Name
Abstract | ||
|---|---|---|
Localization is a general-purpose representational technique for partitioning a problem into subproblems. A localized problem-solver searches several smaller search spaces, one for each subproblem Unlike most methods of partitioning, however, localization allows for subproblems that overlap - 1 e multiple search spaces may be involved in constructing shared pieces of the overall plan. In this paper we focus on two criteria for forming localizations scope and abstraction. We describe a method for automatically generating such localizations and provide empirical results that contrast their use in an office-building construction domain. |
Year | Venue | Keywords |
|---|---|---|
1995 | IJCAI | localized planning,empirical result,office-building construction domain,localizations scope,localized problem-solver,smaller search space,general-purpose representational technique,overall plan,multiple search space,knowledge based systems,artificial intelligence,search space |
Field | DocType | ISBN |
Abstraction,Computer science,Knowledge-based systems,Artificial intelligence,Machine learning | Conference | 1-55860-363-8 |
Citations | PageRank | References |
21 | 1.72 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| amy l lansky | 1 | 494 | 184.77 |
| Lise Getoor | 2 | 4365 | 320.21 |
| henry lum | 3 | 21 | 1.72 |