Learning Qualitative Models
In general, modeling is a complex and creative task, and building qualitative models is no exception. One way of automating this task is by means of machine learning. Observed behaviors of a modeled system are used as examples for a learning algorithm that constructs a model that is consistent with...
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| Veröffentlicht in: | The AI magazine 2003-12, Vol.24 (4), p.107-119 |
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| container_end_page | 119 |
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| container_title | The AI magazine |
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| creator | Bratko, Ivan Šuc, Dorian |
| description | In general, modeling is a complex and creative task, and building qualitative models is no exception. One way of automating this task is by means of machine learning. Observed behaviors of a modeled system are used as examples for a learning algorithm that constructs a model that is consistent with the data. In this article, we review approaches to learning qualitative models, either from numeric data or qualitative observations. We describe the quin program that looks for qualitative patterns in numeric data and outputs the results of learning as “qualitative trees.” We illustrate this using applications associated with systems control, in particular, the identification and optimization of controllers and human operator's control skill. We also review approaches that learn models in terms of qualitative differential equations. |
| doi_str_mv | 10.1609/aimag.v24i4.1734 |
| format | Article |
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| ispartof | The AI magazine, 2003-12, Vol.24 (4), p.107-119 |
| issn | 0738-4602 2371-9621 |
| language | eng |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Algorithms Artificial intelligence Automation Datasets Decision trees Design Design engineering Identification Leaves Libraries Machine learning Mathematical models Noise Reverse engineering Simulation Variables |
| title | Learning Qualitative Models |
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