Name
Abstract | ||
|---|---|---|
A central purpose of knowledge acquisition technology is to assist with the formulation of domain models that underlie knowledge systems. In this article we examine the model formulation process itself as a problem-solving task. Drawing from AI research in qualitative reasoning about physical systems, we characterize the model formulation task in terms of the inputs, the reasoning subtasks, and the knowledge needed to perform the problem solving. We describe the elements of a high-level representation of modeling knowledge, and techniques for providing intelligent assistance to the model builder. Applying the results from engineering modeling to knowledge acquisition in general, we identify properties of the representation that facilitate the construction of knowledge systems from libraries of reusable models. |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1002/int.4550080108 | Int. J. Intell. Syst. |
Keywords | Field | DocType |
model formulation,problem-solving task,computer-assisted engineering modeling,qualitative reasoning,domain model,knowledge systems | Procedural knowledge,Knowledge representation and reasoning,Domain knowledge,Software engineering,Computer science,Knowledge-based systems,Model-based reasoning,Artificial intelligence,Knowledge base,Knowledge acquisition,Machine learning,Qualitative reasoning | Journal |
Volume | Issue | ISSN |
8 | 1 | 0884-8173 |
ISBN | Citations | PageRank |
0-471-59368-0 | 12 | 1.95 |
References | Authors | |
27 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas R. Gruber | 1 | 2235 | 272.54 |