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2. A bayesian metareasoner for algorithm.
| Content Provider | CiteSeerX |
|---|---|
| Author | Guo, Haipeng |
| Abstract | Bayesian network (BN) inference has long been seen as a very important and hard problem in AI. Both exact and approximate BN inference are NP-hard [Co90, Sh94]. To date researchers have developed many different kinds of exact and approximate BN inference algorithms. Each of these has different properties and works better for different classes of inference problems. Given a BN inference problem instance, it is usually hard but important to decide in advance which algorithm among a set of choices is the most appropriate. This problem is known as the algorithm selection problem [Ri76]. The goal of this research is to design and implement a meta-level reasoning system that acts as a “BN inference expert ” and is able to quickly select the most appropriate algorithm for any given Bayesian network inference problem, and then predict the run time performance. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Bayesian Metareasoner Run Time Performance Date Researcher Approximate Bn Inference Algorithm Bayesian Network Inference Problem Bn Inference Problem Instance Meta-level Reasoning System Hard Problem Different Class Different Property Bayesian Network Algorithm Selection Problem Ri76 Approximate Bn Inference Np-hard Co90 Appropriate Algorithm Bn Inference Expert Many Different Kind Inference Problem |
| Content Type | Text |
