Experience is one of the major factor of success of Case-Based Reasoning systems. This paper presents a learning algorithm that introduces reminding to update dynamically the experience of a system using a Reinforcement Learning model. Current research focuses on maintaining the experience growth by applying reduction techniques, but usually they do not consider adding new experience. For this ...

Humans have developed jurisprudence as a mechanism to solve conflictive situations by using past experiences. Following this principle, we propose an approach to enhance a multi-agent system by adding an authority which is able to generate new regulations whenever conflicts arise. Regulations are generated by learning from previous similar situations, using a machine learning technique (based o...

The focus of this paper is on the introduction of a quite general type of case-based reasoning systems called logical case memory systems. The development of the underlying concepts has been driven by investigations in certain problems of case-based learning. Therefore, the present development of the target concepts is accompanied by an in-depth discussion of related learning problems. Logical ...

The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically ind...

In this paper, a fuzzy case based reasoning approach to Web access path prediction is developed and tested. It is based on the assumption that new user's access patterns can be predicted by referencing to the behaviors of similar Web users in the past. This method has three phases. Firstly, a Web case base is constructed from the Web log data. This includes the pre-processing and cleaning of th...

The ability of case-based reasoning systems to solve novel problems depends on their capability to adapt past solutions to new circumstances. However, acquiring the knowledge required for case adaptation is a classic challenge for CBR. This motivates the use of machine learning to generate adaptation knowledge. Much adaptation learning research has studied the case difference heuristic (CDH) ap...

A basic observation is that case-based reasoning has roots in different disciplines: cognitive science, knowledge representation and processing, machine learning and mathematics. As a consequence, there are foundational aspects from each of these areas. We briefly discuss them and comment on the relations between these types of foundations.

Transfer learning is the process of using knowledge gained while solving one problem to solve a new, previously unencountered problem. Current research has concentrated on analogical transfer – a mechanic is able to fix a type of car he has never seen before by comparing it to cars he has fixed before. This approach is typical of case-based reasoning systems and has been successful on a wide va...

In case-based reasoning (CBR) systems for product recommendation, the retrieval of acceptable products based on limited information is an important and challenging problem. As we show in this paper, basic retrieval strategies such as nearest neighbor are potentially unreliable when applied to incomplete queries. To address this issue, we present techniques for automating the discovery of recomm...

An important focus of recent CBR research is on how to develop strategies for achieving compact, competent case-bases, as a way to improve the performance of CBR systems. However, compactness and competence are not always good predictors of performance, especially when problem distributions are non-uniform. Consequently, this paper argues for developing methods that tie case-base maintenance mo...