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NASA NTRS2022

Functional Allocation Approach for Separation Assurance for Remotely Piloted Aircraft

A functional analysis framework is employed with the objective of exploring the separation assurance function for the remotely piloted aircraft system. The architecture of the remotely piloted aircraft system—highlighting several of the component systems and the functions resident onboard the remotely piloted aircraft and in the ground control station—is described to provide the context for understanding the complexity of the said system for a detailed functional analysis. The interactions between the agents of the separation assurance function belonging to the air traffic service provider, remotely piloted aircraft system operator and remotely piloted aircraft are described. Separation assurance by air traffic control, remain well clear by the remotely piloted aircraft system and collision avoidance onboard the remotely piloted aircraft are briefly discussed. The functional analysis framework is illustrated by relating the agents to the actions of (a) acquiring the surveillance information, (b) checking for conflicts, (c) creating the solutions for resolving conflicts and (d) implementing the conflict resolution solutions. This example is offered as a template by which a more detailed functional analysis of this and other functions could be developed. The architecture of the remotely piloted aircraft system is described to aid this process.

Functional analysis↗

DOE OSTI2023

DSS-SimPy-RL (Open-DSS and SimPy based Cyber-Physical RL environment) [SWR-23-29]

Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. With the advancement of reinforcement learning (RL) based techniques, gradually the conventional optimization based solvers are being replaced with RL approach where there is uncertainty in the environment such as renewable generation or cyber system emulation. However, to train an agent efficiently, it requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for the power systems based on some well-known simulators, similarly there are environment for communication domains. While majority of the cyber emulators are based in an UNIX environment, the power simulators are based in the Windows-based operating system, the generation of cyber-physical mixed domain RL environment has been challenging. Existing co-simulation methods are efficient but resource and time intensive to generate large scale data set for training RL agents. Hence, this software focuses on development and validation of a mixed domain RL environment using Open DSS for the physical side and leverages a discrete event simulator python package, SimPy, for cyber-side emulation which is Operating Systems agnostic. Further utilizing this software co-simulation and training RL agents for re-routing based resilient control for network reconfiguration and volt-var control in power distribution feeder are performed.

Sahu, Abhijeet↗

NASA NTRS2004

Overview of Intelligent Systems and Operations Development

To achieve NASA's ambitious mission objectives for the future, aircraft and spacecraft will need intelligence to take the correct action in a variety of circumstances. Vehicle intelligence can be defined as the ability to "do the right thing" when faced with a complex decision-making situation. It will be necessary to implement integrated autonomous operations and low-level adaptive flight control technologies to direct actions that enhance the safety and success of complex missions despite component failures, degraded performance, operator errors, and environment uncertainty. This paper will describe the array of technologies required to meet these complex objectives. This includes the integration of high-level reasoning and autonomous capabilities with multiple subsystem controllers for robust performance. Future intelligent systems will use models of the system, its environment, and other intelligent agents with which it interacts. They will also require planners, reasoning engines, and adaptive controllers that can recommend or execute commands enabling the system to respond intelligently. The presentation will also address the development of highly dependable software, which is a key component to ensure the reliability of intelligent systems.

Pallix, Joan↗

NASA NTRS2006

Integrated Vehicle Health Management (IVHM) for Aerospace Systems

To achieve NASA's ambitious Integrated Space Transportation Program objectives, aerospace systems will implement a variety of new concept in health management. System level integration of IVHM technologies for real-time control and system maintenance will have significant impact on system safety and lifecycle costs. IVHM technologies will enhance the safety and success of complex missions despite component failures, degraded performance, operator errors, and environment uncertainty. IVHM also has the potential to reduce, or even eliminate many of the costly inspections and operations activities required by current and future aerospace systems. This presentation will describe the array of NASA programs participating in the development of IVHM technologies for NASA missions. Future vehicle systems will use models of the system, its environment, and other intelligent agents with which they may interact. IVHM will be incorporated into future mission planners, reasoning engines, and adaptive control systems that can recommend or execute commands enabling the system to respond intelligently in real time. In the past, software errors and/or faulty sensors have been identified as significant contributors to mission failures. This presentation will also address the development and utilization of highly dependable sohare and sensor technologies, which are key components to ensure the reliability of IVHM systems.

Baroth, Edmund C.↗

DOE OSTI2024

Characterizing Interaction Uncertainty in Human-Machine Teams

With the increasing use and adoption of artificial intelligence (AI), the reliability of modern data systems will be driven by a tighter teaming between human experts and intelligent machine teammates. As in the case of human-human teams, the success of human-machine teams will also rely on clear communication about mutual goals and actions. In this paper, we combine related literature from cognitive psychology, human-machine teaming, uncertainty in data analysis, and multi-agent systems to propose a new form of uncertainty: interaction uncertainty for characterizing bidirectional communication in human-machine teams. We map the causes and effects of interaction uncertainty and outline potential ways to mitigate uncertainty for mutual trust in a high-consequence real-world scenario.

uncertainty, data analytics, interaction, trust, h↗

NASA NTRS1995

A user-system interface agent

Agent-based technologies answer to several challenges posed by additional information processing requirements in today's computing environments. In particular, (1) users desire interaction with computing devices in a mode which is similar to that used between people, (2) the efficiency and successful completion of information processing tasks often require a high-level of expertise in complex and multiple domains, (3) information processing tasks often require handling of large volumes of data and, therefore, continuous and endless processing activities. The concept of an agent is an attempt to address these new challenges by introducing information processing environments in which (1) users can communicate with a system in a natural way, (2) an agent is a specialist and a self-learner and, therefore, it qualifies to be trusted to perform tasks independent of the human user, and (3) an agent is an entity that is continuously active performing tasks that are either delegated to it or self-imposed. The work described in this paper focuses on the development of an interface agent for users of a complex information processing environment (IPE). This activity is part of an on-going effort to build a model for developing agent-based information systems. Such systems will be highly applicable to environments which require a high degree of automation, such as, flight control operations and/or processing of large volumes of data in complex domains, such as the EOSDIS environment and other multidisciplinary, scientific data systems. The concept of an agent as an information processing entity is fully described with emphasis on characteristics of special interest to the User-System Interface Agent (USIA). Issues such as agent 'existence' and 'qualification' are discussed in this paper. Based on a definition of an agent and its main characteristics, we propose an architecture for the development of interface agents for users of an IPE that is agent-oriented and whose resources are likely to be distributed and heterogeneous in nature. The architecture of USIA is outlined in two main components: (1) the user interface which is concerned with issues as user dialog and interaction, user modeling, and adaptation to user profile, and (2) the system interface part which deals with identification of IPE capabilities, task understanding and feasibility assessment, and task delegation and coordination of assistant agents.

Wakim, Nagi T.↗

NASA NTRS1999

The Effect of Shared Information on Pilot/Controller And Controller/Controller Interactions

In order to respond to the increasing demand on limited airspace system resources, a number of applications of information technology have been proposed, or are under investigation, to improve the efficiency, capacity and reliability of ATM (Asynchronous Transfer Mode) operations. Much of the attention in advanced ATM technology has focused on advanced automation systems or decision aiding systems to improve the performance of individual Pilots or Controllers. However, the most significant overall potential for information technology appears to he in increasing the shared information between human agents such as Pilots, Controllers or between interacting Controllers or traffic flow managers. Examples of proposed shared information systems in the US include; Controller Pilot Databank Communication (CPDLC), Traffic Management Advisor (TMA); Automatic Dependent Surveillance (ADS); Collaborative Decision Making (CDM) and NAS Level Common Information Exchange. Air Traffic Management is fundamentally a human centered process consisting of the negotiation, execution and monitoring of contracts between human agents for the allocation of limited airspace, runway and airport surface resources. The decision processes within ATM tend to be Semistructured. Many of the routine elements in ATM decision making on the part of the Controllers or Pilots are well Structured and can be represented by well defined rules or procedures. However in disrupted conditions, the ATM decision processes are often Unstructured and cannot be reduced to a set of discrete rules. As a consequence, the ability to automate ATM processes will be limited and ATM will continue to be a human centric process where the responsibility and the authority for the negotiation will continue to rest with human Controllers and Pilots. The use of information technology to support the human decision process will therefore be an important aspect of ATM modernization. The premise of many of the proposed shared information systems is that the performance of ATM operations will improve with an increase in Shared Situation Awareness between agents (Pilots, Controller, Dispatchers). This will allow better informed control decisions and an improved ability to negotiate between agents. A common information basis may reduce communication load and may increase the level of collaboration in the decision process. In general, information sharing is expected to have advantages for all agents within the system. However there are important questions which remain to be,addressed. For example: What shared information is most important for developing effective Shared Situation Awareness? Are there issues of information saturation? Does information parity create ambiguity in control authority? Will information sharing induce undesirable or unstable gaming behavior between agents? This paper will explore the effect of current and proposed information sharing between different ATM agents. The paper will primarily concentrate on bilateral tactical interactions between specific agents (Pilot/Controller; Controller/Controller; Pilot/Dispatcher; Controller/Dispatcher) however it will also briefly discuss multilateral interaction and more strategic interactions.

Hansman, R. John↗

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↗

DOE OSTI2025

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗

NASA NTRS2004

Overcoming Communication Restrictions in Collectives

Many large distributed system are characterized by having a large number of components (eg., agents, neurons) whose actions and interactions determine a %orld utility which rates the performance of the overall system. Such collectives are often subject to communication restrictions, making it difficult for components which try to optimize their own private utilities, to take actions that also help optimize the world utility. In this article we address that coordination problem and derive four utility functions which present different compromises between how aligned a component s private utility is with the world utility and how readily that component can determine the actions that optimize its utility. The results show that the utility functions specifically derived to operate under communication restrictions outperform both traditional methods and previous collective-based methods by up to 75%.

Tumer, Kagan↗

DOE OSTI2022

An Efficient Distributed Reinforcement Learning for Enhanced Multi-Microgrid Management

Economic dispatch in multi-microgrid (MMG) systems requires coordinating distributed energy resources (DERs) of different microgrids, which leads to a significant increase in the number of states for energy management. In these cases, traditional reinforcement learning (RL) approaches become computationally expensive or output a solution that causes extra-operating costs for the system. This paper proposes an RL approach that employs local learning agents to interact with microgrid environments in a distributed manner and aggregates the outcomes to train the global agent to learn the policy for the MMG system. This distributed exploration and aggregation process provides an effective solution and guides the global agent to learn the dispatch policy efficiently. Case studies are performed on a system with three microgrids with different types of DERs. Results obtained using the proposed RL and comparisons with conventional methods substantiate the effectiveness of the proposed approach in terms of operation costs, computation time, and peak-to-average ratio.

Das, Avijit↗

DOE OSTI2025

Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA) (Project Closeout Report)

The goal of this project was to develop and use domain-aware machine learning formulations, based on the Koopman Operator (KO), for modelling multi-scale, multi-disciplinary (e.g., multi-physics), and/or multi-agent systems. The project developed these formulations for the following cases: • Systems with dynamics at two separate time scales, • Systems with a bi-level hierarchical control structure, • Systems with bi-level hierarchical control and dynamics at two separate time scales (the lower level controls operating at the faster time scale), and • Systems with n separate but interacting agents/disciplines (with/without control, respectively); the controls for each agent could include bi-level hierarchical control and dynamics at two separate time scales as described above. The project then defined a set of dynamical systems consisting of different nonlinear oscillators that could be used to test these different formulations and then subsequently learned the KO models for those systems. With the KO models, we were able to do the following: • Quantify system stability, including both long-term and transient behavior, • Quantify the effects of feedbacks between the different time scales and agents/disciplines in terms of those feedbacks’ effects on system stability, • Replace a standard Proportional-Integral (PI) control in the hierarchical control structure with a KO-based Linear-Quadratic Regular (LQR), a form of optimal control, • Calculate optimal supervisory control policies a) with and without time scale separated dynamics at the lower level control levels and b) with both PI and KO-based LQR lower level control policies, and • Calculate dynamic Nash equilibria for multi-agent systems where each agent makes its own control decisions.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2003

Roles for Agent Assistants in Field Science: Understanding Personal Projects and Collaboration

A human-centered approach to computer systems design involves reframing analysis in terms of the people interacting with each other. The primary concern is not how people can interact with computers, but how shall we design work systems (facilities, tools, roles, and procedures) to help people pursue their personal projects, as they work independently and collaboratively? Two case studies provide empirical requirements. First, an analysis of astronaut interactions with CapCom on Earth during one traverse of Apollo 17 shows what kind of information was conveyed and what might be automated today. A variety of agent and robotic technologies are proposed that deal with recurrent problems in communication and coordination during the analyzed traverse. Second, an analysis of biologists and a geologist working at Haughton Crater in the High Canadian Arctic reveals how work interactions between people involve independent personal projects, sensitively coordinated for mutual benefit. In both cases, an agent or robotic system's role would be to assist people, rather than collaborating, because today's computer systems lack the identity and purpose that consciousness provides.

Clancey, William J.↗

NASA NTRS2002

The Design of Collectives of Agents to Control Non-Markovian Systems

The 'Collective Intelligence' (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided 'world' utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional-'team games'. We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents' ability to learn. The implication is that 'learning' is a property only of high-enough dimensional systems.

Lawson, John W.↗

NASA NTRS2004

The Design of Collectives of Agents to Control Non-Markovian Systems

The Collective Intelligence (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided "world" utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional "team games". We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents ability to learn. The implication is that learning is a property only of high-enough dimensional systems.

Lawson, John W.↗

NASA NTRS2000

Adaptivity in Agent-Based Routing for Data Networks

Adaptivity, both of the individual agents and of the interaction structure among the agents, seems indispensable for scaling up multi-agent systems (MAS s) in noisy environments. One important consideration in designing adaptive agents is choosing their action spaces to be as amenable as possible to machine learning techniques, especially to reinforcement learning (RL) techniques. One important way to have the interaction structure connecting agents itself be adaptive is to have the intentions and/or actions of the agents be in the input spaces of the other agents, much as in Stackelberg games. We consider both kinds of adaptivity in the design of a MAS to control network packet routing. We demonstrate on the OPNET event-driven network simulator the perhaps surprising fact that simply changing the action space of the agents to be better suited to RL can result in very large improvements in their potential performance: at their best settings, our learning-amenable router agents achieve throughputs up to three and one half times better than that of the standard Bellman-Ford routing algorithm, even when the Bellman-Ford protocol traffic is maintained. We then demonstrate that much of that potential improvement can be realized by having the agents learn their settings when the agent interaction structure is itself adaptive.

Wolpert, David H.↗

NASA NTRS2002

Using CONFIG for Simulation of Operation of Water Recovery Subsystems for Advanced Control Software Evaluation

A hybrid discrete/continuous simulation tool, CONFIG, has been developed to support evaluation of the operability life support systems. CON FIG simulates operations scenarios in which flows and pressures change continuously while system reconfigurations occur as discrete events. In simulations, intelligent control software can interact dynamically with hardware system models. CONFIG simulations have been used to evaluate control software and intelligent agents for automating life support systems operations. A CON FIG model of an advanced biological water recovery system has been developed to interact with intelligent control software that is being used in a water system test at NASA Johnson Space Center

Malin, Jane T.↗