This paper presents an approach to solve the variant of the well-known Travelling Salesman Problem (TSP) by using a gamesourcing approach. In contemporary literature is TSP solved by wide spectra of modern as well as classical computational methods. We would like to point out the possibility to solve such problems by computer game plying that is called a gamesourcing. Gamesourcing can be understood as a version of game-driven crowdsourcing. The main part and contribution of this paper is a demonstration of gamesourcing use in the game called Labyrinth that reflects TSP structure. The game has a form of a maze-labyrinth that enables players to move through it like the Ant Colony Optimization. The playing of the Labyrinth, thus, by playing, solves the problem. The performance of the “human ant-like system” is then evaluated and compared against some well-known versions of ACO. As we believe, our experiments suggest that this approach can serve as an alternative way that employs gamesourcing to assist a combinatorial optimizer in achieving better results on a well-known NP-hard optimization problem.

本文提出了一种通过使用游戏外包方法解决著名的旅行推销员问题（TSP）的方法。在当代文学中，TSP可以通过现代以及经典计算方法的广泛应用来解决。我们想指出有可能通过称为游戏外包的计算机游戏来解决此类问题。游戏外包可以理解为游戏驱动的众包的一种版本。本文的主要部分和贡献是对名为Labyrinth的游戏中游戏外包使用的演示，该游戏反映了TSP的结构。游戏具有迷宫迷宫的形式，使玩家可以像蚁群优化一样在迷宫中移动。因此，通过玩迷宫游戏解决了这个问题。然后评估“类人蚂蚁系统”的性能，并与一些著名的ACO版本进行比较。我们相信，我们的实验表明，这种方法可以作为一种替代方法，利用游戏外包来协助组合优化器解决众所周知的NP-hard优化问题上的更好结果。