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NASA NTRS2016

Achieving Fast Operational Intelligence in NASA's Deep Space Network Through Complex Event Processing

NASA’s Deep Space Network (DSN) is a complex, global project, in which the expertise of human operators remain crucial for its successful operation. To find ways to save costs in operations and to improve its services, a number of modernization efforts are underway in the DSN. One such effort is a research and technology development task at the Jet Propulsion Laboratory that is investigating the use of complex event processing (CEP) for intelligent assessment of situations, trend analysis, and advanced automation. The technology leverages the significant business intelligence (BI) and data science advancements made in the enterprise industries over the last several years. The open source big data processing engine Apache SparkTM and the high-throughput, distributed messaging system Apache Kafka form the core of the DSN Complex Event Processing (DCEP) framework. This paper discusses the system engineering perspective of why achieving efficient, lower-cost operations in the DSN is a challenging problem, how the DCEP system handles the use cases that help realize intelligent operations, and how this solution fits into the overall model of the planned DSN Follow-the- Sun Operations (FtSO).

Choi, Joshua S.↗

NASA NTRS2012

Revamping Spacecraft Operational Intelligence with Splunk

So what is Splunk? Instead of giving the technical details, which you can find online, I'll tell you what it did for me. Splunk slapped everything into one place, with one uniform format, and gave me the ability to forget about all these annoying details of where it is, how to parse it, and all that. Instead, I only need to interact with Splunk to find the data I need. This sounds simple and obvious, but it's surprising what you can do once you all of your data is indexed in one place. By having your data organized, querying becomes much easier. Let's say that I want to search telemetry for a sensor_name gtemp_1 h and to return all data that is at most five minutes old. And because Splunk can hook into a real ]time stream, this data will always be up-to-date. Extending the previous example, I can now aggregate all types of data into one view based in time. In this picture, I've got transaction logs, telemetry, and downlinked files all in one page, organized by time. Even though the raw data looks completely than this, I've defined interfaces that transform it into this uniform format. This gives me a more complete picture for the question what was the spacecraft doing at this particular time? And because querying data is simple, I can start with a big block of data and whiddle it down to what I need, rather than hunting around for the individual pieces of data that I need. When we have all the data we need, we can begin widdling down the data with Splunk's Unix-like search syntax. These three examples highlights my trial-and-error attempts to find large temperature changes. I begin by showing the first 5 temperatures, only to find that they're sorted chronologically, rather than from highest temperatures to lowest temperatures. The next line shows sorting temperatures by their values, but I find that that fs not really what I want either. I want to know the delta temperatures between readings. Looking through Splunk's user manual, I find the delta function, which lets me dynamically generate new information to use in my query. With that extra piece of information, I can now return only the telemetry readings where the temperature changed by at least 10. One other useful feature I'll mention is that all of these queries can be run through Splunk's API. So any scripting language you can think of can plug right in and make these queries. This gives us the ability to build a lot of new tools.

operational intelligence↗

NASA NTRS2012

Revamping Spacecraft Operational Intelligence

The EPOXI flight mission has been testing a new commercial system, Splunk, which employs data mining techniques to organize and present spacecraft telemetry data in a high-level manner. By abstracting away data-source specific details, Splunk unifies arbitrary data formats into one uniform system. This not only reduces the time and effort for retrieving relevant data, but it also increases operational visibility by allowing a spacecraft team to correlate data across many different sources. Splunk's scalable architecture coupled with its graphing modules also provide a solid toolset for generating data visualizations and building real-time applications such as browser-based telemetry displays.

Hwang, Victor↗

NASA NTRS1988

Intent inferencing by an intelligent operator's associate - A validation study

In the supervisory control of a complex, dynamic system, one potential form of aiding for the human operator is a computer-based operator's associate. The design philosophy of the operator's associate is that of 'amplifying' rather than automating human skills. In particular, the associate possesses understanding and control properties. Understanding allows it to infer operator intentions and thus form the basis for context-dependent advice and reminders; control properties allow the human operator to dynamically delegate individual tasks or subfunctions to the associate. This paper focuses on the design, implementation, and validation of the intent inferencing function. Two validation studies are described which empirically demonstrate the viability of the proposed approach to intent inferencing.

Jones, Patricia M.↗

NASA NTRS2005

Decision Facilitator for Launch Operations using Intelligent Agents

Launch operations require millions of micro-decisions which contribute to the macro decision of 'Go/No-Go' for a launch. Knowledge workers"(such as managers and technical professionals) need information in a timely precise manner as it can greatly affect mission success. The intelligent agent (web search agent) uses the words of a hypertext markup language document which is connected through the internet. The intelligent agent's actions are to determine if its goal of seeking a website containing a specified target (e.g., keyword or phrase), has been met. There are few parameters that should be defined for the keyword search like "Go" and "No-Go". Instead of visiting launch and range decision making servers individually, the decision facilitator constantly connects to all servers, accumulating decisions so the final decision can be decided in a timely manner. The facilitator agent uses the singleton design pattern, which ensures that only a single instance of the facilitator agent exists at one time. Negotiations could proceed between many agents resulting in a final decision. This paper describes details of intelligent agents and their interaction to derive an unified decision support system.

Thirumalainambi, Rajkumar↗

NASA NTRS1989

Reflections on the relationship between artificial intelligence and operations research

Historically, part of Artificial Intelligence's (AI's) roots lie in Operations Research (OR). How AI has extended the problem solving paradigm developed in OR is explored. In particular, by examining how scheduling problems are solved using OR and AI, it is demonstrated that AI extends OR's model of problem solving through the opportunistic use of knowledge, problem reformulation and learning.

Fox, Mark S.↗

NASA NTRS1987

Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS): A conceptual framework

In recent years there has been increasing interest in applying the computer based problem solving techniques of Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS) to analyze extremely complex problems. A conceptual framework is developed for successfully integrating these three techniques. First, the fields of AI, OR, and DSS are defined and the relationships among the three fields are explored. Next, a comprehensive adaptive design methodology for AI and OR modeling within the context of a DSS is described. These observations are made: (1) the solution of extremely complex knowledge problems with ill-defined, changing requirements can benefit greatly from the use of the adaptive design process, (2) the field of DSS provides the focus on the decision making process essential for tailoring solutions to these complex problems, (3) the characteristics of AI, OR, and DSS tools appears to be converging rapidly, and (4) there is a growing need for an interdisciplinary AI/OR/DSS education.

Parnell, Gregory S.↗

NASA NTRS1993

Advancing satellite operations with intelligent graphical monitoring systems

For nearly twenty-five years, spacecraft missions have been operated in essentially the same manner: human operators monitor displays filled with alphanumeric text watching for limit violations or other indicators that signal a problem. The task is performed predominately by humans. Only in recent years have graphical user interfaces and expert systems been accepted within the control center environment to help reduce operator workloads. Unfortunately, the development of these systems is often time consuming and costly. At the NASA Goddard Space Flight Center (GSFC), a new domain specific expert system development tool called the Generic Spacecraft Analyst Assistant (GenSAA) has been developed. Through the use of a highly graphical user interface and point-and-click operation, GenSAA facilitates the rapid, 'programming-free' construction of intelligent graphical monitoring systems to serve as real-time, fault-isolation assistants for spacecraft analysts. Although specifically developed to support real-time satellite monitoring, GenSAA can support the development of intelligent graphical monitoring systems in a variety of space and commercial applications.

Hughes, Peter M.↗

NASA NTRS1994

An intelligent ground operator support system

This paper presents first results of the project 'Technologien fuer die intelligente Kontrolle von Raumfahrzeugen' (TIKON). The TIKON objective was the demonstration of feasibility and profit of the application of artificial intelligence in the space business. For that purpose a prototype system has been developed and implemented for the operation support of the Roentgen Satellite (ROSAT), a scientific spacecraft designed to perform the first all-sky survey with a high-resolution X-ray telescope and to investigate the emission of specific celestial sources. The prototype integrates a scheduler and a diagnosis tool both based on artificial intelligence techniques. The user interface is menu driven and provides synoptic displays for the visualization of the system status. The prototype has been used and tested in parallel to an already existing operational system.

Goerlach, Thomas↗

NASA NTRS1993

PILOT: An intelligent distributed operations support system

The Real-Time Data System (RTDS) project is exploring the application of advanced technologies to the real-time flight operations environment of the Mission Control Centers at NASA's Johnson Space Center. The system, based on a network of engineering workstations, provides services such as delivery of real time telemetry data to flight control applications. To automate the operation of this complex distributed environment, a facility called PILOT (Process Integrity Level and Operation Tracker) is being developed. PILOT comprises a set of distributed agents cooperating with a rule-based expert system; together they monitor process operation and data flows throughout the RTDS network. The goal of PILOT is to provide unattended management and automated operation under user control.

Rasmussen, Arthur N.↗

NASA NTRS2003

A Comparison of Techniques for Scheduling Fleets of Earth-Observing Satellites

Earth observing satellite (EOS) scheduling is a complex real-world domain representative of a broad class of over-subscription scheduling problems. Over-subscription problems are those where requests for a facility exceed its capacity. These problems arise in a wide variety of NASA and terrestrial domains and are .XI important class of scheduling problems because such facilities often represent large capital investments. We have run experiments comparing multiple variants of the genetic algorithm, hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on two variants of a realistically-sized model of the EOS scheduling problem. These are implemented as permutation-based methods; methods that search in the space of priority orderings of observation requests and evaluate each permutation by using it to drive a greedy scheduler. Simulated annealing performs best and random mutation operators outperform our squeaky (more intelligent) operator. Furthermore, taking smaller steps towards the end of the search improves performance.

Globus, Al↗

NASA NTRS1989

Intent inferencing with a model-based operator's associate

A portion of the Operator Function Model Expert System (OFMspert) research project is described. OFMspert is an architecture for an intelligent operator's associate or assistant that can aid the human operator of a complex, dynamic system. Intelligent aiding requires both understanding and control. The understanding (i.e., intent inferencing) ability of the operator's associate is discussed. Understanding or intent inferencing requires a model of the human operator; the usefulness of an intelligent aid depends directly on the fidelity and completeness of its underlying model. The model chosen for this research is the operator function model (OFM). The OFM represents operator functions, subfunctions, tasks, and actions as a heterarchic-hierarchic network of finite state automata, where the arcs in the network are system triggering events. The OFM provides the structure for intent inferencing in that operator functions and subfunctions correspond to likely operator goals and plans. A blackboard system similar to that of Human Associative Processor (HASP) is proposed as the implementation of intent inferencing function. This system postulates operator intentions based on current system state and attempts to interpret observed operator actions in light of these hypothesized intentions.

Jones, Patricia M.↗

NASA NTRS1989

Human-computer interaction in distributed supervisory control tasks

An overview of activities concerned with the development and applications of the Operator Function Model (OFM) is presented. The OFM is a mathematical tool to represent operator interaction with predominantly automated space ground control systems. The design and assessment of an intelligent operator aid (OFMspert and Ally) is particularly discussed. The application of OFM to represent the task knowledge in the design of intelligent tutoring systems, designated OFMTutor and ITSSO (Intelligent Tutoring System for Satellite Operators), is also described. Viewgraphs from symposia presentations are compiled along with papers addressing the intent inferencing capabilities of OFMspert, the OFMTutor system, and an overview of intelligent tutoring systems and the implications for complex dynamic systems.

Mitchell, Christine M.↗

NASA NTRS1989

OFMTutor: An operator function model intelligent tutoring system

The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described.

Jones, Patricia M.↗

NASA NTRS1991

Model-based displays for satellite ground control

With the emergence of new technology for both human-computer interaction and knowledge-based systems, a range of opportunities exists to enhance the effectiveness and efficiency of satellite ground controllers. The use of models of operator function to represent operator activity in the context of changing system events and operator functions is illustrated. Although there are many models, the operator function model (OFM) was used in this research. In addition to representing operator activities, the OFM can be used to design 'intelligent' operator displays and, in real time, control the displayed information so that the operator has the appropriate information, at the appropriate time, and at the appropriate level of abstraction. The OFM was demonstrated in the context of a NASA Goddard Space Flight Center satellite ground control system. To evaluate the effectiveness of the model-based workstation, an experiment was conducted to compare system performance with a conventional operator workstation versus the model based workstation.

Mitchell, Christine M.↗

NASA NTRS1990

A development framework for artificial intelligence based distributed operations support systems

Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself.

Adler, Richard M.↗

NASA NTRS2023

Ch. 12. A Theoretical Approach to Management of Limited Attentional Resources to Support the m:N Operation in Advanced Air Mobility Ecosystem

Advanced air mobility (AAM) technologies incorporate increasingly autonomous systems that allow fully remote, independent, and intelligent operation of air vehicles to support the transportation of goods and passengers within and across urban and rural areas. With a myriad of automated technologies enabling the AAM ecosystem, the human operator’s role will likely be a passive supervisory monitor of the air vehicles, involving increasingly fewer humans (m) that manage many more autonomous systems (N), or m:N operations. Unfortunately, the human performance literature suggests that human operators will exhibit poor supervision of numerous autonomous agents due to the limits of attentional resources in the operators. In the general human information-processing model, a human operator exercises a limited pool of attentional resources to engage various information-processing stages including detecting, perceiving, comprehending, and predicting objects around them. Yamani and Horrey (2018) expanded the human information-processing model to characterize a tradeoff between information-processing demand and resource relief that automation brings in the context of automated driving. In their model, a driver interacting with an automated driving system is assumed to reallocate resources “freed” by automation to support other information-processing stages required for successful task performance. A future AAM ecosystem enabled by an orchestration of advanced automated systems, however, requires a single operator to interact with more than one air vehicle with varying levels and degrees of automated systems, making the traditional framework of human-automation interaction insufficient. To address this gap, we provide a review of the literature on situation assessment and trust, two constructs identified as critical for a fuller understanding of intimate and intricate interactions between a human operator and multiple air vehicles equipped with increasingly autonomous systems. Then, we propose an expansion of Yamani and Horrey’s (2018) model to motivate systematic research on the human operator’s role, identify factors that influence resource allocation and guide human-centered design of an interface supporting the m:N operation in the AAM environment.

Advanced Air Mobility↗

NASA NTRS1989

View graphs for GSFC Colloquium on OFMspert

Viewgraphs providing an overview of activities concerned with the development and testing of the Operator Function Model (OFM) expert system (OFMspert) are presented. The OFM is a mathematical tool for representing operator interaction with predominantly automated space ground control systems. OFM provides cognitive task analysis and served as the basis for the design of an intelligent operator's associate called OMFspert. An experimental implementation of OFMspert, referred to as Ally, was developed. An empirical evaluation of Ally was conducted to determine the effectiveness of a supervisory control team consisting of a human operator and Ally versus a control team consisting of two human operators. The experiment was carried out in the GT-MSOCC (Georgia Tech MultiSatellite Operations Control Center) domain, a research tool consisting of a high fidelity implementation of the operator interface to a GSFC ground control system. The viewgraphs outline the experimental design, operator performance measures, and preliminary results.

Mitchell, Christine M.↗