Knowledge-Based Neural Networks (KBNN) are concerned with the use of domain knowledge to determine the initial structure of Neural Networks(NN). KBNN are shown to classify better unseen examples than randomly initialized NN. In this paper we study the potential of Case-Based Reasoning (CBR) for further improvement of a trained KBNN. The idea is to apply CBR only for correction of KBNN solutions...

JAGUAR is a set of tools that perform model-based planning and replanning, real-time execution monitoring, and adaptive modeling in the domain of military air operations. Model driven planners are susceptible to biases, omissions, and errors within the models. Operating in a dynamic, realtime environment requires continual model updates to meet the ever changing requirements. This paper describ...

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning ...

Product recommender systems are a popular application and research field of CBR for several years now. However, almost all CBRbased recommender systems are not case-based in the original view of CBR, but just perform a similarity-based retrieval of product descriptions. Here, a predefined similarity measure is used as a heuristic for estimating the customers’ product preferences. In this paper ...

The paper presents an approach to describe the semantics of reusable software components by specifiably chosen input-output tuples. The initial data basis for such tuples are test cases. We discuss, how test cases can serve as descriptors for software components. Further, it is shown how an optimal search structure can be obtained from such tuples by means of supervised learning.

Several studies have compared the prediction accuracy of different types of techniques with emphasis placed on linear and stepwise regressions, and Case-based Reasoning (CBR). We believe the use of only one type of CBR technique may bias the results, as there are others that may also be used for effort prediction. This paper has two objectives. The first is to compare the prediction accuracy of...

0164-1212/$ see front matter 2008 Elsevier Inc. A doi:10.1016/j.jss.2008.06.001 * Corresponding author. Tel.: +65 83442816. E-mail address: [email protected] (Y.F. Li). A number of software cost estimation methods have been presented in literature over the past decades. Analogy based estimation (ABE), which is essentially a case based reasoning (CBR) approach, is one of the most popular techni...

In both research fields, Case-Based Reasoning and Reinforcement Learning, the system under consideration gains its expertise from experience. Utilizing this fundamental common ground as well as further characteristics and results of these two disciplines, in this paper we develop an approach that facilitates the distributed learning of behaviour policies in cooperative multi-agent domains witho...

Classification involves associating instances with particular classes by maximizing intra-class similarities and minimizing inter-class similarities. Thus, the way similarity among instances is measured is crucial for the success of the system. In case-based reasoning, it is assumed that similar problems have similar solutions. The case-based approach to classification is founded on retrieving ...

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...