Designing games with a purpose

Abstract

MANy TASKS ARE trivial for humans but continue to challenge even the most sophisticated computer programs. Traditional computational approaches to solving such problems focus on improving artificialintelligence algorithms. Here, we advocate a different approach: the constructive channeling of human brainpower through computer games. Toward this goal, we present general design principles for the development and evaluation of a class of games we call "games with a purpose," or GWAPs, in which people, as a side effect of playing, perform tasks computers are unable to perform.The Entertainment Software Association (www. theesa.com/facts/gamer_data.php) has reported that more than 200 million hours are spent each day playing computer and video games in the U.S. Indeed, by age 21, the average American has spent more than 10,000 hours playing such games 15 -equivalent to five years of working a full-time job 40 hours per week.

Linking Smart Cities Datasets with Human Computation – The Case of UrbanMatch

“…As noted in [31], this strategy can guarantee the correct assessment of link quality with arbitrarily high probability.…”

“…Having created many GWAPs (e.g., ESP Game, Peekaboom, Phetch, and Verbosity), Luis Von Ahn and Laura Dabbish reports in [31] on three game-structure templates that generalize successful instances of Human Computation games: input-agreement games, inversion-problem games, and output-agreement games. In input-agreement games, players must determine whether they have been given the same input; in inversion-problem games, given an input, a player produces an output, and another player guesses the input; in output-agreement games, players are given the same input and must agree on an appropriate output.…”

“…The metrics [31] proposed to evaluate GWAPS include throughput and average lifetime play (ALP). The throughput of a GWAP is defined as the average number of problem instances solved per human hour.…”

“…We can certainly trust a large majority of the players to play earnestly, but we need to consider that some players can cheat or misunderstand the task, thus giving wrong answers. As proposed in [31], we can mitigate the risk of trusting erroneous inputs with two strategies: i) repeating the same task multiple times to randomly picked users, and ii) testing the player reliability.…”

Rebooting science? Implications of science 2.0 main trends for scientific method and research institutions

“…Also, management of large collaboration groups generates higher transaction costs. Finally, the push for engagement of citizen scientists may result in further gamification of science (von Ahn, L., & Dabbish, L. 2008) and distort for the wider public the research objectives.…”