Welcome! Enjoy playing the games 🙂

Game 1: The Travelling Snakesman:


Game 2: ProjectANT


Background: Interactive Machine Learning (iML) can be defined as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human [1], [2].” A “human-in-the-loop” can be beneficial in solving computationally hard problems [3]. This is due to the fact that humans have excellent problem solving abilities, intuition and instantaneous gut-feeling, which can be helpful to reduce a huge search space through heuristic selection of samples. Consequently, a human in the loop can be help to contribute to solve NP-hard problems – at least in the lower dimensions.

Experiment: In this two on-line games we evaluate the effectiveness of the iML-”human-in-the-loop” approach by enabling a human to directly manipulating and interacting with an algorithm. For this purpose, we selected the Ant Colony Optimization (ACO) framework, and apply it on the Traveling Salesman Problem (TSP) [4], [5] which is an NP-hard problem and is of highest importance in solving many practical problems in health informatics, e.g. in the study of proteins etc.


[1] Holzinger, A. 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Springer Brain Informatics (BRIN), 3, (2), 119-131, doi:10.1007/s40708-016-0042-6.

[2] Holzinger, A. 2016. Interactive Machine Learning (iML). Informatik Spektrum, 39, (1), 64-68, doi:10.1007/s00287-015-0941-6.

[3] Dossier: Interactive Machine Learning for Health Informatics

[4] Wikipedia: Travelling salesman problem (last visited: 01.08.2016, 18:00 CET)

[5] Google Scholar: Traveling salesman problem (last visited: 01.08.2016, 18:05 CET – 46,800 results)