Abstract. In this paper, we propose an approach to attach semantic annotations to textual cases for their representation. To achieve this goal, a framework that combines machine le...
Typically, case-based recommender systems recommend single items to the on-line customer. In this paper we introduce the idea of recommending a user-defined collection of items whe...
Conor Hayes, Paolo Avesani, Emiliano Baldo, Padrai...
Tutoring systems have been a popular domain for CBR since its very beginning. In this paper we draw a connection between casebased teaching and learning-by-doing approach to tutori...
In this paper we partially describe JV2 M, a metaphorical simulation of the Java Virtual Machine where students can learn Java language compilation and reinforce object-oriented pr...
Conversational Case Based Reasoning (CCBR) is a form of CBR where users initiate conversations with the system to solve a certain problem. Current CCBR solutions are limited to spe...
CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agen...
Traditional CBR approaches imply centralized storage of the case base and, most of them, the retrieval of similar cases by an exhaustive comparison of the case to be solved with th...
This paper presents a model constructed for the evaluation of the interaction of the atmosphere and the ocean. The work here presented focuses in the development of an agent based ...