Learning models of player behavior has been the focus of several studies. This work is motivated by better understanding of player behavior, a knowledge that can ultimately be employed to provide player-adapted or personalized content. In this paper, we propose the use of active learning for player experience modeling. We use a dataset from hundreds of players playing Infinite Mario Bros. as a ...

We propose in this paper a new active learning method that makes no considerations about the data distribution and does not need to adjust any free parameter. The proposed algorithm is based on extreme learning machines (ELM) and a perceptron with analytical calculation of weights. We show that the proposed model have good results using a reduced set of patterns.

The performance of image retrieval with SVM active learning is known to be poor when started with few labelled images only. In this paper, the problem is solved by incorporating the unlabelled images into the bootstrapping of the learning process. In this work, the initial SVM classifier is trained with the few labelled images and the unlabelled images randomly selected from the image database....

This paper discusses the implementation of experiential learning techniques in a behavioural economics class. In order to deepen students’ understanding of both behavioural economics and the experimental approach to research students in the course developed and conducted variants of economic experiments.We believe that the process of designing and implementing the experiments fostered a better ...

In “natural learning” the learner takes responsibility for learning. This responsibility applies to setting objectives, selecting active learning tasks, obtaining feedback, and making applications. Self-oriented skill training (SOS) provides a highly structured procedure to help the learner through the above four phases of natural learning. Of particular importance in SOS is the experiential ex...

In this paper we review some Active Learning experimental results in order to set up the basis for designing an active learning based system for corpus annotation. Based on the experimental data we design a modular system that allows for initially learning fast, but that it is capable of switching to a slower and more precise learning strategy. The system is designed to perform a semantic role ...

Active learning and domain adaptation are both important tools for reducing labeling effort to learn a good supervised model in a target domain. In this paper, we investigate the problem of online active learning within a new active domain adaptation setting: there are insufficient labeled data in both source and target domains, but it is cheaper to query labels in the source domain than in the...

In this paper we investigate a computational model of word learning that is cognitively plausible. The model is partly trained on incorrect form-referent pairings, modelling the input to a word-learning child that may contain such mismatches due to inattention to a joint communicative scene. We introduce a procedure of active learning, based on attested cognitive processes. We then show how thi...