Maintaining preference networks that adapt to changing preferences
| Autor: | Scott Buffett, Ki Hyang Lee, Michael Fleming |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
preference elicitation
user modeling Computer science Efficient algorithm business.industry User modeling Small number automated decision making Machine learning computer.software_genre algorithms artificial intelligence Transitive reduction Graph Preference decision making Term (time) transitive reductions preference graph Preference elicitation Artificial intelligence business dense graphs computer amount of information |
| Zdroj: | Advances in Artificial Intelligence ISBN: 9783642384561 Canadian Conference on AI |
| Popis: | Decision making can be more difficult with an enormous amount of information, not only for humans but also for automated decision making processes. Although most user preference elicitation models have been developed based on the assumption that user preferences are stable, user preferences may change in the long term and may evolve with experience, resulting in dynamic preferences. Therefore, in this paper, we describe a model called the dynamic preference network (DPN) that is maintained using an approach that does not require the entire preference graph to be rebuilt when a previously-learned preference is changed, with efficient algorithms to add new preferences and to delete existing preferences. DPNs are shown to outperform existing algorithms for insertion, especially for large numbers of attributes and for dense graphs. They do have some shortcomings in the case of deletion, but only when there is a small number of attributes or when the graph is particularly dense. © 2013 Springer-Verlag. 26th Canadian Conference on Artificial Intelligence (Canadian AI 2013), May 28-31, 2013, Regina, Saskatchewan, Canada Series: Lecture Notes in Computer Science; no. 7884 |
| Databáze: | OpenAIRE |
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