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Human-based computation game
A human-based computation game or game with a purpose (GWAP) is a human-based computation technique of outsourcing steps within a computational process to humans in an entertaining way (gamification).
Luis von Ahn first proposed the idea of "human algorithm games", or games with a purpose (GWAPs), in order to harness human time and energy for addressing problems that computers cannot yet tackle on their own. He believes that human intellect is an important resource and contribution to the enhancement of computer processing and human computer interaction. He argues that games constitute a general mechanism for using brainpower to solve open computational problems. In this technique, human brains are compared to processors in a distributed system, each performing a small task of a massive computation. However, humans require an incentive to become part of a collective computation. Online games are used as a means to encourage participation in the process.
The tasks presented in these games are usually trivial for humans, but difficult for computers. These tasks include labeling images, transcribing ancient texts, common sense or human experience based activities, and more. Human-based computation games motivate people through entertainment rather than an interest in solving computation problems. This makes GWAPs more appealing to a larger audience. GWAPs can be used to help build the semantic web, annotate and classify collected data, crowdsource general knowledge, and improving other general computer processes. GWAPs have a vast range of applications in variety of areas such as security, computer vision, Internet accessibility, adult content filtering, and Internet search. In applications such as these, games with a purpose have lowered the cost of annotating data and increased the level of human participation.
The first human-based computation game or games with a purpose was created in 2004 by Luis von Ahn. The idea was that ESP would use human power to help label images. The game is a two player agreement game and relied on players to come up with labels for images and attempt to guess what labels a partner was coming up with. ESP used microtasks, simple tasks that can be solved quickly without the need of any credentials.
Games with a purpose categorized as output agreement games are microtask games where players are matched into pairs and randomly assigned partners attempt to match output with each other given a shared visible input. ESP is an example of an output agreement game.
Given an image, the ESP Game can be used to determine what objects are in the image, but cannot be used to determine the location of the object in the image. Location information is necessary for training and testing computer vision algorithms, so the data collected by the ESP Game is not sufficient. Thus, to deal with this problem, a new type of microtask game known as inversion problem games were introduced by creator of ESP, von Ahn in 2006. Peekaboom extended upon ESP and had players associate labels with a specific region of an image. In inversion problem games, two players are randomly paired together. One is assigned as the describer and the other is the guesser. The describer is given an input, which the guesser must reproduce given hints from the describer. In Peekaboom, for example, the describer slowly reveals small sections of an image until the guesser correctly guesses the label provided to the describer.
In input-agreement games two randomly paired players are each given an input that is hidden from the other player. Player inputs will either match or be different. The goal of these games is for players to tag their input such that the other player can determine whether or not the two inputs match. In 2008, Edith L. M. Law created the input-agreement game called TagATune. In this game, players label sound clips. In TagATune, players describe sound clips and guess if their partner's sound clip is the same as their own given their partner's tags.
Macrotask games, unlike microtask games, contain complex problems that are usually left to experts to solve. In 2008, a macrotask game called Foldit was created by Seth Cooper. The idea was that players would attempt to fold a three-dimensional representation of a protein. This task was a hard problem for computers to automate completely. Locating the biologically relevant native conformation of a protein is a difficult computational challenge given the very large size of the search space. By gamification and implementation of user friendly versions of algorithms, players are able to perform this complex task without much knowledge of biology.
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Human-based computation game AI simulator
(@Human-based computation game_simulator)
Human-based computation game
A human-based computation game or game with a purpose (GWAP) is a human-based computation technique of outsourcing steps within a computational process to humans in an entertaining way (gamification).
Luis von Ahn first proposed the idea of "human algorithm games", or games with a purpose (GWAPs), in order to harness human time and energy for addressing problems that computers cannot yet tackle on their own. He believes that human intellect is an important resource and contribution to the enhancement of computer processing and human computer interaction. He argues that games constitute a general mechanism for using brainpower to solve open computational problems. In this technique, human brains are compared to processors in a distributed system, each performing a small task of a massive computation. However, humans require an incentive to become part of a collective computation. Online games are used as a means to encourage participation in the process.
The tasks presented in these games are usually trivial for humans, but difficult for computers. These tasks include labeling images, transcribing ancient texts, common sense or human experience based activities, and more. Human-based computation games motivate people through entertainment rather than an interest in solving computation problems. This makes GWAPs more appealing to a larger audience. GWAPs can be used to help build the semantic web, annotate and classify collected data, crowdsource general knowledge, and improving other general computer processes. GWAPs have a vast range of applications in variety of areas such as security, computer vision, Internet accessibility, adult content filtering, and Internet search. In applications such as these, games with a purpose have lowered the cost of annotating data and increased the level of human participation.
The first human-based computation game or games with a purpose was created in 2004 by Luis von Ahn. The idea was that ESP would use human power to help label images. The game is a two player agreement game and relied on players to come up with labels for images and attempt to guess what labels a partner was coming up with. ESP used microtasks, simple tasks that can be solved quickly without the need of any credentials.
Games with a purpose categorized as output agreement games are microtask games where players are matched into pairs and randomly assigned partners attempt to match output with each other given a shared visible input. ESP is an example of an output agreement game.
Given an image, the ESP Game can be used to determine what objects are in the image, but cannot be used to determine the location of the object in the image. Location information is necessary for training and testing computer vision algorithms, so the data collected by the ESP Game is not sufficient. Thus, to deal with this problem, a new type of microtask game known as inversion problem games were introduced by creator of ESP, von Ahn in 2006. Peekaboom extended upon ESP and had players associate labels with a specific region of an image. In inversion problem games, two players are randomly paired together. One is assigned as the describer and the other is the guesser. The describer is given an input, which the guesser must reproduce given hints from the describer. In Peekaboom, for example, the describer slowly reveals small sections of an image until the guesser correctly guesses the label provided to the describer.
In input-agreement games two randomly paired players are each given an input that is hidden from the other player. Player inputs will either match or be different. The goal of these games is for players to tag their input such that the other player can determine whether or not the two inputs match. In 2008, Edith L. M. Law created the input-agreement game called TagATune. In this game, players label sound clips. In TagATune, players describe sound clips and guess if their partner's sound clip is the same as their own given their partner's tags.
Macrotask games, unlike microtask games, contain complex problems that are usually left to experts to solve. In 2008, a macrotask game called Foldit was created by Seth Cooper. The idea was that players would attempt to fold a three-dimensional representation of a protein. This task was a hard problem for computers to automate completely. Locating the biologically relevant native conformation of a protein is a difficult computational challenge given the very large size of the search space. By gamification and implementation of user friendly versions of algorithms, players are able to perform this complex task without much knowledge of biology.