Predictive Process Monitoring aims to predict the future information of ongoing process executions by leveraging machine and deep learning techniques. One of the tasks is known as remaining time prediction , which focuses on predicting the remaining time of ongoing cases. Accurate remaining time prediction can be valuable and important for improving business operations or taking timely interventions to prevent delays. For predicting the remaining time, existing work has used deep learning techniques to achieve high prediction accuracy. However, most of these techniques tend to learn very complex models that are difficult to explain. Systematic feature selection approaches may help improve both the prediction accuracy and the explainability of the model. In this paper, we introduce a feature-informed cascade prediction framework to predict the remaining time. Specifically, we first propose an approach that builds a tree of features by systematically estimating their effects on the remaining time prediction. Next, we use the tree to either automatically select an optimal combination of features or to guide users in this selection process. Each selected feature is correlated with its prediction results in our Feature-informed Cascade Prediction Model (FCPM) for explainability. The proposed approach has been implemented and is made publicly available. Using eight public real-life event logs, the proposed approach is compared to the state-of-the-art approaches in terms of prediction accuracy. In addition, it is demonstrated that our approach visualizes the impact of each input feature in the prediction of individual cases, producing explanations of the prediction results.

Path planning is a difficult part of the navigation task for the mobile robot under dynamic and unknown environment. It needs to solve a mapping relationship between the sensing space and the action space. The relationship can be achieved through different ways. But it is difficult to be expressed by an accurate equation. This paper uses multi-layer feedforward artificial neural network (ANN) to construct a path-planning controller by its powerful nonlinear functional approximation. Then the path planning task is simplified to a classified problem which are five state-action mapping relationship. One reinforcement learning method, Q-learning, is used to collect training samples for the ANN controller. At last the trained controller runs in the simulation environment and retrains itself furthermore combining the reinforcement signal during the interaction with the environment. Strategy based on the Combination of ANN and Q-learning is better than using only one of the two methods. The simulation result also shows that the strategy can find the optimal path than using Q-learning only.

In order to solve the local path planning problem of mobile robot in the unknown environment, more complex control algorithms and solutions are needed for

This paper proposes a fusion iterations strategy based on the Standard map to generate a chaotic path planner of the mobile robot for surveillance missions. The distances of the chaotic trajectories between the adjacent iteration points which are produced by the Standard map are too large for the robot to track. So a fusion iterations strategy combined with the large region iterations and the small grids region iterations is designed to resolve the problem. The small region iterations perform the iterations of the Standard map in the divided small grids, respectively. It can reduce the adjacent distances by dividing the whole surveillance workspace into small grids. The large region iterations combine all the small grids region iterations into a whole, switch automatically among the small grids, and maintain the chaotic characteristics of the robot to guarantee the surveillance missions. Compared to simply using the Standard map in the whole workspace, the proposed strategy can decrease the adjacent distances according to the divided size of the small grids and is convenient for the robot to track.