Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

Abstract

Part 1: The International Cross Domain Conference (CD-ARES 2016)International audienceMost Machine Learning (ML) researchers focus on automatic Machine Learning (aML) where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from the availability of “big data”. However, sometimes, for example in health informatics, we are confronted not a small number of data sets or rare events, and with complex problems where aML-approaches fail or deliver unsatisfactory results. Here, interactive Machine Learning (iML) may be of help and the “human-in-the-loop” approach may be beneficial in solving computationally hard problems, where human expertise can help to reduce an exponential search space through heuristics.In this paper, experiments are discussed which help to evaluate the effectiveness of the iML-“human-in-the-loop” approach, particularly in opening the “black box”, thereby enabling a human to directly and indirectly manipulating and interacting with an algorithm. For this purpose, we selected the Ant Colony Optimization (ACO) framework, and use it on the Traveling Salesman Problem (TSP) which is of high importance in solving many practical problems in health informatics, e.g. in the study of proteins

Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning

“…Holzinger also shows that the solution of complex problems is possible by using ciML. He presents the integration of users into an ant colony algorithm to solve a traveling salesman problem (TSP) [32]. A visualization shows the pheromone tracks of the ants in the TSP and the optimal round-trip found by the algorithm so far.…”

“…Users can select edges and add or remove the current amount of pheromones on the edge between each of the iterations. First experiments show that the process is sped up in terms of required iterations to find the optimal solution [32]. The collaborative variant of interactive machine learning is shown in Fig.…”

“…Moreover, research in this area, i.e. at the intersection of cognitive science and computational science is fruitful for further improving ML thus improve performance on a wide range of tasks, including settings which are difficult for humans to process (e.g., big data and high dimensional problems) [32]. According to Lee and Holzinger [73], there is a very common misconception about high dimensionality, i.e.…”

Machine Learning for Health Informatics

“…interactive Machine Learning (iML) in bringing the human-in-the-loop is necessary if we have small amounts of data ("little data"), rare events or deal with complex problems [22,23].…”

Explainable AI (ex-AI)

“…Ante-hoc-Methoden sind systemimmanent interpretierbar, also von Natur aus transparent (Glass-Box), ähnlich wie beim ,,interactive Machine Learning" (iML)-Modell [5]. Viele Ante-hoc-Ansätze erscheinen besonders neuartig, aber gerade diese Ansätze haben eine lange Tradition und wurden in Expertensystemen seit Beginn der AI eingesetzt, insbesondere Entscheidungsbäume, lineare Regression und Random Forests, um drei zu nennen.…”