Canadian Traveler Problem with Neutralizations
作者：
Highlights：
• We introduce the Canadian Traveler Problem with Neutralizations to the literature.
• We design a solution model based on Markov Decision Processes (MDP) and AO trees.
• We introduce CAON*, a novel algorithm that solves CTPN optimally.
• We show how our exact algorithm outperforms other state-of-the-art algorithms.
• We show that neutralization capability is better than disambiguation capability.
摘要
•We introduce the Canadian Traveler Problem with Neutralizations to the literature.•We design a solution model based on Markov Decision Processes (MDP) and AO trees.•We introduce CAON*, a novel algorithm that solves CTPN optimally.•We show how our exact algorithm outperforms other state-of-the-art algorithms.•We show that neutralization capability is better than disambiguation capability.
论文关键词：Autonomous navigation,Path planning,Canadian traveler problem,Markov decision process,AO* search,Reinforcement learning
论文评审过程：Received 28 July 2018, Revised 30 April 2019, Accepted 1 May 2019, Available online 2 May 2019, Version of Record 10 May 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.001
