Posts Tagged ‘Explanation-based learning’
An Explanation-based Learning (EBL ) system accepts an example (i.e. a training example) and explains what it learns from the example. The EBL system takes only the relevant aspects of the training. This explanation is translated into particular form that a problem solving program can understand. The explanation is generalized so that it can be used to solve other problems.
PRODIGY is a system that integrates problem solving, planning, and learning methods in a single architecture. It was originally conceived by Jaime Carbonell and Steven Minton, as an AI system to test and develop ideas on the role that machine learning plays in planning and problem solving. PRODIGY uses the EBL to acquire control rules.
The EBL module uses the results from the problem-solving trace (ie. Steps in solving problems) that were generated by the central problem solver (a search engine that searches over a problem space). It constructs explanations using an axiomatized theory that describes both the domain and the architecture of the problem solver. The results are then translated as control rules and added to the knowledge base. The control knowledge that contains control rules is used to guide the search process effectively.