978-1-4577-0557-1/12/$26.00 ©2012 IEEE 1 A Rule-Based Decision Support Tool for Architecting Earth Observing Missions Daniel Selva Massachusetts Institute of Technology 77 Massachusetts Ave, Room 33-409 Cambridge, MA 02139 617-682-6521 dselva@mit.edu Edward F. Crawley Massachusetts Institute of Technology 77 Massachusetts Ave, Room 33-413 Cambridge, MA 02139 617-253-7510 crawley@mit.edu 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. TABLE OF CONTENTS 1. INTRODUCTION ................................................. 1 2. APPROACH ........................................................ 5 3. APPLICATION .................................................... 8 4. CONCLUSION................................................... 13 REFERENCES....................................................... 16 BIOGRAPHIES...................................................... 18 APPENDIX............................................................ 19 1. INTRODUCTION This paper is concerned with the assessment of scientific and societal merit of Earth Observing Missions or more generally Earth Observing Satellite Systems (EOSS), in the context of early architectural trade studies. The methodology presented in this paper is particularly relevant for semi-automatic assessment using man-in-the-loop decision support tools. This first section of the paper starts by providing the necessary background on system architecture, decision support tools, EOSS, and the particular problem of scientific benefit assessment of EOSS. A review of prior methods for scientific merit assessment is provided. A gap in the literature, and subsequent research goals, are identified. Knowledge-based systems (KBS) and in particular rule- based expert systems (RBES) are then introduced as an alternative approach. The necessary background on RBES is provided, including a short history and a critique of RBES. The section concludes with a review of the structure of the rest of the paper. System Architecting and Decision Support Tools System Architecting— In the late 80’s, researchers started to realize that some concepts from traditional architecture and civil engineering were being used by engineers in charge of designing and building unprecedented, large, complex systems [1]. These concepts included the creation of a separate position for a lead systems engineer at the interface between the client and the design team, a more direct engagement of the client in the high-level design of the system, and a holistic, value-centered, lifecycle view of the system. Rechtin was arguably the first to formalize this concept, and he coined the term “systems architecting” [2]. His book with Maier is, perhaps, still the best introduction to the field [1]. Crawley defines system architecture as “t he embodiment of concept, and the allocation of physical/informational function (process) to elements of form (objects) and definition of structural interfaces among the objects” [3]. Essentially, the architecture of a system is its highest level design. However, it takes a holistic view that goes beyond traditional design. More precisely: 1) it takes into account technical and non-technical factors; 2) it is centered in delivering value to stakeholders as opposed to optimizing performance or cost; 3) it takes into account all the phases of the lifecycle including manufacturing, testing, operations and disposal.