NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Transformation reborn: A new generation expert system for planning HST operationsThe Transformation expert system (TRANS) converts proposals for astronomical observations with the Hubble Space Telescope (HST) into detailed observing plans. It encodes expert knowledge to solve problems faced in planning and commanding HST observations to enable their processing by the Science Operations Ground System (SOGS). Among these problems are determining an acceptable order of executing observations, grouping of observations to enhance efficiency and schedulability, inserting extra observations when necessary, and providing parameters for commanding HST instruments. TRANS is currently an operational system and plays a critical role in the HST ground system. It was originally designed using forward-chaining provided by the OPS5 expert system language, but has been reimplemented using a procedural knowledge base. This reimplementation was forced by the explosion in the amount of OPS5 code required to specify the increasingly complicated situations requiring expert-level intervention by the TRANS knowledge base. This problem was compounded by the difficulty of avoiding unintended interaction between rules. To support the TRANS knowledge base, XCL, a small but powerful extension to Commom Lisp was implemented. XCL allows a compact syntax for specifying assignments and references to object attributes. XCL also allows the capability to iterate over objects and perform keyed lookup. The reimplementation of TRANS has greatly diminished the effort needed to maintain and enhance it. As a result of this, its functions have been expanded to include warnings about observations that are difficult or impossible to schedule or command, providing data to aid SPIKE, an intelligent planning system used for HST long-term scheduling, and providing information to the Guide Star Selection System (GSSS) to aid in determination of the long range availability of guide stars.
Document ID
19910013460
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Gerb, Andrew
(Space Telescope Science Inst. Baltimore, MD, United States)
Date Acquired
September 6, 2013
Publication Date
May 1, 1991
Publication Information
Publication: NASA. Goddard Space Flight Center, The 1991 Goddard Conference on Space Applications of Artificial Intelligence
Subject Category
Cybernetics
Accession Number
91N22773
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
No Preview Available