Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

Creative Teaching in EFL Classrooms: Voices from Afghanistan

This qualitative research aims to explore the reported experiences of five Afghan EFL instructors at the English Department, Herat University, concerning the creative teaching of English. It specifically investigated the challenges and the opportunities regarding the creative teaching of English that these teachers have encountered in their courses. The researchers employed constructivist perspectives of learning in which learners make meaning out of their previous experiences and knowledge (Hill, 2014) as the theoretical framework to analyze and interpret the data. The data were collected through semi-structured interviews and analyzed by using thematic analysis. The findings indicated that creative teaching made the learning process fun. It also revealed that creative teaching increased students’ participation and motivation because it put students in the center of the learning process. The findings also demonstrated that some students showed resistance toward change—moving from a teacher-centered approach to a student-centered approach—when creative teaching was implemented. It also showed that some instructors needed the support of higher education administrators to incorporate creative teaching into their courses as there was a conspicuous lack of professional development needs in this regard. The study argued that the creative teaching of English positively impacted students’ academic achievements. This study could serve as a significant way to introduce information and strategies on creative teaching to L2 instructors in similar contexts as Afghanistan. The results provided implications for creative teaching in EFL classrooms as well as for the future of teaching English in ESL and EFL contexts

PCGRL: Procedural Content Generation via Reinforcement Learning

We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes and apply these to three game environments.Comment: 7 pages, 7 figures, 1 table, published at AIIDE202