强化学习和概率推理算法旨在分别从互动体验和概率语境知识中学习推理。在本研究中，我们开发了机器人任务完成算法，同时研究了强化学习和概率推理技术的辅助优势。机器人从试错经验中学习，以增强其声明性知识库，增强知识可用于加快潜在多重任务的学习过程。我们已经实施并评估了使用移动机器人执行对话和导航任务的算法。从结果中，我们看到，通过运用人类知识推理和学习已完成任务的经验，可以提高机器人的性能。更有趣的是，机器人能够从导航任务中学习，以改善其对话策略。
原文标题：Learning and Reasoning for Robot Dialog and Navigation Tasks
原文：Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In this research, we develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base, and the augmented knowledge can be used for speeding up the learning process in potentially different tasks. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks. From the results, we see that our robot's performance can be improved by both reasoning with human knowledge and learning from task-completion experience. More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.
原文作者：Keting Lu, Shiqi Zhang, Peter Stone, Xiaoping Chen
原文地址：http://arxiv.org/abs/2005.09833
