Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9651773 | International Journal of Approximate Reasoning | 2005 | 28 Pages |
Abstract
This paper investigates methods that balance time and space constraints against the quality of Bayesian network inferences--we explore the three-dimensional spectrum of “time Ã space Ã quality” trade-offs. The main result of our investigation is the adaptive conditioning algorithm, an inference algorithm that works by dividing a Bayesian network into sub-networks and processing each sub-network with a combination of exact and anytime strategies. The algorithm seeks a balanced synthesis of probabilistic techniques for bounded systems. Adaptive conditioning can produce inferences in situations that defy existing algorithms, and is particularly suited as a component of bounded agents and embedded devices.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Fabio Tozeto Ramos, Fabio Gagliardi Cozman,