No abstract

Jumping has been an important mechanic since its introduction in Donkey Kong. It has taken a variety of forms and shown up in numerous games, with each jump having a di erent feel. In this paper, we use a modi ed Nintendo Entertainment System (NES) emulator to semi-automatically run experiments on a large subset (∼30%) of NES platform games. We use these experiments to build models of jumps from di erent developers, series, and games across the history of the console. We then examine these models to gain insights into di erent forms of jumping and their associated feel.

We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system. Working from a sequence of continuous state values and predicates about the environment, CHARDA recovers the distinct dynamic modes, learns a model for each mode from a given set of templates, and postulates causal guard conditions which trigger transitions between modes. Our main contribution is the use of information-theoretic measures (1) as a cost function for data segmentation and model selection to penalize over-fitting and (2) to determine the likely causes of each transition. CHARDA is easily extended with different classes of model templates, fitting methods, or predicates. In our experiments on a complex videogame character, CHARDA successfully discovers a reasonable over-approximation of the character's true behaviors. Our results also compare favorably against recent work in automatically learning probabilistic timed automata in an aircraft domain: CHARDA exactly learns the modes of these simpler automata.

Game maps are useful for human players, general-game-playing agents, and data-driven procedural content generation. ese maps are generally made by hand-assembling manually-created screenshots of game levels. Besides being tedious and error-prone, this approach requires additional e ort for each new game and level to be mapped. e results can still be hard for humans or computational systems to make use of, privileging visual appearance over semantic information. We describe a so ware system, Mappy, that produces a good approximation of a linked map of rooms given a Nintendo Entertainment System game program and a sequence of bu on inputs exploring its world. In addition to visual maps, Mappy outputs grids of tiles (and how they change over time), positions of non-tile objects, clusters of similar rooms that might in fact be the same room, and a set of links between these rooms. We believe this is a necessary step towards developing larger corpora of high-quality semantically-annotated maps for PCG via machine learning and other applications.

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.