# Procedural Video Game Scene Generation by Genetic and Neutrosophic WASP Algorithms
## 2 Related Work  
There is a rising interest in automated game level generation. Machine learning algorithms are masters of specific computational tasks, but there is no perfect solution to mimic human creativity. The primary goal of this type of research is to identify creativity measurements and apply them to automated content generation. The current stage of results in this field is mostly exploratory and does not substitute for creative work in most cases, but it gradually increases assistance levels for the creator by overtaking simple creative tasks. PCGML is one of the assistance tools for work generation. There are 4 modeling steps: problem identification, solution, results, and application of generated results.  
There are research examples that tackle the problem of computed creativity in the game design field. One of the examples in the industry is the generation of physical puzzle game levels with the objective of fitness of the feasibility and stability of objects [26]. Final fitness is calculated using an agent that plays the game. This method reduces the computational cost for this problem and adds new solutions to calculate the rewards of the genetic algorithm, not focusing too much on the penalty. Another example is a level generator for a Lode Runner-type game. It assesses playability and connectivity using the 'A\" algorithm. Generator uses an autoencoder with a multi-channel approach, analyzes 150 pre-made levels, and uses evolutionary algorithms. Levels are encoded into multichannel strings. This solution adds some unpredictability. Performance evaluation compares similarity to the original game levels [27]. There is also a framework for general 2D games (mostly top-down adventures) [28]. It evaluates levels for symmetry, balance, density, and reachability with a focus on aesthetics and difficulty. For final fitness, it derives 3 different fitness values and calculates the average value (Score Difference Fitness, Unique Rule Fitness, and Metric Based Fitness). It tries to apply the procedural video game generation problem to a variety of games with differing rules. Another example is focused on creative patterns [29]. The match 3 type game is used as a base for the generator. For evaluation, it uses visual pattern recognition and line symmetry. The results are judged by expert study analysis. It learns from existing content and uses pattern-aware PCGML, random Markov fieldswith symmetric positional information, and visual analysis. This research tries to generate larger structural patterns. The Pac-Man arcade-type game [30] evaluates playability, the spread of objects, ratios, and evolving levels using a genetic algorithm. It tries to generate unique levels with each iteration of the algorithm. Another more generalized example is the generation of verticality for mostly flat surfaces on grid-based surfaces [31].  
Most of the examples in the literature uses 2D space for experimentation and games from the 1980s or simple game levels made specifically for the selected tasks. Usual objects for game levels are empty space, wall, player, goal, collectibles, and hazards. General evaluation criteria are guidance, progression, aesthetics, safe zones, and pace breaking. The current state of the computational creativity field in video games is quite young and has not yet been applied to a game structure for large and complex games. It is also difficult to model systems that can fully replicate manual creative work. As the result becomes more complex, it is easier to distinguish synthetic creativity.