• 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