This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies.
Numerous automated and semi-automated planning & scheduling systems have been developed for space applications. Most of these systems are model-based in that they encode domain knowledge necessary to predict spacecraft state and resources based on initial conditions and a proposed activity plan. The spacecraft state and resources as often modeled as a series of timelines, with a timeline or set of timelines to represent a state or resource key in the operations of the spacecraft. In this paper, we first describe a basic timeline representation that can represent a set of state, resource, timing, and transition constraints. We describe a number of planning and scheduling systems designed for space applications (and in many cases deployed for use of ongoing missions) and describe how they do and do not map onto this timeline model.
We have developed an architecture called MUSE (Multi-User Scheduling Environment) to enable the integration of multi-objective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to reuse existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to actively engage in the optimization process, each representing one or more objectives in the optimization problem. As initial applications, we apply our approach to scheduling the James Webb Space Telescope, where three objectives aremodeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angularmomentumthat would necessitate the use of mission critical propellant to dump the momentum. As a second application area, we model aspects of the Cassini science planning process, including the trade-off between collecting data (subject to onboard recorder capacity) and transmitting saved data to Earth. A third mission application is that of scheduling the Cluster 4-spacecraft constellation plasma experiment. In this paper we describe our overall architecture and our adaptations for these different application domains. We also describe our plans for applying this approach to other science mission planning and scheduling problems in the future.
HORNE is a programming system that offers a set of tools for building automated reasoning systems. It offers three major modes of inference:--a horn clause theorem prover (backwards chaining mechanism): --a forward chaining mechanism; and --a mechanism for restricting the range of variables with arbitra* . . predicates.All three modes use a common representation of facts, namely horn clauses with universally quantified variables, and use the unification algorithm. Also. they all share the following additional specialized reasoning capabilities: 1) variables may be typed with a fairly general type theory that allows -intersecting types; 2) full reasoning about equality between ground terms, and limited equality reasoning for quantified terms; and 3) escapes into LISP for use as necessary. This paper contains an introduction to each of these facilities, and the HORNE User's Manual. -HORNE is a programming system that offers a set of tools for building automated reasoning systems. It offers three major modes of inference:(1' a horn clause theorem prover (backwards chaining mechanism); a forward chaining mechanism; and a mechanism for restricting the range of variables with arbitrary predicates. All three modes use a conmon representation of facts, namely horn clauses-DD IJAN72 1473 EDITION OFINOV 65 IS OBSOLETE Unclassified -. SECURITY CLASSIFICATION OF THIS PAGE (1407en Date Entered). " %, %%SEC.URITY CLASSIFICATION OF T!*rIS PAGE(Wmef Dote Ent.,.d) Abstract (cont.),,u1th universally quanitified variables, and use the unification algorithm.
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