The program committee selected the paper "Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game" by David W. Aha (Naval Research Laboratory), The second day featured reasoning research. This report describes the conference in detail. David Aha noted the need Derek Bridge, the University College to enhance the theoretical foundations Case-Based Reasoning (ICCBR) Cork, and Craig Knoblock, the of CBR. College Dublin) stressed the fact that meeting on case-based reasoning ICCBR-05 received 74 paper submissions in recent years we have focused on (CBR). Of these, the program committee needed with respect to experience highlighting the most significant selected 26 for poster presentations modeling and reuse.

High retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. These mechanisms require that the user judges the quality of the results of the query by marking all the retrieved images as being either relevant or not. Then, the search engine exploits this information to adapt the search to better meet user's needs. At present, the vast majority of proposed relevance feedback mechanisms are formulated in terms of search model that has to be optimized. Such an optimization involves the modification of some search parameters so that the nearest neighbor of the query vector contains the largest number of relevant images.

This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate nearest neighbor approach called Locality Sensitive Hashing (LSH). In this paper we ask the question: can earlier spatial data structure approaches to exact nearest neighbor, such as metric trees, be altered to provide approximate answers to proximity queries and if so, how? We introduce a new kind of metric tree that allows overlap: certain datapoints may appear in both the children of a parent. We also introduce new approximate k-NN search algorithms on this structure. We show why these structures should be able to exploit the same randomprojection-based approximations that LSH enjoys, but with a simpler algorithm and perhaps with greater efficiency. We then provide a detailed empirical evaluation on five large, high dimensional datasets which show up to 31-fold accelerations over LSH. This result holds true throughout the spectrum of approximation levels.

This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate nearest neighbor approach called Locality Sensitive Hashing (LSH). In this paper we ask the question: can earlier spatial data structure approaches to exact nearest neighbor, such as metric trees, be altered to provide approximate answers to proximity queries and if so, how? We introduce a new kind of metric tree that allows overlap: certain datapoints may appear in both the children of a parent. We also introduce new approximate k-NN search algorithms on this structure. We show why these structures should be able to exploit the same randomprojection-based approximations that LSH enjoys, but with a simpler algorithm and perhaps with greater efficiency. We then provide a detailed empirical evaluation on five large, high dimensional datasets which show up to 31-fold accelerations over LSH. This result holds true throughout the spectrum of approximation levels.

High retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. These mechanisms require that the user judges the quality of the results of the query by marking all the retrieved images as being either relevant or not. Then, the search engine exploits this information to adapt the search to better meet user's needs. At present, the vast majority of proposed relevance feedback mechanisms are formulated in terms of search model that has to be optimized. Such an optimization involves the modification of some search parameters so that the nearest neighbor of the query vector contains the largest number of relevant images.

This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perceptionareas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate nearest neighbor approach called Locality Sensitive Hashing (LSH).In this paper we ask the question: can earlier spatial data structure approaches to exact nearest neighbor, such as metric trees, be altered to provide approximate answers to proximity queries and if so, how? We introduce a new kind of metric tree that allows overlap: certain datapoints may appear in both the children of a parent. We also introduce newapproximate k-NN search algorithms on this structure. We show why these structures should be able to exploit the same randomprojection-based approximationsthat LSH enjoys, but with a simpler algorithm and perhaps with greater efficiency. We then provide a detailed empirical evaluation on five large, high dimensional datasets which show up to 31-fold accelerations over LSH. This result holds true throughout the spectrum of approximation levels.

High retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. These mechanisms require that the user judges the quality of the results of the query by marking all the retrieved images as being either relevant or not. Then, the search engine exploits this information to adapt the search to better meet user's needs. At present, the vast majority of proposed relevance feedback mechanisms are formulated in terms of search model that has to be optimized. Such an optimization involves the modification of some search parameters so that the nearest neighbor of the query vector contains the largest number of relevant images.

Since 1994, GE Plastics has employed a case-based reasoning (CBR) tool that determines color formulas that match requested colors. This tool, called FormTool, has saved GE millions of dollars in productivity and material (that is, colorant) costs. The technology developed in FormTool has been used to create an online color-selection tool for our customers called ColorXpress Select. A customer innovation center has been developed around the FormTool software.

TheGeneral Motors Variation-Reduction Adviser is a knowledge system built on case-based reasoning principles that is currently in use in eighteen General Motors asssembly centers. This article reviews the overall characteristics of the system and then focuses on various AI elements critical to support its deployment to a production system. A key AI enabler is ontology-guided search using domainspecific ontologies.

Additional initial ontologies include: search was used, queries were expanded to include (4) single part issues--relate to only one more words to search for, and thus, relevant vehicle component, such as a ding in a fender; documents could be found. Since the documents (5) multiple part issues--relate to two or more being searched were in a limited parts, especially misalignments, unsatisfactory domain, there were few problems with multiple gaps, malformations of joints between parts; senses of words introducing problems that (6) data analysis--results of analysis of measurement hurt precision. In our database, case entries are data generated by optical and mechanical similar--the textual fields do not contain long gages; and (7) plant locations--zones descriptions, and the content is limited to and stations organized topologically or functionally.