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| Content Provider | ACM Digital Library |
|---|---|
| Author | Huang, Y.-M. Rozenblit, J. W. Hu, J. |
| Abstract | The methods for transforming real-world problem into simulation models are being increasingly explored with the availability of inexpensive computing power. In general, traditional model building procedures involve a lengthy problem formulation and interaction between the analyst and the client. Furthermore, after the model definition is arrived at, the simulation model is often programmed manually. Recent developments in simulation modeling have focused on automatic model generation employing artificial intelligence (AI) techniques. Such developments focus on the transfer of the user's knowledge about the system into an executable simulation model. Current techniques still lack effective knowledge acquisition tools and a global database from which model alternatives can be generated. In this paper, a set of knowledge bases (KBs) will be proposed to aid in the hierarchical model construction. |
| Starting Page | 584 |
| Ending Page | 592 |
| Page Count | 9 |
| File Format | |
| ISBN | 0911801588 |
| DOI | 10.1145/76738.76814 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 1989-10-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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