Advances in Case-based Reasoning: ... European Workshop, EWCBR ... : Selected PapersSpringer, 1994 - Expert systems (Computer science) |
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Page 200
... CADRE avoids partitioning of various abstractions and maintains simultaneously requirements of diverse abstractions using the dimensionality reduction process ( Faltings et al . , 1991 ) . 2 CADRE : System Architecture and Processes 2.1 ...
... CADRE avoids partitioning of various abstractions and maintains simultaneously requirements of diverse abstractions using the dimensionality reduction process ( Faltings et al . , 1991 ) . 2 CADRE : System Architecture and Processes 2.1 ...
Page 204
... CADRE is provided . Most of the interaction with CADRE takes place in a main working window . Pulldown menus provide access to various operations , e.g. , calling up a case browser . A case may be brought up and viewed using various ...
... CADRE is provided . Most of the interaction with CADRE takes place in a main working window . Pulldown menus provide access to various operations , e.g. , calling up a case browser . A case may be brought up and viewed using various ...
Page 206
... CADRE and any changes are propagated to related variables inside relevant areas of the building ( Figure 6 , right ) . Once the dimensional adaptation is carried out , it may be necessary , as in this example , to also undertake ...
... CADRE and any changes are propagated to related variables inside relevant areas of the building ( Figure 6 , right ) . Once the dimensional adaptation is carried out , it may be necessary , as in this example , to also undertake ...
Contents
Integrating Induction in a CaseBased Reasoner | 3 |
Methodological Approach | 18 |
Experimental Study of an Evaluation Function for Cases Imperfectly Explained | 33 |
Copyright | |
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abstract adaptation algorithm application approach architecture Artificial Intelligence behaviour building CADRE case-based reasoning case-based reasoning system CBR system classified co-domain combination comparison component Computer concepts Conference on Artificial constraints decision tree defined 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 NOOS numbers of relevant objects paper parameters pawn performance protein prototype purification plan relevant attributes represented requirements retrieval reuse Rick's Ripple Down Rules selected similarity sketch solution specific step structure task telling the truth transition rules unmatching facts values Veloso Wess workpiece Workshop