Novelty-organizing team of classifiers in noisy and dynamic environments
This addresses the problem of robust classification in changing, noisy environments for AI systems, but it is incremental as it builds on existing methods like NEAT.
The paper tackled classification in noisy and dynamic environments by applying the Novelty-Organizing Team of Classifiers (NOTC) to mountain car problems, achieving the best performance compared to NEAT, with NOTC requiring more trials to converge.
In the real world, the environment is constantly changing with the input variables under the effect of noise. However, few algorithms were shown to be able to work under those circumstances. Here, Novelty-Organizing Team of Classifiers (NOTC) is applied to the continuous action mountain car as well as two variations of it: a noisy mountain car and an unstable weather mountain car. These problems take respectively noise and change of problem dynamics into account. Moreover, NOTC is compared with NeuroEvolution of Augmenting Topologies (NEAT) in these problems, revealing a trade-off between the approaches. While NOTC achieves the best performance in all of the problems, NEAT needs less trials to converge. It is demonstrated that NOTC achieves better performance because of its division of the input space (creating easier problems). Unfortunately, this division of input space also requires a bit of time to bootstrap.