Version 3.1 of Symbolic Constraint Maintenance Grid (SCMG) is a software system that provides a general conceptual framework for utilizing pre-existing programming techniques to perform symbolic transformations of data. SCMG also provides a language (and an associated communication method and protocol) for representing constraints on the original nonsymbolic data. SCMG provides a facility for exchanging information between numeric and symbolic components without knowing the details of the components themselves. In essence, it integrates symbolic software tools (for diagnosis, prognosis, and planning) with non-artificial-intelligence software. SCMG executes a process of symbolic summarization and monitoring of continuous time series data that are being abstractly represented as symbolic templates of information exchange. This summarization process enables such symbolic-reasoning computing systems as artificial-intelligence planning systems to evaluate the significance and effects of channels of data more efficiently than would otherwise be possible. As a result of the increased efficiency in representation, reasoning software can monitor more channels and is thus able to perform monitoring and control functions more effectively.
This work was done by Mark James of Caltech for NASA’s Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Software category.
This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-42001.
This Brief includes a Technical Support Package (TSP).
Symbolic Constraint Maintenance Grid
(reference NPO-42001) is currently available for download from the TSP library.
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Overview
The document discusses the Symbolic Constraint Maintenance Grid (SCMG), a significant advancement in the monitoring and control capabilities of NASA's Deep Space Network (DSN). The DSN is a complex system composed of various hardware and software elements that generate data for spacecraft operations. Over time, the expertise required to maintain and understand these elements has diminished, leading to challenges in monitoring the health of subsystems.
The SCMG addresses these challenges by providing a new capability that operates in shadow mode, allowing DSN operations to gain visibility into the overall health of subsystems that previously lacked such insight. This system enables the symbolic assimilation and exchange of information across diverse subsystems, transforming raw numeric data into meaningful information through high-level interpretations.
One of the key features of the SCMG is its ability to facilitate communication between numeric and symbolic components without needing detailed knowledge of the components themselves. This integration allows for the efficient exchange of information among various elements of a complex software system, enhancing the capabilities of diagnostic, prognostic, and planning tools.
The SCMG performs symbolic summarization and monitoring of continuous time series data, abstractly representing this data as symbolic templates. This approach enables AI planning systems to evaluate the significance and impact of data channels more efficiently, allowing for the monitoring of a larger number of channels. Consequently, this leads to improved monitoring and control of robotic systems.
The document emphasizes the novelty of the SCMG, which includes new methods and a proof-of-concept tool that implements a general framework for symbolic transformation. This tool also provides the capability to communicate diagnostic findings derived from data analysis and summarization.
Overall, the SCMG represents a critical advancement in the ability to monitor and control spacecraft systems, ensuring that the vast amounts of data generated are effectively utilized. By enhancing the integration of symbolic reasoning with non-AI software, the SCMG contributes to the ongoing evolution of the DSN and its operational capabilities, ultimately supporting NASA's mission in space exploration.
