Engineering topics

Ong, James

Publications and source records attributed to Ong, James.

3 records

NASA NTRS2015

Software Testbed for Developing and Evaluating Integrated Autonomous Subsystems

To implement fault tolerant autonomy in future space systems, it will be necessary to integrate planning, adaptive control, and state estimation subsystems. However, integrating these subsystems is difficult, time-consuming, and error-prone. This paper describes Intelliface/ADAPT, a software testbed that helps researchers develop and test alternative strategies for integrating planning, execution, and diagnosis subsystems more quickly and easily. The testbed's architecture, graphical data displays, and implementations of the integrated subsystems support easy plug and play of alternate components to support research and development in fault-tolerant control of autonomous vehicles and operations support systems. Intelliface/ADAPT controls NASA's Advanced Diagnostics and Prognostics Testbed (ADAPT), which comprises batteries, electrical loads (fans, pumps, and lights), relays, circuit breakers, invertors, and sensors. During plan execution, an experimentor can inject faults into the ADAPT testbed by tripping circuit breakers, changing fan speed settings, and closing valves to restrict fluid flow. The diagnostic subsystem, based on NASA's Hybrid Diagnosis Engine (HyDE), detects and isolates these faults to determine the new state of the plant, ADAPT. Intelliface/ADAPT then updates its model of the ADAPT system's resources and determines whether the current plan can be executed using the reduced resources. If not, the planning subsystem generates a new plan that reschedules tasks, reconfigures ADAPT, and reassigns the use of ADAPT resources as needed to work around the fault. The resource model, planning domain model, and planning goals are expressed using NASA's Action Notation Modeling Language (ANML). Parts of the ANML model are generated automatically, and other parts are constructed by hand using the Planning Model Integrated Development Environment, a visual Eclipse-based IDE that accelerates ANML model development. Because native ANML planners are currently under development and not yet sufficiently capable, the ANML model is translated into the New Domain Definition Language (NDDL) and sent to NASA's EUROPA planning system for plan generation. The adaptive controller executes the new plan, using augmented, hierarchical finite state machines to select and sequence actions based on the state of the ADAPT system. Real-time sensor data, commands, and plans are displayed in information-dense arrays of timelines and graphs that zoom and scroll in unison. A dynamic schematic display uses color to show the real-time fault state and utilization of the system components and resources. An execution manager coordinates the activities of the other subsystems. The subsystems are integrated using the Internet Communications Engine (ICE). an object-oriented toolkit for building distributed applications.

Testbed↗

NASA NTRS2013

Adaptive Distributed Environment for Procedure Training (ADEPT)

ADEPT (Adaptive Distributed Environment for Procedure Training) is designed to provide more effective, flexible, and portable training for NASA systems controllers. When creating a training scenario, an exercise author can specify a representative rationale structure using the graphical user interface, annotating the results with instructional texts where needed. The author's structure may distinguish between essential and optional parts of the rationale, and may also include "red herrings" - hypotheses that are essential to consider, until evidence and reasoning allow them to be ruled out. The system is built from pre-existing components, including Stottler Henke's SimVentive instructional simulation authoring tool and runtime. To that, a capability was added to author and exploit explicit control decision rationale representations. ADEPT uses SimVentive's Scalable Vector Graphics (SVG)- based interactive graphic display capability as the basis of the tool for quickly noting aspects of decision rationale in graph form. The ADEPT prototype is built in Java, and will run on any computer using Windows, MacOS, or Linux. No special peripheral equipment is required. The software enables a style of student/ tutor interaction focused on the reasoning behind systems control behavior that better mimics proven Socratic human tutoring behaviors for highly cognitive skills. It supports fast, easy, and convenient authoring of such tutoring behaviors, allowing specification of detailed scenario-specific, but content-sensitive, high-quality tutor hints and feedback. The system places relatively light data-entry demands on the student to enable its rationale-centered discussions, and provides a support mechanism for fostering coherence in the student/ tutor dialog by including focusing, sequencing, and utterance tuning mechanisms intended to better fit tutor hints and feedback into the ongoing context.

Domeshek, Eric↗

NASA NTRS2000

Intelligent Tutoring Systems for Procedural Task Training of Remote Payload Operations at NASA

Intelligent Tutoring Systems (ITSs) encode and apply the subject matter and teaching expertise of experienced instructors to provide students with individualized instruction automatically. ITSs complement training simulators by providing automated instruction when it is not economical or feasible to dedicate an instructor to each student during training simulations. Despite their proven training effectiveness and favorable operating cost, however, relatively few ITSs are in use. This is largely because it is usually costly and difficult to encode the task knowledge used by the ITS to evaluate the student's actions and assess the student's performance. Procedural tasks are tasks for which there exist procedures, guidelines, and strategies that determine the correct set of steps to be taken within each situation. To lower the cost and difficulty of creating tutoring systems for procedural task training, Stottler Henke Associates, Inc. (SHAI) worked closely with the Operations Training Group at NASA's Marshall Space Flight Center to develop the Task Tutor Toolkit (T (exp 3)), a generic tutoring system shell and scenario authoring tool. The Task Tutor Toolkit employs a case-based reasoning approach where the instructor creates a procedure template that specifies the range of student actions that are "correct" within each scenario. Because each procedure template is specific to a single scenario, the system can employ relatively simple reasoning methods to represent a correct set of actions and assess student performance. This simplicity enables a non-programmer to specify task knowledge quickly and easily by via graphical user interface, using a "demonstrate, generalize, and annotate" paradigm, that recognizes the range of possible valid actions and infers principles understood (or misunderstood) by the student when those actions are carried out. The Task Tutor Toolkit was also designed to be modular and general, so that it can be interfaced with a wide range of training simulators and support a variety of training domains. SHAI and NASA applied the Task Tutor Toolkit to create the Remote Payload Operations Tutor (RPOT). RPOT is a specific tutoring system application which lets scientists who are new to space mission operations learn to monitor and control their experiments aboard the International Space Station according to NASA payload regulations, guidelines, and procedures. The RPOT simulator lets students practice these skills by monitoring the telemetry variable values of a simple, hypothetical experiment, sending commands to the experiment, coordinating with NASA personnel via voice communication loops, and submitting and retrieving information via documents and forms. At the end of each scenario, RPOT displays the principles correctly or incorrectly demonstrated by the student, along with explanations and background information. The effectiveness of RPOT and the Task Tutor Toolkit are currently under evaluation at NASA.

Ong, James↗