation in biological systems. Potential advantages and limitations of using bleomycin to treat biological specimen as an easily available model to study effects of space radiation on biological systems and to develop countermeasures for space radiation associated risks will be discussed.

Lu, Tao↗

NASA NTRS2003

Agent Based Software for the Autonomous Control of Formation Flying Spacecraft

Distributed satellite systems is an enabling technology for many future NASA/DoD earth and space science missions, such as MMS, MAXIM, Leonardo, and LISA [1, 2, 3]. While formation flying offers significant science benefits, to reduce the operating costs for these missions it will be essential that these multiple vehicles effectively act as a single spacecraft by performing coordinated observations. Autonomous guidance, navigation, and control as part of a coordinated fleet-autonomy is a key technology that will help accomplish this complex goal. This is no small task, as most current space missions require significant input from the ground for even relatively simple decisions such as thruster burns. Work for the NMP DS1 mission focused on the development of the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems. NMRA integrates traditional real-time monitoring and control with components for constraint-based planning, robust multi-threaded execution, and model-based diagnosis and reconfiguration. The complexity of using an autonomous approach for space flight software was evident when most of its capabilities were stripped off prior to launch (although more capability was uplinked subsequently, and the resulting demonstration was very successful).

How, Jonathan P.↗

DOE OSTI2025

Chelation ion chromatography as an automated, and cost-effective analytical technique for REE determination: method development and applications

Rare earth elements (REEs), as critical minerals, have important uses in modern energy and technologies, yet are vulnerable to potential supply chain disruptions. To establish domestic REE supply chain, efficient REE detection methods for resource characterization and mineral processing will be needed to accelerate innovations for domestic REE recovery. This study developed a rapid, novel, and cost-effective for REE detection method using ion chromatography (IC) for aqueous samples. Various REE-targeted eluent gradients and post-column agent compositions were tested on the chelation ion chromatography (CIC) with UV-vis detector for optimal separation and quantification of REEs within approximately 20 min. The single-channel pump to deliver the post-column solution to UV-vis detector was replaced with a 4-channel gradient pump, to increase operation and maintenance efficiencies. After method optimization, resulting calibration curves for more than ten REEs achieved high coefficients of determination (R2>0.999) and low relatively standard deviations (below 3.24%), demonstrating sub-ppm level detection limits (0.0897 to 0.1149 mg/L). The reliability of the CIC method was validated through comparison with inductively coupled plasma mass spectrometry (ICP-MS), showing strong agreement in REE recovery from certified standards. The impact of metal ions and salts on REE recovery using CIC was also systematically investigated. CIC consistently exhibited reliable performance in the presence of salt solutions such as NaCl and Na₂SO₄ (up to 10,000 mg/L). Our study also found the presence of high concentrations of Al ions (at 10,000 mg/L) significantly influenced REE determination, and elevated concentrations of Ca ions affected the recovery of specific REEs, including La, Ce, and Pr. The CIC method was further tested on REE-containing eluents from solvent extraction tests out of fly ash leachates. REE detection from these real processing fluids were reported to achieve 90% to 100% recovery rate from our IC method, compared to ICP-MS results. This study underscores the potential of CIC as a reliable and efficient alternative for REE determination in complex matrices. It also highlights the importance of minimizing select interfering metal ions in solutions to ensure accurate results. The REE CIC method presents a promising, low-maintenance, salt-tolerant, and cost-effective alternative to traditional analytical methods for REE analysis.

detection of rare earth elements (REE)↗

NASA NTRS1995

Molecular indicators for palaeoenvironmental change in a Messinian evaporitic sequence (Vena del Gesso, Italy). II: High-resolution variations in abundances and 13C contents of free and sulphur-bound carbon skeletons in a single marl bed

The extractable organic matter of 10 immature samples from a marl bed of one evaporitic cycle of the Vena del Gesso sediments (Gessoso-solfifera Fm., Messinian, Italy) was analyzed quantitatively for free hydrocarbons and organic sulphur compounds. Nickel boride was used as a desulphurizing agent to recover sulphur-bound lipids from the polar and asphaltene fractions. Carbon isotopic compositions (delta vs PDB) of free hydrocarbons and of S-bound hydrocarbons were also measured. Relationships between these carbon skeletons, precursor biolipids, and the organisms producing them could then be examined. Concentrations of S-bound lipids and free hydrocarbons and their delta values were plotted vs depth in the marl bed and the profiles were interpreted in terms of variations in source organisms, 13 C contents of the carbon source, and environmentally induced changes in isotopic fractionation. The overall range of delta values measured was 24.7%, from -11.6% for a component derived from green sulphur bacteria (Chlorobiaceae) to -36.3% for a lipid derived from purple sulphur bacteria (Chromatiaceae). Deconvolution of mixtures of components deriving from multiple sources (green and purple sulphur bacteria, coccolithophorids, microalgae and higher plants) was sometimes possible because both quantitative and isotopic data were available and because either the free or S-bound pool sometimes appeared to contain material from a single source. Several free n-alkanes and S-bound lipids appeared to be specific products of upper-water-column primary producers (i.e. algae and cyanobacteria). Others derived from anaerobic photoautotrophs and from heterotrophic protozoa (ciliates), which apparently fed partly on Chlorobiaceae. Four groups of n-alkanes produced by algae or cyanobacteria were also recognized based on systematic variations of abundance and isotopic composition with depth. For hydrocarbons probably derived from microalgae, isotopic variations are well correlated with those of total organic carbon. A resistant aliphatic biomacromolecule produced by microalgae is, therefore, probably an important component of the kerogen. These variations reflect changes in the depositional environment and early diagenetic transformations. Changes in the concentrations of S-bound lipids induced by variations in conditions favourable for sulphurization were discriminated from those related to variations in primary producer assemblages. The water column of the lagoonal basin was stratified and photic zone anoxia occurred during the early and middle stages of marl deposition. During the last stage of the marl deposition the stratification collapsed due to a significant shallowing of the water column. Contributions from anaerobic photoautotrophs were apparently associated with variations in depth of the chemocline.

Non-NASA Center↗

NASA NTRS1986

Unusual relative strengths of the diffuse interstellar bands in some interstellar dust clouds

Some of the diffuse interstellar features (DIBs) in the spectra of certain stars at high galactic latitudes (1 is greater than 15 degrees) are unusually weak or absent while others have the strength expected for their color excess. In some cases the stars are probably reddened by single interstellar clouds. There appear to be three families of DIBs. The effects of these families are examined. The existance of the three families implies that at least three agents cause the DIBs and that the proportions of the agents or the physical conditions giving rise to the DIBs can vary from cloud to cloud.

Krelowski, J.↗

NASA NTRSDate not supplied

Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

The highly dynamic nature of UAVs imposes significant challenges when conducting initial testing ranging from safety risks posed by high-capacity lithium batteries and spinning propellers to rigorous timing demands on controllers and the consequences of failures mid-air. Flight testing of a single vehicle is time and labor intensive due to these challenges and more, and the complexity increases exponentially with the number of vehicles. While simulations and hardware-in-the-loop bench testing can provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure between simulation and a fleet of real UAVs create a sizable gap that must be navigated carefully during system integration. In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, which had the goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, this gap was tackled from two directions. First, a novel mixed-reality simulation environment was engineered to blur the transition from simulation to flight hardware. Second, a fleet of Unmanned Surface Vehicles (USVs) was developed as a test and evaluation platform that more closely represented the final aerial fleet while eliminating many of the risks associated with air vehicles. This paper delves into the second element, analyzing the efficacy of the USV platform in performing system integration testing for the UAV system. In this paper we present the USV fleet and its role in reducing the aforementioned gaps in deployment architecture, software interfaces, and hardware infrastructure when moving from simulation to flight. An overview of the hardware and software onboard the vehicles will be provided along with supporting infrastructure. The system integration process will be documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted during the project. Finally, we will discuss some of the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P Vaughan↗

NASA NTRS2015

An Approach to Quantify Workload in a System of Agents

The role of humans in aviation and other domains continues to shift from manual control to automation monitoring. Studies have found that humans are often poorly suited for monitoring roles, and workload can easily spike in off-nominal situations. Current workload measurement tools, like NASA TLX, use human operators to assess their own workload after using a prototype system. Such measures are used late in the design process and can result in ex- pensive alterations when problems are discovered. Our goal in this work is to provide a quantitative workload measure for use early in the design process. We leverage research in human cognition to de ne metrics that can measure workload on belief-desire-intentions based multi-agent systems. These measures can alert designers to potential workload issues early in design. We demonstrate the utility of our approach by characterizing quantitative differences in the workload for a single pilot operations model compared to a traditional two pilot model.

Single Pilot Operations↗

NASA NTRSDate not supplied

Exploring Multimodal Interactions in Human-Autonomy Teaming Using a Natural User Interface

The creation of a multimodal, natural user interface to facilitate multi-agent interaction is essential to establishing trust among human and machine teammates in multi-agent systems. Trust is being researched, along with trustworthiness, as a path to certification of autonomous systems by the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project at NASA. The Autonomous Mission Experimental Logistics Interactive Assistant (AMELIA) is a natural user interface that enables multimodal interaction and is designed for rapid mission planning. AMELIA is an intelligent system that considers the user’s preferred communication strategies, as well as the time-critical aspect of the multi-agent system decision-making process. Twenty-four participants planned a multi-agent search and rescue mission, with the aid of an intelligent assistant. The results show that while the combined use of touch and speech was faster than speech alone, the single modality, touch, was still the most efficient. Future research should investigate additional input technologies.

Lisa R Le Vie↗

NASA NTRSDate not supplied

Exploring Multimodal Interactions in Human-Autonomy Teaming Using a Natural User Interface

Lisa Renee Le Vie↗