Planning, Monitoring and Replanning Techniques for Handling Abnormity in HTN-Based Planning and Execution

A framework that integrates planning, monitoring and replanning techniques is proposed. It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution. The framework consists of three parts: the hierarchical task network (HTN) planner based on Monte Carlo tree search (MCTS), hybrid plan monitoring based on forward and backward and norm-based replanning method selection. The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration. Based on specific objectives, it can identify the best solution to the current problem. The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action, thus trigger the replanning. The norm-based replanning selection method can measure the difference between the expected state and the actual state, and then select the best replanning algorithm. The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.