Learning Automata: A Case Study
Abstract
Neural networks are trained to learn their expected behavior. Networks that are designed to learn a particular informational scheme are called learning critical to the operational performance of learning automata. In general, the automata is in some way rewarded for proper behavior and 'punished' for wrong behavior. Initially, all choices of behavior are random, but by using these learning rules of reward and punishment that is used can significantly affect both local and global learning and results in surprising revelations about achieving proper behavior. Learning automata can be applied to a variety of computational problems. For example, a neural network can be trained to recognize which of several available filters, classifiers, or other neural networks are best suited to a particular task. Scientists at the Naval Oceanographic and Atmospheric Research Laboratory's (NOARL's) Map Data Formatting Facility (MDFF) plan to apply this type of neural network training the their research in the automated feature extraction of digital maps. NOARL's dataset of interest consists of scanned aeronautical charts, provided by the Defense Mapping Agency, which are compressed by the MDFF computers into a form that is compatible with digital moving map systems onboard naval aircraft. In an effort to improve the quality of the output images, MDFF computer scientists are testing various digital image enhancement algorithms on this particular dataset. Learning automata could be used to help choose the best digital feature extraction process for a given subtask. For example, the vectorization of desert data requires a significantly different approach that used to classify rugged terrain.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 14, 1990
- Accession Number
- ADA233328
Entities
People
- Henry Rosche. Iii
- Maura Connor Lohrenz
Organizations
- United States Naval Research Laboratory