Speaker
Dr
Johnny Stovall
(Dr. Stovall Foundation for Practical Education and Social Development)
Description
Data-driven parsers have been used in AI and inductive reasoning in ways beyond the abilities of rule-driven parsers. This paper presents an annotated bibliography and guidelines for developing multipurpose data-driven parsers in hopes that developers will assist the author in disaster recovery and OLPC development, will push Free Open Software to new heights, and will benefit from commercial opportunities.
Data-driven parsers of the highest types don't require a predetermined set of rules like rule-driven parsers do. This is most like discovery learning that maybe helps children to learn their first language so easily and fluently. The author contends this same type of learning is necessary for natural responses to a wide variety of real world situations. The data drives the progress and in the higher types can change goals just like it real life. Rule driven parsers are primarily useful in closed languages such as computer programs where the parser developer can know all of the rules before starting.
Declarative programming will also be briefly described. This is an underused part of Python's flexibility but the basis for some data-driven parsers.
Author
Dr
Johnny Stovall
(Dr. Stovall Foundation for Practical Education and Social Development)
