Active Learning for Classifying 2D Grid-Based Level Completability

Determining the completability of levels generated by procedural generators such as machine learning models can be challenging, as it can involve the use of solver agents that often require a significant amount of time to analyze and solve levels. Active learning is not yet widely adopted in game evaluations, although it has been used successfully in natural language processing, image and speech recognition, and computer vision, where the availability of labeled data is limited or expensive. In this paper, we propose the use of active learning for learning level completability classification. Through an active learning approach, we train deep-learning models to classify the completability of generated levels for Super Mario Bros., Kid Icarus, and a Zelda-like game. We compare active learning for querying levels to label with completability against random queries. Our results show using an active learning approach to label levels results in better classifier performance with the same amount of labeled data.Comment: 4 pages, 3 figure

Latent Combinational Game Design

We present latent combinational game design -- an approach for generating playable games that blend a given set of games in a desired combination using deep generative latent variable models. We use Gaussian Mixture Variational Autoencoders (GMVAEs) which model the VAE latent space via a mixture of Gaussian components. Through supervised training, each component encodes levels from one game and lets us define blended games as linear combinations of these components. This enables generating new games that blend the input games and controlling the relative proportions of each game in the blend. We also extend prior blending work using conditional VAEs and compare against the GMVAE and additionally introduce a hybrid conditional GMVAE (CGMVAE) architecture which lets us generate whole blended levels and layouts. Results show that the above approaches can generate playable games that blend the input games in specified combinations. We use both platformers and dungeon-based games to demonstrate our results

Flight Measurements of the Flying Qualities of a Lockheed P-80A Airplane (Army No. 44-85099) - Stalling Characteristics

This report contains the flight-test results of the stalling characteristics measured during the flying-qualities investigation of the Lockheed P-8OA airplane (Army No. 44-85099). The tests were conducted in straight and turning flight with and without wing-tip tanks. These tests showed satisfactory stalling characteristics and adequate stall warning for all configurations and conditions tested

Player Rating Systems for Balancing Human Computation Games : Testing the Effect of Bipartiteness

Human Computation Games (HCGs) aim to engage volunteers to solve information tasks, yet suffer from low sustained engagement themselves. One potential reason for this is limited difficulty balance, as tasks difficulty is unknown and they cannot be freely changed. In this paper, we introduce the use of player rating systems for selecting and sequencing tasks as an approach to difficulty balancing in HCGs and game genres facing similar challenges. We identify the bipartite structure of user-task graphs as a potential issue of our approach: users never directly match users, tasks never match tasks. We therefore test how well common rating systems predict outcomes in bipartite versus non-bipartite chess data sets and log data of the HCG Paradox. Results indicate that bipartiteness does not negatively impact prediction accuracy: common rating systems outperform baseline predictions in HCG data, supporting our approach’s viability. We outline limitations of our approach and future work

Adapting Cognitive Task Analysis to Elicit the Skill Chain of a Game

Playing a game is a complex skill that comprises a set of more basic skills which map onto the component mechanics of the game. Basic skills and mechanics typically build and depend on each other in a nested learning hierarchy, which game designers have modelled as skill chains of skill atoms. For players to optimally learn and enjoy a game, it should introduce skill atoms in the ideal sequence of this hierarchy or chain. However, game designers typically construct and use hypothetical skill chains based solely on design intent, theory, or personal observation, rather than empirical observation of players. To address this need, this paper presents an adapted cognitive task analysis method for eliciting the empirical skill chain of a game. A case study illustrates and critically reflects the method. While effective in foregrounding overlooked low-level skills required by a game, its efficiency and generalizability remain to be proven