Automata Learning in Generation of Scenario-Based Requirements in System Development
information technology and software
Automata Learning in Generation of Scenario-Based Requirements in System Development (GSC-TOPS-71)
A technique for fully tractable code generation from requirements
Overview
NASA sensor networks can be highly distributed autonomous systems of systems that must operate with a high degree of reliability. The solar system and planetary exploration networks necessarily experience long communications delays with Earth. The exploration networks are partly and occasionally out of touch with the Earth and mission control for long periods of time, and must operate under extremes of dynamic environmental conditions. Due to the complexity of these systems as well as the distributed and parallel nature of the exploration networks, the exploration networks have an extremely large state space and are impossible to test completely using traditional testing techniques. The more code or instructions that can be generated automatically from a verifiably correct model, the less likely that human developers will introduce errors.
The Technology
In addition, the higher the level of abstraction that developers can work from, as is afforded through the use of scenarios to describe system behavior, the less likely that a mismatch will occur between requirements and implementation and the more likely that the system can be validated. Working from a higher level of abstraction also provides that errors in the system are more easily caught, since developers can more easily see the big picture of the system.
This technology is a technique for fully tractable code generation from requirements, which has an application in other areas such as generation and verification of scripts and procedures, generation and verification of policies for autonomic systems, and may have future applications in the areas of security and software safety. The approach accepts requirements expressed as a set of scenarios and converts them to a process based description. The more complete the set of scenarios, the better the quality of the process based description that is generated. The proposed technology using automata learning to generate possible additional scenarios can be useful in completing the description of the requirements.
Benefits
- The medium reduces partiality of system requirement specifications, system development time and the amount of testing required of a new system
- The medium allows translating the scenario of the system to a script, without the use of an automated inference engine
Applications
- Satellites
- Software Systems
- Sensors
- Robotic Operations
- Spacecraft
- Artificial Intelligence
Similar Results
Algorithms for stabilizing intelligent networks
Some of the current challenges faced by research in artificial intelligence and autonomous control systems include providing self control, resilience, adaptability, and stability for intelligent systems, especially over a long period of time, in changing environments. The Evolvable Neural Software System (ENSS), Formulation for Emotion Embedding in Logic Systems (FEELS), Stability Algorithm for Neural Entities (SANE), and the Logic Expansion for Autonomously Reconfigurable Neural Systems (LEARNS) are foundations for tackling some of these challenges, by providing the basic algorithms evolvable systems could use to manage its own behavior.
These algorithms would allow networks to self regulate, noticing unusual behavior and the circumstances that may have caused that behavior, and then correcting to behave more predictably when similar circumstances are encountered. The process is similar to how psychology in organisms evolved iteratively, eventually finding and keeping better responses to given stimuli.
Kodiak 3D Lidar
NASA Goddard Space Flight Center has developed a 3D lidar system that consists of microelectromechanical systems (MEMS) beam steering, high performance reconfigurable computing, and an in-depth understanding of systems level integration. Kodiak combines a 3D MEMS scanning lidar with a long range narrow FOV telescope to produce a flexible and capable space flight ranging system. Also included is SpaceCube-level processing power to host a variety of algorithms enabling sensing and 6 degrees of freedom.
Enhancing Fault Isolation and Detection for Electric Powertrains of UAVs
The tool developed through this work merges information from the electric propulsion system design phase with diagnostic tools. Information from the failure mode and effect analysis (FMEA) from the system design phase is embedded within a Bayesian network (BN). Each node in the network can represent either a fault, failure mode, root cause or effect, and the causal relationships between different elements are described through the connecting edges.
This novel approach can help Fault Detection and Isolation (FDI), producing a framework capable of isolating the cause of sub-system level fault and degradation.
This system:
Identifies and quantifies the effects of the identified hazards, the severity and probability of their effects, their root cause, and the likelihood of each cause;
Uses a Bayesian framework for fault detection and isolation (FDI);
Based on the FDI output, estimates health of the faulty component and predicts the remaining useful life (RUL) by also performing uncertainty quantification (UQ);
Identifies potential electric powertrain hazards and performs a functional hazard analysis (FHA) for unmanned aerial vehicles (UAVs)/Urban Air Mobility (UAM) vehicles.
Despite being developed for and demonstrated with an application to an electric UAV, the methodology is generalized and can be implemented in other domains, ranging from manufacturing facilities to various autonomous vehicles.
Multi-Protocol Remote Monitoring for Radio Networks
The system revolves around a Communications Remote Monitoring Panel (CRMP), which interfaces with multiple radios operating under different communication protocols. The CRMP intelligently identifies each radio’s communication specification, such as analog, digital, or proprietary protocols, and dynamically adjusts to ensure the correct protocol is used for proper communication. This capability allows for seamless interaction between otherwise incompatible systems. Once connected, the CRMP transmits queries to assess the status of each connected radio to retrieve metrics including operational status, voltage, power status, and internal temperature. The CRMP can also remotely switch radios on or off, or flag problems for maintenance crews. The system supports real-time data acquisition, cross-protocol translation, and scalable network management. Whether deployed at a single control center or across multiple geographically dispersed facilities, the system improves visibility into the health of communication networks while minimizing the need for on-site technicians. Data trends may also inform replacement equipment acquisitions. Its modular, scalable architecture enables simultaneous monitoring of multiple radios, making it ideal for expanding networks or integrating legacy and next-gen systems.
Goddard's Reconfigurable Laser Ranger (GRLR)
NASA Goddard Space Flight Center has developed a low cost, modular, and flexible space flight laser range finder consisting of optics, electronics, and interfaces for satellite servicing missions (i.e. Restore-L) using customized optics. Built upon previous NASA technologies, the system also consists of a high dynamic range receiver and adjustable laser for a wide range of measurements (i.e. multiples of km to sub-meter).
