A QS-learning-Based Patrol Behavior Learning for Avoiding Illegal Disposals

In this paper, we present an overview of a QS-Learning-based patrol behavior learning approach for conserving the ecosystem of ponds from illegal releasers. The prototype learning mechanism can make users to generate a rule of patrolling of the ponds and simulate the actions. Based on our simulation-based analysis, we observed that we could increase the number of patrollers to handle the situation that the circumference of pond is enlarged.