http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
The performance of all reasoning systems crucially depends on problem representation: the same problem may be easy or difficult, depending on the way we describe it. Researchers in psychology and artificial intelligence have accumulated much evidence on the importance of appropriate representations for both human and artificial intelligence systems. The book proposes techniques for automatic improvement of problem representation, which are based on integration of multiple learning and problem-solving algorithms. It gives theoretical foundations of the proposed techniques, describes their implementation, and discusses empirical evidence of their utility.
Introduction: Motivation. Prodigy search -- Description changers: Primary effects. Abstraction. Summary and extensions -- Top-level control: Multiple representations. Statistical selection. Statistical extensions. Summary and extensions -- Empirical results: Machining Domains. Sokoban Domain. Extended Strips Domain. Logistics Domain. Concluding remarks. References.
Studies in fuzziness and soft computing ; 110