Name
Abstract | ||
|---|---|---|
Various tasks in decision making and decision support require selecting a preferred subset of items from a given set of items. Recent work in this area considered methods for specifying such preferences based on the attribute values of individual elements within the set. Of these, the approach of (Brafman et al. 2006) appears to be the most general. In this paper, we consider the problem of computing an optimal subset given such a specification. The problem is shown to be NP-hard in the general case, necessitating heuristic search methods. We consider two algorithm classes for this problem: direct set construction, and implicit enumeration as solutions to appro- priate CSPs. New algorithms are presented in each class and compared empirically against previous results. |
Year | Venue | Keywords |
|---|---|---|
2007 | AAAI | decision support,direct set construction,attribute value,optimal subset,feasible item,computing optimal subsets,preferred subset,heuristic search method,appropriate csps,general case,algorithm class,heuristic search |
Field | DocType | Citations |
Heuristic,Mathematical optimization,Computer science,Decision support system,Enumeration,Artificial intelligence,Machine learning | Conference | 8 |
PageRank | References | Authors |
0.65 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maxim Binshtok | 1 | 92 | 4.11 |
| Ronen I. Brafman | 2 | 3260 | 220.63 |
| Solomon Eyal Shimony | 3 | 687 | 78.43 |
| Ajay Mani | 4 | 19 | 1.34 |
| Craig Boutilier | 5 | 6864 | 621.05 |