- L. Karl Branting, William Reiners, and Yulan Wei
(2000). Induction for Ecological Hypothesis Evaluation: A Case Study,
University of Wyoming Department of Computer Science Technical Report. PDF
Abstract: Identification of the
processes that produce patterns of life is a central objective of
ecology. This paper explores how machine learning techniques can help
evaluate ecological hypotheses. Seven features were derived from GIS
coverages of a portion of the Wyoming Snowy Range. The ability of these
featues to predict occurrence of trees was tested using various
induction algorithms. Wind velocity was found to be a weaker
determinant of tree occurrence than topographical features.
- Branting, L. K., & Aha, D. W. (1995). Stratified
case-based reasoning: Reusing hierarchical problem solving episodes
(Technical Report AIC-95-001). Washington, DC: Naval Research
Laboratory, Navy Center for Applied Research in Artificial
Intelligence.
Abstract: Stratified case-based
reasoning is a technique in which abstract solutions produced during
hierarchical problem solving are used to assist case-based retrieval,
matching, and adaptation. We describe the motivation for the
integration of case-based reasoning with hierarchical problem solving,
exemplify its benefits, detail a set of algorithms that implement our
approach, and present their comparative empirical evaluation on a path
planning task. Our results show that stratified case-based reasoning
significantly decreases the computational expense required to retrieve,
match, and adapt cases, leading to performance superior both to simple
case-based reasoning and to hierarchical problem solving ab initio.
Keyphrases: Case-based reasoning,
hierarchical problem solving, learning, search
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