Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network （HTN） pla...Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network （HTN） planning process so as to create better high- quality plans quickly. The process of HTN planning is mapped during a depth-first search process in a problem-solving agent, and the models of learning in HTN planning is conducted similar to the learning depth-first search （LDFS）. Based on the models, a learning method integrating HTN planning and LDFS is presented, and a fatigue mechanism is introduced to balance exploration and exploitation in learning. Finally, experiments in two classical do- mains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method.展开更多
As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle dr...As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers＇ fatigue driving by using the human eye tracking technology.展开更多
文摘Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network （HTN） planning process so as to create better high- quality plans quickly. The process of HTN planning is mapped during a depth-first search process in a problem-solving agent, and the models of learning in HTN planning is conducted similar to the learning depth-first search （LDFS）. Based on the models, a learning method integrating HTN planning and LDFS is presented, and a fatigue mechanism is introduced to balance exploration and exploitation in learning. Finally, experiments in two classical do- mains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method.
基金supported by the National Natural Science Foundation of China(61272357,61300074,61572075)
文摘As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers＇ fatigue driving by using the human eye tracking technology.
