## 3 Related Works
### Games for Solving Problems  
Games with a purpose have been developed to utilize individuals' problem-solving skills for addressing computationally challenging problems [37]. These games focus on various domains, such as biology, biochemistry, and linguistics. An example of such a game is BioGames [38], which is an image-matching game that trains its players to recognize malaria-infected red blood cells. This not only provides an engaging training program for student medical personnel but also serves as a means for crowdsourcing labeling data to train machine learning algorithms. The results of BioGames demonstrate the potential of a crowd of untrained individuals to achieve a disease diagnosis with an accuracy comparable to that of an expert. The game Phylo [21] leverages the concept of color matching to facilitate the optimization of nucleotide sequence alignment, thus minimizing the number of mutations needed to produce a different species from an ancestor. This process enables the generation of phylogenetic trees, thereby providing deeper insights into the evolutionary relationships between species with sequenced DNA. The problem of multiple sequence alignment has been shown to be NP-complete [39]; however, Phylo serves as an example of how such problems can be transformed into engaging games. Foldit [40] and EteRNA are two games designed to facilitate the discovery of protein folding and RNA folding mechanisms, respectively. After acquiring a basic understanding of the rules, non-expert players can predict the folding of complex molecules. The players of EteRNA vote on each other's predictions, which are later synthesized and evaluated for accuracy. Foldit allows players to automate their common sequences of actions through a feature called recipes, and through observing these recipes, new strategies have been discovered that improve the performance of existing algorithms [20]. This demonstrates the potential of crowd computing to uncover new algorithms and heuristics for complex problems. Foldit further encodes and conveys the insights gained by players by providing them with a scripting language. EyeWire [41; 42] is a game aimed at mapping the outlines of neurons in 3D space from 2D images obtained from a retina. The player is tasked with tracing the path of a neuron in 3D space by following the edges of the 2D images. The game complements algorithmic approaches by directing the player's focus to areas where the algorithm is uncertain, thereby facilitating the generation of complete maps of neurons and their connections from images. EyeWire represents a successful example of human-computer cooperation in solving a problem, utilizing the strengths of each in areas where the other is ineffective. Google Image Labeler (GIL), which is based on the game called ESP [43], employs a strategy of using human players to label images obtained from the web. The gameplay involves two players, who are unable to communicate with each other, attempting to propose matching labels for an image. When both players agree on the same label, it is then utilized as metadata to enhance the image search service of the company. Similar to GIL's goals and gameplay, TagATune [44; 45] aimed at obtaining metadata for audio files, such as the genre, type of instruments being played, and gender of the vocalists. Both GIL and TagATune incorporate a multiplayer component to increase their entertainment value and attract more players, which also serves as a validation mechanism. With the players incentivized to agree on a label but unable to communicate except through the game's mechanics, poor solutions can be easily detected and disregarded, whether submitted intentionally or not. The focus of several games, including JeuxDeMots [46], OnToGalaxy [47], Verbosity [48], Phrase Detectives [49], and ZombiLingo [50], is on establishing relationships between words and acquiring common sense knowledge of them.