Abstract

Abstract
Computer Assisted Qualitative Data Analysis Software (CAQDAS) are tools that help researchers to develop qualitative research projects. These software packages help the users with tasks such as transcription analysis, coding and text interpretation, writing and annotation, content search and analysis, recursive abstraction, grounded theory methodology, discourse analysis, data mapping, and several other types of analysis. This paper focus the new paradigm of self-learning, that presents itself increasingly as a competence to support learning in a proactive way. It further analyses education and CAQDAS with emphasis on the use of CAQDAS in educational research and the self-learning of CAQDAS. The study conducted had two main goals: (1) analyse the self-learning tools of CAQDAS and (2) identify CAQDAS's users learning profile. Six software packages were selected: NVivo, Atlas.ti, Dedoose, webQDA, MAXQDA, and QDA Miner. They were reviewed, taking into account their transversality, language, (self-learning) tools, among other criteria. The results show that there is a considerable demand for information from users regarding the execution of processes in CAQDAS, and that the packages analysed do not guide users towards the self-learning tools that best fit their learning style.

Abstract
We investigate the learning of a flexible humanoid robot kick controller, i.e., the controller should be applicable for multiple contexts, such as different kick distances, initial robot position with respect to the ball or both. Current approaches typically tune or optimise the parameters of the biped kick controller for a single context, such as a kick with longest distance or a kick with a specific distance. Hence our research question is that, how can we obtain a flexible kick controller that controls the robot (near) optimally for a continuous range of kick distances? The goal is to find a parametric function that given a desired kick distance, outputs the (near) optimal controller parameters. We achieve the desired flexibility of the controller by applying a contextual policy search method. With such a contextual policy search algorithm, we can generalize the robot kick controller for different distances, where the desired distance is described by a real-valued vector. We will also show that the optimal parameters of the kick controller is a non-linear function of the desired distances and a linear function will fail to properly generalize the kick controller over desired kick distances. © 2017, Springer International Publishing AG.

Abstract
During the last decade, Computer Poker has become the preferred test-bed for validating developments on the extensive-form game and multi-agent systems research domains. Because Poker is a game with hundreds of variants differing from each other by their betting structure, number of cards in the deck or winning conditions, numerous agents have been created for several different variants of the game. However, there is not a single unified description model that allows for those agents to be tested across different Poker variants inexpensively. For this reason, we introduce the Poker Game Description Language (PGDL), which, unlike other incomplete information GDL's, is uniquely focused on Poker agent development and testing. PGDL is integrated into a playable system which not only makes available a basic Agent Development API in Prolog, but also provides a simple in-built agent which can adapt to user-defined rules. In addition, this framework has a simple GUI which both basic and advanced test subjects demonstrated to be adequate and easy-to-use when defining new PGDL instances. We believe that despite the existence of more generic general game playing systems, the fact that our language natively supplies a shared infrastructure, common to all Poker variants, renders our approach very pertinent for Poker agent development. Tests demonstrated that our language was capable of describing the most popular Poker variants.

Abstract

Abstract