Fault diagnosis and prognosis of industrial equipment become increasingly important for improving the quality of manufacturing and reducing the cost for product testing. This paper advocates that computer-based diagnosis systems can be built based on sensor information and by using case-based reasoning methodology. The intelligent signal analysis methods are outlined in this context. We then ex...

This paper presents several industrial applications of ML in the context of their effort to solve the "KAML problem", i.e., the problem of merging knowledge acquisition and machine learning techniques. Case-based reasoning is a possible alternative to the problem of acquiring highly compiled expert knowledge, but it raises also many new problems that must be solved before really efficient imple...

Researchers have embraced a variety of machine learning (ML) techniques in their efforts to improve the quality of learning programs. The recent evolution of hybrid architectures for machine learning systems has resulted in several approaches that combine rule-induction methods with case-based reasoning techniques to engender performance improvements over moretraditional one-representation arch...

In this paper we present ColibriCook: a CBR system for ontology-based cooking recipe retrieval and adaptation. The system’s purpose is to participate in the 1st Computer Cooking Contest, organized by the European Conference on Case-Based Reasoning (ECCBR’08), at the University of Trier, Germany. CBR is based on a best-adaptation likeness paradigm between ingredient sets, with a domain ontology ...

The objective of this work is to interpret induc-tive results obtained by the unsupervised learning method OSHAM. We brie BLOCKINy introduce the learning process of OSHAM, that extracts concept hierarchies from unlabelled data, based on a representation combining the classical, prototype and exemplar views on concepts. The in-terpretive process is considered as an intrinsic part in OSHAM and is...

In this paper we propose an approach to address the old problem of identifying the feature conditions under which a gaming strategy can be effective. For doing this, we will build on previous work on CBRetaliate, a system that combines case-based reasoning and reinforcement learning to play team-based First Person Shooter Games. In CBRetaliate, cases are pairs (features, Q-table), where the Q-t...

The definition of similarity measures—one core component of each CBR application—leads to a serious knowledge acquisition problem if domain and application specific requirements have to be considered. To reduce the knowledge acquisition effort, different machine learning techniques have been developed in the past. In this paper, enhancements of our framework for learning knowledge-intensive sim...

Teachers and those who educate them are continually searching for innovative and effective tools to assist them in their task of learning to integrate technology. When teachers are seeking resources for solving a problem, they turn to databases, work groups, and communities of practice. They look for similar situations to see how problems were solved and then adapt the information that they fin...

ing Reusable Cases from Reinforcement Learning Andreas von Hessling and Ashok K. Goel College of Computing Georgia Institute of Technology Atlanta, GA 30318 {avh, goel}@cc.gatech.edu Abstract. Reinforcement Learning is a popular technique for gameplaying because it can learn an optimal policy for sequential decision problems in which the outcome (or reward) is delayed. However, Reinforcement Le...