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All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Artificial Thought is Required for Sustained Autonomy

Current literature primarily addresses what is referred to as “Systems Thinking.” Dr. Marie Morganelli from Southern New Hampshire University states that “Systems thinking is a holistic way to investigate factors and interactions that could contribute to a possible outcome [1]. A mindset more than a prescribed practice, systems thinking provides an understanding of how individuals can work together in different types of teams and through that understanding, create the best possible processes to accomplish just about any-thing.” So, a “Thinking System” is a system that is capable of “systems thinking,” as it should be able to “ …create [utilize] the best possible processes, and possess the [intelligence] to accomplish just about anything.” To achieve this capability, hu-man-like thinking is required. A truly autonomous system must be one that is capable of human-like thinking. Sustained autonomy requires “Thinking Autonomy” (TA) that is enabled by a “Thinking System.”

Autonomous systems↗
NASA NTRSDate not supplied

Autonomy Technologies for Systems of a Moon Base

The aim of the workshop is to “explore emerging autonomy technologies that could enable or enhance mission capabilities, reduce mission risk, and reduce mission cost.” Enhancing mission capabilities will depend on how capable and trustworthy the autonomy implemented is. As systems increase in complexity, and also with multiple interdependent/interacting systems, current autonomy capability and trustworthiness is very low. A paradigm and technology from NASA that addresses this shortcoming will be discussed, the NASA Platform for Autonomous Systems (NPAS). NPAS also happens to address reduction of mission risk and cost. NPAS will be discussed as a capability that is reaching readiness for space use, but also serves as a reference to develop technologies suitable for integrated autonomous operations of lunar systems; encompassing autonomous systems, situational awareness, and reasoning and acting.

Autonomous systems↗
NASA NTRS1978

Prospects of a mathematical theory of human behavior in complex man-machine systems tasks

A hierarchy of human activities is derived by analyzing automobile driving in general terms. A structural description leads to a block diagram and a time-sharing computer analogy. The range of applicability of existing mathematical models is considered with respect to the hierarchy of human activities in actual complex tasks. Other mathematical tools so far not often applied to man machine systems are also discussed. The mathematical descriptions at least briefly considered here include utility, estimation, control, queueing, and fuzzy set theory as well as artificial intelligence techniques. Some thoughts are given as to how these methods might be integrated and how further work might be pursued.

Johannsen, G.↗
NASA NTRS1990

Is thinking computable?

Strong artificial intelligence claims that conscious thought can arise in computers containing the right algorithms even though none of the programs or components of those computers understand which is going on. As proof, it asserts that brains are finite webs of neurons, each with a definite function governed by the laws of physics; this web has a set of equations that can be solved (or simulated) by a sufficiently powerful computer. Strong AI claims the Turing test as a criterion of success. A recent debate in Scientific American concludes that the Turing test is not sufficient, but leaves intact the underlying premise that thought is a computable process. The recent book by Roger Penrose, however, offers a sharp challenge, arguing that the laws of quantum physics may govern mental processes and that these laws may not be computable. In every area of mathematics and physics, Penrose finds evidence of nonalgorithmic human activity and concludes that mental processes are inherently more powerful than computational processes.

Denning, Peter J.↗
NASA NTRSDate not supplied

Implementing Artificial Thinking Autonomy with Model-Based System Engineering

Complex autonomous systems capable of successfully operating independently under ‘known unknowns’ and harsh conditions require paradigm innovation in modern development strategies. In the field of autonomy, developing a system-of-systems which can ostensibly think for itself in the face of ‘unknown unknowns’ is still a field of ongoing research. Maturing the systems architecting and modeling methodologies for developing henceforth named Thinking Autonomous Systems, which are verified with digital mission simulation, can potentially usher in the next generation of artificial intelligence for space exploration. The concept presented in this paper incorporates multiple Model-Based Systems Engineering and simulation methodologies combined as a new paradigm to design a novel, biomimetic thinking autonomy strategy. Anachronistic concepts from classical Kantian philosophy will be leveraged to inspire architectural designs that could be used for complex distributed systems in deep space. To accomplish this, digital transformation of a document-based implementation plan for Thinking Autonomous Systems, generated by experienced NASA software engineers, is implemented for NASA’s Platform for Autonomous Systems by creating descriptive and executable software models in SysML to prototype real-time operating capabilities. This conceptual implementation has been developed by incorporating model-based digital simulations to theorize how a cyberphysical thinking system would achieve specific strategies without crew reliance, while simultaneously being resilient to all operating conditions and remaining functional when devoid of ground communication. Additionally, ensuring that an autonomous system framework is an ethical Artificial Intelligence requires careful consideration of system behavior and accountability, human factors for teaming with a thinking autonomous system, and comparison to other modern approaches used for implementing true autonomy. This paper presents the first steps in formalizing the metacognition required for instantiating a truly Thinking Autonomous System; the approach described symphonizes autonomy characteristics from classical philosophical into a unified software architecture describing human thought. In the future, the foundational models described in this paper can be further leveraged to help advance research into thinking autonomy requirements for future deep space missions as well as for current near-term applications, i.e., living aboard crewed spacecraft like a NASA Gateway cislunar habitat.

Artificial Thought↗
NASA NTRS1986

Systematic errors in determining the propagation direction of interplanetary Alfvenic fluctuations

Analyses of Alfvenic fluctuations in the solar wind contain a systematic effect in which outward propagation is favored. The effect is generally small at and beyond 1 AU in a Mach 10 flow but can become important where the Mach number is lower, e.g., inside 1 AU. If the propagation speeds of the inward and outward propagating Alfvenic fluctuations are properly included, this systematic effect can be removed. A technique for doing this is illustrated using both artificial and spacecraft data. The results suggest that time intervals originally thought to be outward propagating sometimes contain wave number bands in the cross helicity spectrum of inward propagating Alfvenic fluctuations.

Goldstein, M. L.↗
NASA NTRS1971

Artificial microfossils - Experimental studies of permineralization of blue-green algae in silica.

A technique has been developed to artificially fossilize microscopic algae in crystalline silica under conditions of moderately elevated temperature and pressure. The technique is designed to simulate geochemical processes thought to have resulted in the preservation of organic microfossils in Precambrian bedded cherts. In degree of preservation and mineralogic setting, the artificially permineralized microorganisms are comparable to naturally occurring fossil algae.

Oehler, J. H.↗
NASA NTRS2000

AI's Philosophical Underpinnings: A Thinking Person's Walk through the Twists and Turns of Artificial Intelligence's Meandering Path

Few human endeavors can be viewed both as extremely successful and unsuccessful at the same time. This is typically the case when goals have not been well defined or have been shifting in time. This has certainly been true of Artificial Intelligence (AI). The nature of intelligence has been the object of much thought and speculation throughout the history of philosophy. It is in the nature of philosophy that real headway is sometimes made only when appropriate tools become available. Similarly the computer, coupled with the ability to program (at least in principle) any function, appeared to be the tool that could tackle the notion of intelligence. To suit the tool, the problem of the nature of intelligence was soon sidestepped in favor of this notion: If a probing conversation with a computer could not be distinguished from a conversation with a human, then AI had been achieved. This notion became known as the Turing test, after the mathematician Alan Turing who proposed it in 1950. Conceptually rich and interesting, these early efforts gave rise to a large portion of the field's framework. Key to AI, rather than the 'number crunching' typical of computers until then, was viewed as the ability to manipulate symbols and make logical inferences. To facilitate these tasks, AI languages such as LISP and Prolog were invented and used widely in the field. One idea that emerged and enabled some success with real world problems was the notion that 'most intelligence' really resided in knowledge. A phrase attributed to Feigenbaum, one of the pioneers, was 'knowledge is the power.' With this premise, the problem is shifted from 'how do we solve problems' to 'how do we represent knowledge.' A good knowledge representation scheme could allow one to draw conclusions from given premises. Such schemes took forms such as rules,frames and scripts. It allowed the building of what became known as expert systems or knowledge based systems (KBS).

Colombano, Silvano↗
NASA NTRS1998

Applying Space Technology to Enhance Control of an Artificial Arm for Children and Adults With Amputations

The first single function myoelectric prosthetic hand was introduced in the 1960's. This hand was controlled by the electric fields generated by muscle contractions in the residual limb of the amputee user. Electrodes and amplifiers, embedded in the prosthetic socket, measured these electric fields across the skin, which increase in amplitude as the individual contracts their muscle. When the myoelectric signal reached a certain threshold amplitude, the control unit activated a motor which opened or closed a hand-like prosthetic terminal device with a pincher grip. Late in the 1990's, little has changed. Most current myoelectric prostheses still operate in this same, single-function way. To better understand the limitations of the current single-function myoelectric hand and the needs of those who use them, The Institute for Rehabilitation and Research (TIRR), sponsored by the National Institutes of Health (NUH), surveyed approximately 2,500 individuals with upper limb loss [1]. When asked to identify specific features of their current myoelectric prostheses that needed improvement, the survey respondents overwhelmingly identified the lack of wrist and finger movement, as well as poor control capability. However, simply building a mechanism with individual finger and wrist motion is not enough. In the 1960's and 1970's, engineers built a number of more dexterous prosthetic hands. Unfortunately, these were rejected during clinical trials due to a difficult and distracting control interface. The goal of this project, "Applying Space Technology to Enhance Control of an Artificial Arm for Children and Adults with Amputations," was to lay the foundation for a multi-function, intuitive myoelectric control system which requires no conscious thought to move the hand. We built an extensive myoelectric signal database for six motions from ten amputee volunteers, We also tested a control system based on new artificial intelligence techniques on the data from two of these subjects. This data is available to anyone doing myoelectric control research. Its availability is an important contribution to the prosthetics research community, as many researchers do not have access to amputee subjects. Since we collected myoelectric data from subjects' sound arms as well as their residual arms, this database will also prove useful to virtual reality and robotics researchers who want to explore myoelectric-based interfaces between any user and a machine. Currently, one small company (Intelligenta, Inc.) and one university (University of New Brunswick, Canada) are using this myoelectric database under other funding to develop multifunction control systems for prostheses. A prosthetics manufacturer (Liberty Technology, Inc.) is making plans to incorporate the results of their work into an artificial hand capable of several different movements to provide functionality only dreamed of by current myoelectric users. Methods Six adults and four children, all with unilateral, below-elbow amputations served as subjects. Five of the adults (3 male, 2 female, average age 34 years) had amputations due to traumatic injury, while one adult (female, age 32 years) and the four children (3 male, 1 female, average age 13 years) had congenital (i.e. from birth) limb deficiencies.

Atkins, Diane J.↗
NASA NTRS2022

Noble Gases and Nitrogen in Samples of Asteroid Ryugu Record Its Volatile Sources and Recent Surface Evolution

The Hayabusa2 spacecraft retrieved surface and subsurface samples from the carbonaceous near-Earth asteroid (162173) Ryugu, which was expected to be enriched in volatile species. The samples were collected from two locations, one undisturbed surface and the other including material excavated by an artificial impact. Unlike meteorites, these samples have experienced minimal alteration by Earth’s atmosphere. Ryugu is thought to have formed from material ejected (by an impact) from a parent body, which had experienced aqueous alteration (reactions with liquid water) ~4.56 billion years (Gyr) ago. Ryugu’s orbit later migrated from the main asteroid belt to become a near-Earth asteroid.

volatile↗
NASA NTRS1991

Comparison of polynomial approximations and artificial neural nets for response surfaces in engineering optimization

Engineering optimization problems involve minimizing some function subject to constraints. In areas such as aircraft optimization, the constraint equations may be from numerous disciplines such as transfer of information between these disciplines and the optimization algorithm. They are also suited to problems which may require numerous re-optimizations such as in multi-objective function optimization or to problems where the design space contains numerous local minima, thus requiring repeated optimizations from different initial designs. Their use has been limited, however, by the fact that development of response surfaces randomly selected or preselected points in the design space. Thus, they have been thought to be inefficient compared to algorithms to the optimum solution. A development has taken place in the last several years which may effect the desirability of using response surfaces. It may be possible that artificial neural nets are more efficient in developing response surfaces than polynomial approximations which have been used in the past. This development is the concern of the work.

Carpenter, William C.↗
NASA NTRS2018

Biocene 2018 - Welcome to the Period of New Life

Biocene is the period of new life. When our descendants look back at this period in time, they will see evidence, in the geologic and electronic record, of anthropic climate change, growing population, and scarcity of resources. But they will also see the rebirth of human ingenuity as we overcame the challenges that faced us through nature-inspired exploration. The Periodic Table of Life (PeTaL) is a proposed tool and open source framework that uses artificial intelligence to aid in the systematic inquiry of biology for its application to human systems. This presentation defines the PeTaL concept and workflow. Biomimicry, biophysics, biomimetics, bionics and numerous other terms refer to the use of biology and biological principles to inform practices in other disciplines. For the most part, the domain of inquiry in these fields have been confined to extant biological models with the proponents of biomimicry often citing the evolutionary success of extant organisms relative to extinct ones. The primary objective of this paper is to expand the domain of inquiry for human processes that seek to model those that are, were or could be found in nature with examples that relate to the field of aerospace and to spur development of tools that can work together to accelerate the use of artificial intelligence in problem solving. Specifically specialized fields such as paleomimesis, anthropomimesis and physioteleology are proposed in conjunction with artificial evolution. Blockchain technology may be vital in allowing open source design tools such as PeTaL to democratize design and yet protect intellectual property. The overarching philosophy outlined here can be thought of as physiomimetics, a holistic and systematic way of learning from natural history. The backbone of PeTaL integrates an unstructured database with an ontological model consisting of function, morphology, environment, state of matter and ecosystem. Tools include text classification, thesaurus, data visualization, and analysis. Applications of PeTaL include guiding human space exploration, understanding human and geological history, and discovering new or extinct life.

Biocene↗
NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.

Ryan A. Lacinski↗
NASA NTRS2018

A First Look at the Evolution of Flight Crew Requirements for Emerging Market Aircraft

This is an exciting time for aviation. New vehicle and airspace technologies promise large increases in the number of aircraft in operation. One critical technology for these emerging markets is the increased use of automated systems to reduce pilot skill, training, and proficiency requirements. While the use of these systems promises to reduce or eliminate pilot functions in the long-term, the technology development for the required functions will necessitate a phased transition. The transition to, and adoption of automated systems will generate new safety challenges. This paper is a first look at a model to help frame flight crew functions for evaluation of future operational requirements. The model is intended to provide required flight crew functions regardless of whether the functions are performed by human or artificial agent. It is hoped that the model will be useful in identifying safety challenges and enabling a safe transition for the new aviation markets. The paper presents some background for a model for framing the flight crew function model and some thoughts about next steps.

safety-critical automation↗
NASA NTRS1995

Kinetic Kelvin-Helmholtz instability at a finite sized object

Two-dimensional hybrid simulations with particle ions and fluid electrons are used to calculate the kinetic evolution of the self-consistent flow around a two-dimensional obstacle with zero intrinsic magnetic field. Plasma outlfow from the obstacle is used to establish a boundary layer between the incoming solar wind and the outgoing plasma. Because the self-consistent flow solution, a velocity shear is naturally set up at this interface, and since the magnetic field for these simulations is transverse to this flow, the Kelvin-Helmholtz (K-H) instability can be excited at low-velocity shear. Simulations demonstrate the existence of the instability even near the subsolar location, which normally is thought to be stable to this instability. The apparent reason for this result is the overall time dependence at the boundary layer, which gives rise to a Rayleigh-Taylor like instability which provides seed perturbations for the K-H instability. These results are directly applicable to Venus, comets, artificial plasma releases, and laser target experiments. This result has potentially important ramifications for the interpretation of observational results as well as for an estimation of the cross-field transport. The results suggest that the K-H instability may play a role in dayside processes and the Venus ionopause, and may exist within the context of more general situations, for example, the Earth's magnetopause.

Thomas, V. A.↗