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 , .” A “human-in-the-loop” can be beneficial in solving computationally hard problems . 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) ,  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.
 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.