Abstract
Recent advances in automatic machine learning (aML) allow solving problems without any human intervention. However, sometimes a human-in-the-loop can be beneficial in solving computationally hard problems. In this paper we provide new experimental insights on how we can improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML). For this purpose, we used the Ant Colony Optimization (ACO) framework, because this fosters multi-agent approaches with human agents in the loop. We propose unification between the human intelligence and interaction skills and the computational power of an artificial system. The ACO framework is used on a case study solving the Traveling Salesman Problem, because of its many practical implications, e.g. in the medical domain. We used ACO due to the fact that it is one of the best algorithms used in many applied intelligence problems. For the evaluation we used gamification, i.e. we implemented a snake-like game called Traveling Snakesman with the MAX–MIN Ant System (MMAS) in the background. We extended the MMAS–Algorithm in a way, that the human can directly interact and influence the ants. This is done by “traveling” with the snake across the graph. Each time the human travels over an ant, the current pheromone value of the edge is multiplied by 5. This manipulation has an impact on the ant’s behavior (the probability that this edge is taken by the ant increases). The results show that the humans performing one tour through the graphs have a significant impact on the shortest path found by the MMAS. Consequently, our experiment demonstrates that in our case human intelligence can positively influence machine intelligence. To the best of our knowledge this is the first study of this kind.
Highlights
1.1 Automatic machine learningOne of the fundamental objectives of Artificial Intelligence (AI) in general and of Machine Learning (ML) in particular is to find methods and develop algorithms and tools that automatically learn from data, and based on them, provide results without human interaction
Scientific examples include the automatic ML (aML) approaches based on Gaussian processes, which are weak on function extrapolation problems, these problems are quite simple for humans [36]
In a first step we multiplied the pheromone-values by 2 but, this had no significant impact on the ants, so we tried several other values and we came to the conclusion, that multiplying the value by 5 will increase the performance significantly, a larger value decreases the performance of the algorithm again, because a mistake of the human has major impact on the ants
Summary
One of the fundamental objectives of Artificial Intelligence (AI) in general and of Machine Learning (ML) in particular is to find methods and develop algorithms and tools that automatically learn from data, and based on them, provide results without human interaction. Such algorithms can be called automatic ML (aML) - where automatic means autonomous in the sense of classical AI [1]. This paper explores some catalytic and synergetic effects of the integration of human expertise into the data processing pipeline as in standard supervised learning, but directly into the algorithm [11]
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