Name
Abstract | ||
|---|---|---|
Current approaches to recommending mathematical software are qualitative and categorical. These approaches are unsatisfactory when the problem to be solved has features that can “trade-off” in the recommendation process. A quantitative system is proposed that permits tradeoffs and can be built and modified incrementally. This quantitative approach extends other knowledge-engineering techniques in its knowledge representation and aggregation facilities. The system is demonstrated on the domain of ordinary differential equation initial value problems. The results are significantly superior to an existing qualitative (decision tree) system. |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1145/128745.128747 | ACM Trans. Math. Softw. |
Keywords | Field | DocType |
mathematical software,quantitative approach,automated selection,quantitative system,current approach,existing qualitative,initial value problem,ordinary differential equations,quantitative knowledge representation,software selection,knowledge representation,decision tree,aggregation facility,knowledge-engineering technique,knowledge engineering,ordinary differential equation | Decision tree,Data mining,Knowledge representation and reasoning,Ordinary differential equation,Categorical variable,Expert system,Theoretical computer science,Mathematical software,Initial value problem,Artificial intelligence,Knowledge base,Mathematics | Journal |
Volume | Issue | ISSN |
18 | 1 | 0098-3500 |
Citations | PageRank | References |
15 | 1.14 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Lucks | 1 | 18 | 1.63 |
| Ian Gladwell | 2 | 66 | 12.63 |