This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliate uses CBR to allow RL to respond more quickly to changing conditions. CBRetaliate combines two key features: it uses a time window to compute similarity and stores...

In the paper, we investigate the use of reinforcement learning in CBR for estimating and managing a legacy case base for playing the game of Tetris. Each case corresponds to a local pattern describing the relative height of a subset of columns where pieces could be placed. We evaluate these patterns through reinforcement learning to determine if significant performance improvement can be observ...

Positive transfer learning (TL) occurs when, after gaining experience from learning how to solve a (source) task, the same learner can exploit this experience to improve performance and/or learning on a different (target) task. TL methods are typically complex, and case-based reasoning can support them in multiple ways. We introduce a method for recognizing intent in a source task, and then app...

A case in case-based reasoning is a contextualized piece of experience, which can be represented in various forms. Traditional approaches can be classified into three main categories: feature vector representations, structured representations, and textual representations. More sophisticated approaches make use of hierarchical representations or generalized cases. For particular tasks such as de...

In previous research we have shown the architecture and application of a case-based reasoning (CBR) system used to discover user preferences in an existing mixed-initiative dialogue system. In this paper we apply this CBR system to increasingly large datasets to test its ability to maintain nearreal time performance in generating new user preferences. We also propose possible future application...

Case-based reasoning (CBR) provides an adequate framework to cope with continuous domains, where a great amount of new valuable experiences are generated in a non-stop way. CBR systems become more competent in their evolution over time by means of learning new relevant experiences. There are two central problems derived from the continuous nature of some domains: the fast growing size of the ca...

Opinion Mining is a promising discipline, defined as an intersection of information retrieval and computational linguistic techniques to deal with the opinions expressed in a document. The field aims at solving the problems related to opinions about products, Politian in newsgroup posts, review sites, comments on Facebook posts and twitter etc. This paper is about to covers the techniques, appl...

Automated assessment and immediate feedback are staple features of modern e-learning systems. In the case of programming exercises, most systems only provide binary (correct/incorrect) feedback, which is often inadequate for students struggling with the material, as they may need expert guidance in order to successfully overcome obstacles to understanding. We propose a Case-Based Reasoning (CBR...

In this paper we present a method for supplementing incomplete cases with information from other cases within a case base. The acquisition of complete and correct cases is a time-consuming task, but nevertheless crucial for the quality and acceptance of a case-based reasoning system. The method introduced in this paper uses association rules to identify relations between attributes and, based o...

To meet time constraints, a CBR system must control the time spent searching in the case base for a solution. In this paper, we presents the results of a case study comparing the proficiency of some criteria for forgetting cases, hence bounding the number of cases to be explored during retrieval. The criteria being considered are case usage, case value and case density. As we make use of a sequ...