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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

An Architecture for Controlling Multiple Robots

The Control Architecture for Multirobot Outpost (CAMPOUT) is a distributed-control architecture for coordinating the activities of multiple robots. In the CAMPOUT, multiple-agent activities and sensor-based controls are derived as group compositions and involve coordination of more basic controllers denoted, for present purposes, as behaviors. The CAMPOUT provides basic mechanistic concepts for representation and execution of distributed group activities. One considers a network of nodes that comprise behaviors (self-contained controllers) augmented with hyper-links, which are used to exchange information between the nodes to achieve coordinated activities. Group behavior is guided by a scripted plan, which encodes a conditional sequence of single-agent activities. Thus, higher-level functionality is composed by coordination of more basic behaviors under the downward task decomposition of a multi-agent planner

Aghazarian, Hrand↗
DOE OSTI2025

Dual Inhibitors of SARS-CoV-2 3CL Protease and Human Cathepsin L Containing Glutamine Isosteres Are Anti-CoV-2 Agents

SARS-CoV-2 3CL protease (Main protease) and human cathepsin L are proteases that play unique roles in the infection of human cells by SARS-CoV-2, the causative agent of COVID-19. Both proteases recognize leucine and other hydrophobic amino acids at the P 2 position of a peptidomimetic inhibitor. At the P 1 position, cathepsin L accepts many amino acid side chains, with a partial preference for phenylalanine, while 3CL-PR protease has a stringent specificity for glutamine or glutamine analogues. We have designed, synthesized, and evaluated peptidomimetic aldehyde dual-target (dual-acting) inhibitors using two peptide scaffolds based on those of two Pfizer 3CL-PR inhibitors, Nirmatrelvir, and PF-835321. Our inhibitors contain glutamine isosteres at the P 1 position, including 2-pyridon-3-yl-alanine, 3-pyridinyl-alanine, and 1,3-oxazo-4-yl-alanine groups. Inhibition constants for these new inhibitors ranged from K i = 0.6–18 nM (cathepsin L) and K i = 2.6–124 nM (3CL-PR), for which inhibitors with the 2-pyridon-3-yl-alanal substituent were the most potent for 3CL-PR. The anti-CoV-2 activity of these inhibitors ranged from EC 50 = 0.47–15 μM. X-ray structures of the peptidomimetic aldehyde inhibitors of 3CL-PR with similar scaffolds all demonstrated the formation of thiohemiacetals with Cys 145 , and hydrogen-bonding interactions with the heteroatoms of the pyridon-3-yl-alanyl group, as well as the nitrogen of the N-terminal indole and its appended carbonyl group at the P 3 position. The absence of these hydrogen bonds for the inhibitors containing the 3-pyridinyl-alanyl and 1,3-oxazo-4-yl-alanyl groups was reflected in the less potent inhibition of the inhibitors with 3CL-PR. In summary, our studies demonstrate the value of a second generation of cysteine protease inhibitors that comprise a single agent that acts on both human cathepsin L and SARS-CoV-2 3CL protease. Such dual-target inhibitors will provide anti-COVID-19 drugs that remain active despite the development of resistance due to mutation of the viral protease. Such dual-target inhibitors are more likely to remain useful therapeutics despite the emergence of inactivating mutations in the viral protease because the human cathepsin L will not develop resistance. This particular dual-target approach is innovative since one of the targets is viral (3CL-PR) required for viral protein maturation and the other is human (hCatL) which enables viral infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Engineered IL-18 variants with half-life extension and improved stability for cancer immunotherapy

Background The pro-inflammatory cytokine, interleukin-18 (IL-18), plays an instrumental role in bolstering anti-tumor immunity. However, the therapeutic application of IL-18 has been limited due to its susceptibility to neutralization by IL-18 binding protein (IL-18BP), short in vivo half-life, and unfavorable physicochemical properties. Methods In order to overcome the poor drug-like properties of IL-18, we installed an artificial disulfide bond, removed the native, unpaired cysteines, and fused the stabilized cytokine to an IgG Fc domain. The stability, potency, pharmacokinetic and pharmacodynamic properties as well as efficacy of disulfide-stabilized IL-18 Fc-fusion (dsIL-18-Fc) were assessed via in vitro and in vivo studies. Results The stability and mammalian host cell production yields of dsIL-18-Fc were improved, compared to the wild-type (WT) cytokine, while maintaining its biological potency and interactions with IL-18 receptor α (IL-18Rα) and IL-18BP. Recombinant fusion of the cytokine to an IgG Fc domain provided extended half-life. Notably, despite maintaining sensitivity to IL-18BP, dsIL-18-Fc was effective at activating both T and natural killer (NK) cells, and elicited a strong anti-tumor response, either as a single agent, or in conjunction with anti-programmed cell death-ligand 1 (anti-PD-L1) therapy. Conclusions We engineered IL-18 for reinforced stability, extended half-life, and improved manufacturability. The therapeutic benefit of dsIL-18-Fc, coupled with a more favorable manufacturability profile and enhanced drug-like properties, underscores the potential utility of this engineered cytokine in cancer immunotherapy.

Immunology↗
NASA NTRS1991

NASA TileWorld manual (system version 2.2)

The commands are documented of the NASA TileWorld simulator, as well as providing information about how to run it and extend it. The simulator, implemented in Common Lisp with Common Windows, encodes a particular range in a spectrum of domains, for controllable research experiments. TileWorld consists of a two dimensional grid of cells, a set of polygonal tiles, and a single agent which can grasp and move tiles. In addition to agent executable actions, there is an external event over which the agent has not control; this event correspond to a 'gust of wind'.

Philips, Andrew B.↗
NASA NTRS1992

Experiments with a decision-theoretic scheduler

This paper describes DTS, a decision-theoretic scheduler designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver, introduced a similar approach to solving single-agent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

Hansson, Othar↗
NASA NTRS1993

Decision-theoretic control of EUVE telescope scheduling

This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

Hansson, Othar↗
NASA NTRS1994

DTS: Building custom, intelligent schedulers

DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solving single-agent and adversarial graph search problems.

Hansson, Othar↗
NASA NTRS1999

Reinforcement Learning in a Nonstationary Environment: The El Farol Problem

This paper examines the performance of simple learning rules in a complex adaptive system based on a coordination problem modeled on the El Farol problem. The key features of the El Farol problem are that it typically involves a medium number of agents and that agents' pay-off functions have a discontinuous response to increased congestion. First we consider a single adaptive agent facing a stationary environment. We demonstrate that the simple learning rules proposed by Roth and Er'ev can be extremely sensitive to small changes in the initial conditions and that events early in a simulation can affect the performance of the rule over a relatively long time horizon. In contrast, a reinforcement learning rule based on standard practice in the computer science literature converges rapidly and robustly. The situation is reversed when multiple adaptive agents interact: the RE algorithms often converge rapidly to a stable average aggregate attendance despite the slow and erratic behavior of individual learners, while the CS based learners frequently over-attend in the early and intermediate terms. The symmetric mixed strategy equilibria is unstable: all three learning rules ultimately tend towards pure strategies or stabilize in the medium term at non-equilibrium probabilities of attendance. The brittleness of the algorithms in different contexts emphasize the importance of thorough and thoughtful examination of simulation-based results.

Bell, Ann Maria↗
DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2003

Scoping Planning Agents With Shared Models

In this paper we provide a formal framework to define the scope of planning agents based on a single declarative model. Having multiple agents sharing a single model provides numerous advantages that lead to reduced development costs and increase reliability of the system. We formally define planning in terms of extensions of an initial partial plan, and a set of flaws that make the plan unacceptable. A Flaw Filter (FF) allows us to identify those flaws relevant to an agent. Flaw filters motivate the Plan Identification Function (PIF), which specifies when an agent is is ready hand control to another agent for further work. PIFs define a set of plan extensions that can be generated from a model and a plan request. FFs and PIFs can be used to define the scope of agents without changing the model. We describe an implementation of PIFsand FFswithin the context of EUROPA, a constraint-based planning architecture, and show how it can be used to easily design many different agents.

Bedrax-Weiss, Tania↗
NASA NTRS1982

Chemical weathering and the Viking biology experiments on Mars

Since the publication of studies describing models that do not require external energy sources, new LR (labeled release) test standards module studies have been reported by Levin and Straat (1981). These studies impose important constraints on the various models, and they offer strong support for the 'frost-weathering' model. These findings are discussed here, and it is proposed that the activity of all three biology experiments can now adequately be explained as a consequence of a single active agent produced by contemporary chemical weathering. Some of the possible consequences and implications of contemporary weathering for the current state and evolution of the regolith and atmosphere are briefly discussed.

Huguenin, R. L.↗
NASA NTRS2000

Cooperative Robotics and the Search for Extraterrestrial Life

If we think tenuous abodes of life may be hiding in remote extraterrestrial environmental niches, and if we want to assess the biological status of a given locale or entire planet before sending humans (perhaps because of contamination concerns or other motivations) then we face the challenge of robotically exploring a large space efficiently and in enough detail to have confidence in our assessment of the biological status of the environment in question. On our present schedule of perhaps two or so missions per opportunity, we will likely need a different exploratory approach than singular stationary landers or singular rover missions or sample return, because there appear to be fundamental limitations in these mission profiles to-obtain the many samples we will likely need if we want to have confidence in assessing the biological status of an environment in which life could be hiding in remote environmental niches. Singular rover missions can potentially accommodate sampling over a fairly large area, but are still limited by range and can be a single point of failure. More importantly, such mission profiles have limited payload capabilities which are unlikely to meet the demanding requirements of life-detection. Sample return has the advantage of allowing sophisticated analysis of the sample, but also has the severe limitations associated with only being able to bring back a few samples. This presentation will suggest two cooperative robotic approaches for exploration that have the potential to overcome these difficulties and facilitate efficient and thorough life-detecting exploration of a large space. Given the two premises state above, it appears at least two fundamental challenges have to be met simultaneously: coverage of a large space and bringing to bear a sophisticated suite of detection and experimental payloads on any specific location in order to address a major challenge in looking for extraterrestrial life: namely, executing a wide variety of detection scenarios and in situ experiments in order to gather the required data for a confident assessment that life has been detected and to, more generally, cover a wide range of extraterrestrial life possibilities. Cooperative robotics ]ends itself to this kind of problem because cooperation among the combined capabilities of a variety of simple single function agents can give rise to fairly complex task execution such as the search for and detection of extraterrestrial life.

Lupisella, Mark L.↗
NASA NTRS2000

Cooperative Robotics and the Search for Extraterrestrial Life

If we think tenuous abodes of life may be hiding in remote extraterrestrial environmental niches, and if we want to assess the biological status of a given locale or entire planet before sending humans (perhaps because of contamination concerns or other motivations) then we face the challenge of robotically exploring a large space efficiently and in enough detail to have confidence in our assessment of the biological status of the environment in question. On our present schedule of perhaps two or so missions per opportunity, we will likely need a different exploratory approach than singular stationary landers or singular rover missions or sample return, because there appear to be fundamental limitations in these mission profiles to obtain the many samples we will likely need if we want to have confidence in assessing the biological status of an environment in which life could be hiding in remote environmental niches. Singular rover missions can potentially accommodate sampling over a fairly large area, but are still limited by range and can be a single point of failure. More importantly, such mission profiles have limited payload capabilities which are unlikely to meet the demanding requirements of life-detection. Sample return has the advantage of allowing sophisticated analysis of the sample, but also has the severe limitations associated with only being able to bring back a few samples. This presentation will suggest two cooperative robotic approaches for exploration that have the potential to overcome these difficulties and facilitate efficient and thorough life-detecting exploration of a large space. Given the two premises stated above, it appears at least two fundamental challenges have to be met simultaneously: (1) coverage of a large space and (2) bringing to bear a sophisticated suite of detection and experimental payloads on any specific location in order to address a major challenge in looking for extraterrestrial life: namely, executing a wide variety of detection scenarios and in situ experiments in order to gather the required data for a confident assessment that life has been detected and to, more generally, cover a wide range of extraterrestrial life possibilities. Cooperative robotics lends itself to this kind of problem because cooperation among the combined capabilities of a variety of simple single function agents can give rise to fairly complex task execution such as the search for and detection of extraterrestrial life.

Lupisella, M. L.↗
NASA NTRS2001

Neuroprotection for the new millennium. Matchmaking pharmacology and technology

A major theme of the 1990s in the pathophysiology of nervous system injury has been the multifactorial etiology of irreversible injury. Multiple causes imply multiple opportunities for therapeutic intervention--hence the abandonment of the "magic bullet" single pharmacologic agent for neuroprotection in favor of pharmacologic "cocktails". A second theme of the 1990s has been the progress in technology for neuroprotection, minimally- or non-invasive monitoring as well as treatment. Cardiac stenting has eliminated the need, in many cases, for open heart surgery; deep brain stimulation for Parkinson's disease has offered significant improvement in quality of life for many who had exhausted cocktail drug treatment for their disease. Deep brain stimulation of the subthalamic nucleus offers a novel treatment for Parkinson's disease where a technological advance may actually be an intervention with effects that are normally expected from pharmacologic agents. Rather than merely "jamming" the nervous system circuits involved in Parkinson's disease, deep brain stimulation of the subthalamic nucleus appears to improve the neurotransmitter imbalance that lies at the heart of Parkinson's disease. It may also slow the progression of the disease. Given the example of deep brain stimulation of the subthalamic nucleus for Parkinson's disease, in future one may expect other technological or "hardware" interventions to influence the programming or "software" of the nervous system's physiologic response in certain disease states.

Review Literature↗
NASA NTRS2020

Disk-resolved Photometric Properties of Pluto and the Coloring Materials across its Surface

A multiwavelength regionally dependent photometric analysis of Pluto's anti-Charon-facing hemisphere using images collected by New Horizons' Multispectral Visible Imaging Camera (MVIC) reveals large variations in the absolute value and spectral slope of the single-scattering albedo. Four regions of interest are analyzed: the dark equatorial belt, Pluto's north pole, nitrogen-rich regions, and the mid-latitude terrains. Regions dominated by volatile ices such as Lowell Regio and Sputnik Planitia present single-scattering albedos of ~0.98 at 492 nm, almost neutral across MVIC's visible wavelength range (400–910 nm), indicating limited contributions from tholin materials. Pluto's dark equatorial regions, informally named Cthulhu and Krun Maculae, have single-scattering albedos of ~0.16 at 492 nm and are the reddest regions. Applying the Hapke radiative transfer model to combined MVIC and Linear Etalon Imaging Spectral Array (LEISA) spectra (400–2500 nm) of Cthulhu Macula and Lowell Regio successfully reproduces the spectral properties of these two regions of dramatically disparate coloration, composition, and morphology. Since this model uses only a single coloring agent, very similar to the Titan-like tholin of Khare et al., to account for all of Pluto's colors, this result supports the Grundy et al. conclusion that Pluto's coloration is the result of photochemical products mostly produced in the atmosphere. Although cosmic rays and extreme ultraviolet photons reach Pluto's surface where they can drive chemical processing, observations of diverse surface colors do not require different chemical products produced in different environments. We report a correction scaling factor in the LEISA radiometric calibration of 0.74 ± 0.05.

Silvia Protopapa↗
NASA NTRS2002

CATS-based Air Traffic Controller Agents

This report describes intelligent agents that function as air traffic controllers. Each agent controls traffic in a single sector in real time; agents controlling traffic in adjoining sectors can coordinate to manage an arrival flow across a given meter fix. The purpose of this research is threefold. First, it seeks to study the design of agents for controlling complex systems. In particular, it investigates agent planning and reactive control functionality in a dynamic environment in which a variety perceptual and decision making skills play a central role. It examines how heuristic rules can be applied to model planning and decision making skills, rather than attempting to apply optimization methods. Thus, the research attempts to develop intelligent agents that provide an approximation of human air traffic controller behavior that, while not based on an explicit cognitive model, does produce task performance consistent with the way human air traffic controllers operate. Second, this research sought to extend previous research on using the Crew Activity Tracking System (CATS) as the basis for intelligent agents. The agents use a high-level model of air traffic controller activities to structure the control task. To execute an activity in the CATS model, according to the current task context, the agents reference a 'skill library' and 'control rules' that in turn execute the pattern recognition, planning, and decision-making required to perform the activity. Applying the skills enables the agents to modify their representation of the current control situation (i.e., the 'flick' or 'picture'). The updated representation supports the next activity in a cycle of action that, taken as a whole, simulates air traffic controller behavior. A third, practical motivation for this research is to use intelligent agents to support evaluation of new air traffic control (ATC) methods to support new Air Traffic Management (ATM) concepts. Current approaches that use large, human-in-the-loop simulations are unquestionably valuable for this purpose, but pose considerable logistical, fiscal, and experimental control problems. First, data analysis is extremely complicated, owing simply to the large number of participants and data sources in such simulations. In addition, experienced human air traffic controllers working adjacent sectors tend to flexibly adapt to the evolving control problem - potentially shifting to other strategies than those under investigation. In addition, their performance is tightly coupled to the control interface, which in the development phase may support some concepts and supporting strategies better than others. A simple shift in strategy by one controller can change the character of a particular traffic scenario dramatically, which makes experimental comparison of ATC performance under different traffic scenarios difficult. Training a given team of controllers on operations under a new ATM concept for a sufficient period of time could avert such difficulties, but instituting an adequate training program is expensive and logistically difficult.

Callantine, Todd J.↗
DOE OSTI2026

NeuDiff Agent: a governed AI workflow for single-crystal neutron crystallography

Large-scale facilities increasingly face analysis and reporting latency as a limiting step in scientific throughput, particularly for structural studies that require iterative reduction, integration, refinement and validation. To improve the time to result and analysis efficiency, NeuDiff Agent is introduced as a governed, tool-using AI workflow for TOPAZ at the Spallation Neutron Source. NeuDiff Agent takes instrument data through reduction, integration, refinement and validation to a validated crystal structure and a publication-ready CIF. NeuDiff Agent coordinates established crystallographic tools under explicit governance by restricting actions to allowlisted tools, enforcing fail-closed verification gates at key workflow boundaries, and capturing complete provenance for inspection, auditing and controlled replay. The present benchmark is limited to structural crystallography for periodic structures; magnetic structure analysis and incommensurate or superspace refinement are outside the scope of the current workflow. Performance is assessed using a fixed prompt protocol and repeated end-to-end runs with two large language model backends, with user and machine time partitioned and intervention burden and recovery behaviors quantified under gating. In a reference-case benchmark, NeuDiff Agent reduces wall time from 435 min (manual) to 86.5 ± 4.7 to 94.4 ± 3.5 min (4.6–5.0× faster) while producing a validated CIF with no checkCIF level A or B alerts. These results establish a practical route to deploy agentic AI in facility crystallography while preserving traceability and publication-facing validation requirements.

Xiao, Zhongcan [ORNL] (ORCID:0000000220761961)↗