EBL in Prodigy
EBL in Prodigy
EBL produces control rules by examining the complete problem-solving
trace generated by the planner. This process is divided into two
steps. First, knowledge gained from a trace is compressed into control
rules. Second, these rules are evaulated to determine their
utility. Utility is expressed as the rule's value in probable
search-reduction minus the probable matching cost of the rule. Only
rules with a high utility are stored. If the evaulation is accurate, a
stored control rule will be easy to match, and will save search
time. The purpose of this evaluation is to keep only those rules that
will most likely result in faster problem solving when utilized in
future planning.
Learning from failure is an important capability of EBL. Because EBL
has access to the complete trace, it can exploit failures as well as
successes. Dead ends in the search tree may provide EBL with knowledge
it can compress into control rules.
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