A rule-based decision support tool for architecting Earth observing missions

Abstract

A decision support tool is presented that is especially tailored for architecting Earth observing missions and programs. The tool features both a cost model and a performance model. This paper focuses on the description of the performance model. Indeed, while considerable effort has been put into the development of cost estimating models, comparably much less effort has been put into the development of quantitative methods to assess how well Earth Observing Mission satisfy scientific and societal needs. A literature review revealed that existing methods include a commercial approach, a value-of-information approach, end-to-end simulation, assimilation in Observing System Simulation Experiments, and simple expert judgment. Limitations of these methods include limited applicability, computational complexity, low modeling fidelity (e.g. abstraction of synergies between measurements), and subjectivity. Our method uses a knowledge-based system to store and manage large quantities of expert knowledge in the form of rules-of-thumb that replace expensive computations.Scientific and societal measurement requirements and instrument capabilities are expressed in the form of logical rules and data structures. An efficient pattern matching algorithm performs the comparison of the measurement requirements and the measurement capabilities on the basis of 64 different measurement attributes. The system is demonstrated on the Earth Science Decadal Survey. While the system is still under development, it shows great potential to enhance traceability in the modeling of scientific and societal value of Earth observing missions. Furthermore, the recursive nature of rule-based systems shows potential to model synergies between instruments and measurements, at a sufficient level of fidelity for architectural trade studies, especially for the ones conducted in committees with experts such as Decadal Surveys.

Towards knowledge modeling and manipulation technologies: A survey

“…In addition, capturing an expert's knowledge entirely is difficult as most of their knowledge is hidden in their skills (Selva & Crawley, 2012).…”

“…In this system, if the antecedent is true, then the consequent is also true (Negnevitsky, 2005). Mostly, in a LRS, knowledge is acquired manually from domain experts through interviews, where experts communicate their knowledge using questionnaires (Connaghan et al, 2013;Dimitroula et al, 2001;Selva & Crawley, 2012). However, knowledge in forms of rules can be acquired automatically, such as RUBRIC which constructs rules from thesauri (Minkoo et al, 2000) and semi-automated like KnowRob, which automatically acquires information from different knowledge sources with the aid of human for correcting mistakes and aligning imported knowledge sources (Tenorth & Beetz, 2013).…”

“…Logical rule-based systems have been applied in a number of areas such as, hole maching (Park, 2003), flouorescein angiography (Dimitroula et al, 2001), bridge crane control (Capella et al, 2003), estimating infant immunization coverage (Kowalski & Burton, 2012), Lungs abnormality detection (Lahouar et al, 2012), text extraction system (Ramakrishnan et al, 2012), decision support (Selva & Crawley, 2012) and power grid dispatch and control (H. Zhang & Zou, 2012). Prolog is the programming language used mostly for knowledge representation in logical rule-based systems, as seen in WUENIC (Kowalski & Burton, 2012), sports coaching (Connaghan et al, 2013), KnowRob (Tenorth & Beetz, 2013) and online poker agent (Teofilo et al, 2014).…”