Traditionally, crosswords are made as complete puzzles and then shared with players. In the digital age, crosswords can be created dynamically while the player plays, and can even react to how the player is performing. In this paper, we present a system for reactive crossword construction as a form of dynamic difficulty adjustment. Difficulty can be adapted within a single puzzle, by constructing a puzzle while the player plays, and the word/clue pairs can be chosen to be more or less difficult. We also show that hints, in the form of additional words, can be embedded into the puzzle for struggling players. We tested this system with three player personas across five levels of puzzle difficulty, using time as a proxy for difficulty. Our results show that reactive puzzle construction reduced the time needed to solve puzzles compared to static construction. The addition of hints further reduced the time to solve. These results are limited to simulated player personas, but establish a foundation and identify directions for future evaluation with real players. We expect that reactive puzzle construction could be applied to otherwise static puzzle types, by revealing clues as players progress.

When generating levels, algorithmically evaluating the results is essential. In this paper, we looked at predicting a level’s difficulty and enjoyment. Past work has approached this problem for puzzle games like Sudoku by analyzing the characteristics of the initial level, the solved level, and the process that led to that solution. In this work, we examined a set of heuristics for Roguelike levels and their solutions, and their relationship to subjective player ratings of the levels. We gathered ratings of difficulty and enjoyment of levels in a study with 143 players. We ran an ablation study on the set of heuristics to find the best combination of heuristics for predicting difficulty and enjoyment with a linear regression model, and found solution path-based heursitics performed well. However, these models did not outperform a simple baseline for predicting enjoyment. Jaccard similarity on paths—a method we have not seen used in the field of game AI—was a useful predictor of difficulty. Testing proximity to enemies across a solution path is the only heuristic needed to predict how enjoyable a level will be.

An increasingly common area of study in procedural content generation is the creation of level segments: short pieces that can be used to form larger levels. Previous work has used concatenation to form these larger levels. However, even if the segments themselves are completable and well-formed, concatenation can fail to produce levels that are completable and can cause broken in-game structures (e.g. malformed pipes in Mario). We show this with three tile-based games: a side-scrolling platformer, a vertical platformer, and a top-down roguelike. To address this, we present a Markov chain and a tree search algorithm that finds a link between two level segments, which uses filters to ensure completability and unbroken in-game structures in the linked segments. We further show that these links work well for multi-segment levels. We find that this method reliably finds links between segments and is customizable to meet a designer’s needs.

In the context of procedural content generation via machine learning (PCGML), quality-diversity (QD) algorithms are a powerful tool to generate diverse game content. A branch of QD uses genetic operators to generate content (e.g. MAP-Elites). Problematically, levels generated with these operators have no guarantee of matching the style of a game. This can be addressed by incorporating whether a level is generable by an n-gram into the fitness function. Unfortunately, this leads to wasted computation and poor results. In this work, we introduce n-gram genetic operators, which produce only solutions that are generable by the n-gram model; we call MAP-Elites combined with these operators Gram-Elites. We test on a tile-based side-scrolling platformer, vertical platformer, and roguelike. For all three, n-gram operators outperform standard operators and random n-gram generation, finding more usable (i.e. completable and generable) solutions at a faster rate. By integrating structure into operators, instead of fitness, these genetic operators could be beneficial to QD in PCGML.

Reflection is a critical aspect of the learning process. However, educational games tend to focus on supporting learning concepts rather than supporting reflection. While reflection occurs in educational games, the educational game design and research community can benefit from more knowledge of how to facilitate player reflection through game design. In this paper, we examine educational programming games and analyze how reflection is currently supported. We find that current approaches prioritize accuracy over the individual learning process and often only support reflection post-gameplay. Our analysis identifies common reflective features, and we develop a set of open areas for future work. We discuss these promising directions towards engaging the community in developing more mechanics for reflection in educational games.