Advances in Case-Based Reasoning: Second European Workshop, EWCBR-94, Chantilly, France, November 7 - 10, 1994. Selected Papers
Jean-Paul Haton, Mark Keane, Michel Manago
Springer, Nov 27, 1995 - Expert systems (Computer science) - 307 pages
This book presents a selection of revised refereed papers taken from the contributions to the Second European Workshop on Case-Based Reasoning, EWCBR-94, held at the Abbaye de Royaumont near Paris in November 1994. The 22 papers included were chosen from a total of 60 submissions. The important evolution by experienced artificial intelligence during the last few years has been essentially influenced by case-based reasoning, particularly by the area of knowledge-based decision support. This book documents the progress achieved in CBR methods and tools during the very recent past. It also outlines the substantial success achieved in the applications domain, especially in the fields of architecture and computer-aided design, task planning, chemical synthesis, maintenance and diagnosis, and law.
14 pages matching learning goals in this book
Results 1-3 of 14
What people are saying - Write a review
We haven't found any reviews in the usual places.
Integrating Induction in a CaseBased Reasoner
Experimental Study of an Evaluation Function for Cases Imperfectly Explained
14 other sections not shown
abstract adaptation algorithm application approach architecture Artificial Intelligence behaviour building CADRE case-based reasoning case-based reasoning system CBR system chess positions classified combination comparison components Computer computer chess concepts Conference on Artificial constraints context decision tree described diagnosis domain knowledge domain theory episodic frames evaluation example experiments explanation Figure footprint free features function generalisation gestalt gestalten given heuristic Hierarchy incremental CBR inductive input integration k-d tree Knowledge Acquisition knowledge base knowledge engineering knowledge representation learning goals legal rules Machine Learning matching memory Meta-AQUA method Morgan Kaufmann Neural Network node numbers of relevant objects paper parameters pawn performance protein prototype relevant attributes represented requirements retrieval reuse Ripple Down Rules selected similarity sketch solution specific step structure task telling the truth transition rules unmatching facts values Veloso Wess workpiece Workshop