Serious Gaming for Building a Basis of Certification via Trust and Trustworthiness of Autonomous Systems

Autonomous systems governed by a variety of adaptive and nondeterministic algorithms are being planned for inclusion into safety-critical environments, such as unmanned aircraft and space systems in both civilian and military applications. However, until autonomous systems are proven and perceived to be capable and resilient in the face of unanticipated conditions, humans will be reluctant or unable to delegate authority, remaining in control aided by machine-based information and decision support. Proving capability, or trustworthiness, is a necessary component of certification. Perceived capability is a component of trust. Trustworthiness is an attribute of a cyber-physical system that requires context-driven metrics to prove and certify. Trust is an attribute of the agents participating in the system and is gained over time and multiple interactions through trustworthy behavior and transparency. Historically, artificial intelligence and machine learning systems provide answers without explanation - without a rationale or insight into the machine thinking. In order to function as trusted teammates, machines must be able to explain their decisions and actions. This transparency is a product of both content and communication. NASAs Autonomy Teaming & TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project seeks to build a basis for certification of autonomous systems via establishing metrics for trustworthiness and trust in multi-agent team interactions, using AI (Artificial Intelligence) explainability and persistent modeling and simulation, in the context of mission planning and execution, with analyzable trajectories. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ATTRACTOR modeling and simulation environment is similar to online gaming environments in which player (aka agent) participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual agents active participation. This persistent simulation environment will accommodate individual agents, groups of self-organizing agents, and large-scale infrastructure behavior. The effects of the emerging adaptation and coevolution can be observed and measured to building a basis of measurable trustworthiness and trust, toward certification of safety-critical autonomous systems

Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification

f autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautical Solutions (CAS) Project. One critical research element of ATTRACTOR is explainability of the decision-making across relevant subsystems of an autonomous system. The ability to explain why an autonomous system makes a decision is needed to establish a basis of trustworthiness to safely complete a mission. Convolutional Neural Networks (CNNs) are popular visual object classifiers that have achieved high levels of classification performances without clear insight into the mechanisms of the internal layers and features. To explore the explainability of the internal components of CNNs, we reviewed three feature visualization methods in a layer-by-layer approach using aviation related images as inputs. Our approach to this is to analyze the key components of a classification event in order to generate component labels for features of the classified image at different layers of depths. For example, an airplane has wings, engines, and landing gear. These could possibly be identified somewhere in the hidden layers from the classification and these descriptive labels could be provided to a human or machine teammate while conducting a shared mission and to engender trust. Each descriptive feature may also be decomposed to a combination of primitives such as shapes and lines. We expect that knowing the combination of shapes and parts that create a classification will enable trust in the system and insight into creating better structures for the CNN

Future of Interoperability (IR) Research

This presentation provides a forum for discussion about the work presented by the Interoperability (IR) focus area and the future of interoperability research. Of particular interest will be the direction IR research should take in future year

A Planning Pipeline for Large Multi-Agent Missions

In complex multi-agent applications, human operators are often tasked with planning and managing large heterogeneous teams of humans and autonomous vehicles. Although the use of these autonomous vehicles broadens the scope of meaningful applications, many of their systems remain unintuitive and difficult to master for human operators whose expertise lies in the application domain and not at the platform level. Current research focuses on the development of individual capabilities necessary to plan multi-agent missions of this scope, placing little emphasis on the integration of these components in to a full pipeline. The work presented in this paper presents a complete and user-agnostic planning pipeline for large multiagent missions known as the HOLII GRAILLE. The system takes a holistic approach to mission planning by integrating capabilities in human machine interaction, flight path generation, and validation and verification. Components modules of the pipeline are explored on an individual level, as well as their integration into a whole system. Lastly, implications for future mission planning are discussed

Serious Gaming for Test & Evaluation of Clean-Slate (Ab Initio) National Airspace System (NAS) Designs

Incremental approaches to air transportation system development inherit current architectural constraints, which, in turn, place hard bounds on system capacity, efficiency of performance, and complexity. To enable airspace operations of the future, a clean-slate (ab initio) airspace design(s) must be considered. This ab initio National Airspace System (NAS) must be capable of accommodating increased traffic density, a broader diversity of aircraft, and on-demand mobility. System and subsystem designs should scale to accommodate the inevitable demand for airspace services that include large numbers of autonomous Unmanned Aerial Vehicles and a paradigm shift in general aviation (e.g., personal air vehicles) in addition to more traditional aerial vehicles such as commercial jetliners and weather balloons. The complex and adaptive nature of ab initio designs for the future NAS requires new approaches to validation, adding a significant physical experimentation component to analytical and simulation tools. In addition to software modeling and simulation, the ability to exercise system solutions in a flight environment will be an essential aspect of validation. The NASA Langley Research Center (LaRC) Autonomy Incubator seeks to develop a flight simulation infrastructure for ab initio modeling and simulation that assumes no specific NAS architecture and models vehicle-to-vehicle behavior to examine interactions and emergent behaviors among hundreds of intelligent aerial agents exhibiting collaborative, cooperative, coordinative, selfish, and malicious behaviors. The air transportation system of the future will be a complex adaptive system (CAS) characterized by complex and sometimes unpredictable (or unpredicted) behaviors that result from temporal and spatial interactions among large numbers of participants. A CAS not only evolves with a changing environment and adapts to it, it is closely coupled to all systems that constitute the environment. Thus, the ecosystem that contains the system and other systems evolves with the CAS as well. The effects of the emerging adaptation and co-evolution are difficult to capture with only combined mathematical and computational experimentation. Therefore, an ab initio flight simulation environment must accommodate individual vehicles, groups of self-organizing vehicles, and large-scale infrastructure behavior. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ab initio simulation environment is similar to online gaming environments in which player participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual player's active participation

Object Persistence and Availability in Digital Libraries

We have studied object persistence and availability of 1,000 digital library (DL) objects. Twenty World Wide Web accessible DLs were chosen and from each DL, 50 objects were chosen at random. A script checked the availability of each object three times a week for just over 1 year for a total of 161 data samples. During this time span, we found 31 objects (3% of the total) that appear to no longer be available: 24 from PubMed Central, 5 from IDEAS, 1 from CogPrints, and 1 from ETD

Hardware design optimization for human motion tracking systems

A key component of any interactive computer graphics application is the system for tracking user or input device motion. An accurate estimate of the position and/or orientation of the virtual world tracking targets is critical to effectively creating a convincing virtual experience. Tracking is one of the pillars upon which a virtual reality environment is built and it imposes a fundamental limit on how real the reality of Virtual Reality can be. Whether working on a new or modified tracking system, designers typically begin the design process with requirements for the working volume, the expected user motion, and the infrastructure. Considering these requirements they develop a candidate design that includes one or more tracking mediums (optical, acoustic, etc.), associated source/sensor devices (hardware), and an algorithm (software) for combining the information from the devices. They then simulate the candidate system to estimate the performance for some specific motion paths. Thus the predictions of such traditional simulations typically include the combined effect of hardware and algorithm choices, but only for the chosen motion paths. Before tracker algorithm selection, and irrespective of the motion paths, it is the choice and configuration of the source/sensor devices that are critical to performance. The global limitations imposed by these hardware design choices set a limit on the quantity and quality of the available information (signal) for a given system configuration, and they do so in complex and sometimes unexpected ways. This complexity often makes it difficult for designers to predict or develop intuition about the expected performance impact of adding, removing, or moving source/sensor devices, changing the device parameters, etc. This research introduces a stochastic framework for evaluating and comparing the expected performance of sensing systems for interactive computer graphics. Incorporating models of the sensor devices and expected user motion dynamics, this framework enables complimentary system- and measurement-level hardware information optimization, independent of algorithm and motion paths. The approach for system-level optimization is to estimate the asymptotic position and/or orientation uncertainty at many points throughout a desired working volume or surface, and to visualize the results graphically. This global performance estimation can provide both a quantitative assessment of the expected performance and intuition about how to improve the type and arrangement of sources and sensors, in the context of the desired working volume and expected scene dynamics. Using the same model components required for these system-level optimization, the optimal sensor sampling time can be determined with respect to the expected scene dynamics for measurement-level optimization. Also presented is an experimental evaluation to support the verification of asymptotic analysis of tracking system hardware design along with theoretical analysis aimed at supporting the validity of both the system- and measurement-level optimization methods. In addition, a case study in which both the system- and measurement-level optimization methods to a working tracking system is presented. Finally, Artemis, a software tool for amplifying human intuition and experience in tracking hardware design is introduced. Artemis implements the system-level optimization framework with a visualization component for insight into hardware design choices. Like fluid flow dynamics, Artemis examines and visualizes the information flow of the source and sensor devices in a tracking system, affording interaction with the modeled devices and the resulting performance uncertainty estimate

Towards Informing an Intuitive Mission Planning Interface for Autonomous Multi-Asset Teams via Image Descriptions

Establishing a basis for certification of autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Human-Machine Interface (HMI) team is working to capture and utilize the multitude of ways in which humans are already comfortable communicating mission goals and translate that into an intuitive mission planning interface. Several input/output modalities (speech/audio, typing/text, touch, and gesture) are being considered and investigated in the context human-machine teaming for the ATTRACTOR design reference mission (DRM) of Search and Rescue or (more generally) intelligence, surveillance, and reconnaissance (ISR). The first of these investigations, the Human Informed Natural-language GANs Evaluation (HINGE) data collection effort, is aimed at building an image description database to train a Generative Adversarial Network (GAN). In addition to building an image description database, the HMI team was interested if, and how, modality (spoken vs. written) affects different aspects of the image description given. The results will be analyzed to better inform the designing of an interface for mission planning

Time-Coordination Strategies and Control Laws for Multi-Agent Unmanned Systems

Time-critical coordination tools for unmanned systems can be employed to enforce the type of temporal constraints required in terminal control areas, ensure minimum distance requirements among vehicles are satisfied, and successfully perform coordinated missions. In comparison with previous literature, this paper presents an ampler spectrum of coordination and temporal specifications for unmanned systems, and proposes a general control law that can enforce this range of constraints. The constraint classification presented con- siders the nature of the desired arrival window and the permissible coordination errors to define six different types of time-coordination strategies. The resulting decentralized coordination control law allows the vehicles to negotiate their speeds along their paths in response to information exchanged over the communication network. This control law organizes the different members in the fleet hierarchically per their behavior and informational needs as reference agent, leaders, and followers. Examples and simulation results for all the coordination strategies presented demonstrate the applicability and efficacy of the coordination control law for multiple unmanned systems

A Persistent Simulation Environment for Autonomous Systems

The age of Autonomous Unmanned Aircraft Systems (AUAS) is creating new challenges for the accreditation and certification requiring new standards, policies and procedures that sanction whether a UAS is safe to fly. Establishing a basis for certification of autonomous systems via research into trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautics Solution (CAS) project. Simulation Environments to test and evaluate AUAS decision making may be a low-cost solution to help certify that various AUAS systems are trustworthy enough to be allowed to fly in current general and commercial aviation airspace. NASA is working to build a peer-to-peer persistent simulation (P3 Sim) environment. The P3 Sim will be a Massively Multiplayer Online (MMO) environment were AUAS avatars can interact with a complex dynamic environment and each other. The focus of the effort is to provide AUAS researchers a low-cost intuitive testing environment that will aid training for and assessment of decisions made by autonomous systems such as AUAS. This presentation focuses on the design approach and challenges faced in development of the P3 Sim Environment is support of investigating trustworthiness of autonomous systems