Keller, R. M.; Rimon, Michal and Das, Aseem, \A Knowledge-based Prototyping Environment for Construction of Scienti c Modeling Software", Automted Software Engineering, pp 79-128  Home/Search   Document Details and Download   Summary   Related Articles   Check

....as (a) black box, or (b) an integral part of the software system in which all components, e.g. sub models, must be viewed as acting together towards achieving a common goal. emerged to formalize some of the human decision processes and information management associated with model development [3,5,6,19,33,38]. Model information management can be approached at di#erent levels (Fig. 1) In the model information management strategy that is most commonly seen within the GIS community (Fig. 1a) all decision rules are held separate from the process models and decisions are made centrally [10,21,36] This ....

R.M. Keller, M. Rimin, A. Das, A knowledge-based prototyping environment for construction of scienti#c modeling software, Autom. Software Eng. 1 (1994) 79--128.

....equations. Examples include an aircraft simulator for computational design of jet engine nozzles; Gelsey et al. 1996 ] a sailing yacht simulator for computational design of hull geometries [ Ellman et al. 1993 ] and a planetary scientist s model of the atmosphere of the moon Titan [ Keller et al. 1994 ] among others. Simulators for such systems can be constructed as configurations of numerical integration and root extraction routines. Libraries of such generic numerical tools are now widely available [ Press et al. 1986 ] They are under utilized because of the difficulty of bridging the ....

....[ Smith, 1993 ] or constructive type theory [ Constable, 1986 ] In any case, a more complete diagnosis of the difficulty awaits further investigation. 4 Related Work Our research was originally motivated by difficulties encountered in attempting to apply the SIGMA scientific modeling system [ Keller et al. 1994 ] to the task of constructing numerical simulators for automated op Inputs: A point in time: t. Output: The state (x 1 (t) xn (t) of the system at time t. Specification: b 1 (x 1 (t i ) xn (t i ) 0 : b k (x 1 (t i ) xn (t i ) 0 b k 1 (x 1 (t f ) ....

R. Keller, M. Rimon, and A. Das. A knowledge-based prototyping environment for construction of scientific modeling software. Automated Software Engineering, 1:79--128, 1994.

....His order of magnitude reasoning method is restricted to generating the behavior at a fixed point in time, but we can predict the behavior changing with time as well as the behavior at a fixed point in time. The SIGMA system developed at NASA Ames Research Center [Keller and Rimon, 1992; Keller et al. 1994] is a tool which aids a scientist user in building a model. After the interaction with the user, it produces a model specified in data flow graph and executes the model to compute a unknown quantity. Like Oracle, SIGMA organizes and represents domain knowledge in frame structures. 70 However, ....

Richard M. Keller, Michal Rimon, and Assem Das. A Knowledge-Based Prototyping Environment for Construction of Scientific Modeling Software. Journal of Automated Software Engineering, 1(1), 1994. to appear.

....differential equations. Examples include an aircraft simulator for computational design of jet engine nozzles [Gelsey et al. 1996] a sailing yacht simulator for computational design of hull geometries [Ellman et al. 1993] and a planetary scientist s model of the atmosphere of the moon Titan [Keller et al. 1994]. Simulators for such systems can be constructed as configurations of numerical integration and root extraction routines. Libraries of such generic numerical tools are now widely available [Press et al. 1986] They are under utilized because of the difficulty of bridging the gap between equations ....

....with various different numerical algorithms and tolerance levels, in order to have a program with suitable levels of efficiency, accuracy and stability. 9 Related Work Our research was originally motivated by difficulties encountered in attempting to apply the SIGMA scientific modeling system [Keller et al. 1994] to the task of constructing numerical simulators for automated optimization of designs of jet engine nozzles and sailing yachts. SIGMA uses a graphical first order data flow language to represent scientific models. Unfortunately SIGMA is not capable of representing models based on simultaneous ....

R. Keller, M. Rimon, and A. Das. A knowledge-based prototyping environment for construction of scientific modeling software. Automated Software Engineering, 1(1):79--128, 1994.

....number of autonomous yet weakly interacting modules. Figure 2. The user surface of the prototype SAFE DIS information system. The architecture of the prototype SAFE DIS IS has been influenced by design considerations in NASA s Scientists Intelligent Graphic Modelling System (SIGMA) developed by Keller, Rimon and Das (1994) and in expert systems developed at the University of Surrey for the UK National Water Authority (see Ahmad and Griffin 1994, Ahmad and Griffin 1993) SIGMA was designed as a rapid prototyping tool for building scientific models and developed as a specialised tool for the scientific modelling ....

Keller, Richard M., Rimon, Michal & Das, Aseem (1994) 'A Knowledge-Based Prototyping Environment for Construction of Scientific Modelling Software'.

....and Guha have identified a variety of other uses of contexts. Collecting together a set of related axioms into a context can be used to focus reasoning. Consider SIGMA, a knowledge base of scientific domain knowledge that supports building consistent, coherent, and executable domain models (Keller, Rimon, Das 1994). SIGMA contains axioms describing two different application domains: modeling the atmosphere of Titan (a moon of Saturn) and modeling a forest ecosystem. By separating the axioms of these two domains into different contexts, reasoning can be easily focused on just the axioms of the domain of ....

Keller, R. M.; Rimon, M.; and Das, A. 1994. A knowledge based prototyping environment for construction of scientific modeling software. Automated Software Engineering 1(1).

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Keller, R. M.; Rimon, Michal and Das, Aseem, \A Knowledge-based Prototyping Environment for Construction of Scienti c Modeling Software", Automted Software Engineering, pp 79-128

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