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基于Agent的智能机器人的自主性和自适应性研究
罗彤
学位类型工学硕士
导师谭民
2000-06-01
学位授予单位中国科学院自动化研究所
学位授予地点中国科学院自动化研究所
学位专业控制理论与控制工程
摘要本文指出建造智能机器人的目标是建造一个具有高度自主性、适应性和 学习性的智能Agent.自主性、适应性和学习性是机器人的重要性能指标. 传统的人工智能在构造机器人时,人为地对世界建模,用符号来表示世 界,然后机器人通过对符号的规划和推理来完成任务.但是,由于人为对世 界建模的复杂性和费时性,传统的智能机器人几乎无法在实际的动态环境中 正常运行. 新一代的人工智能学者Brooks认为智能体现在机器人与外界环境的交 互能力上.他认为机器人没有必要对世界建立符号模型,机器人只要通过感 知—反应这个没有世界模型和推理的过程就能够很好地完成任务.Brooks 的机器人能够在动态环境中快速地行进,但是无法完成复杂的任务. 不同于学者们以前的认识,本文认为世界是机器人感知的世界.机器人 可以通过自己的传感器和执行器来认识世界,并且可以根据自己的认知把世 界表示为符号.与以往不同的是,这个符号世界不是人为建立的,而是机器 人自己建立的.这样,机器人就可以利用这些符号(称之为机器人语言)来 进行规划和推理,实现较高程度的智能. 本文提出了构造智能机器人的步骤:机器人通过感知和行为与外界环境 的交互作用来对传感信号分类、机器人学习特定情形下的正确行为和学习机 器人语言的逻辑推理。 因为学习能够提高机器人的自主性和适应性,本文应用了两种学习方法 到机器人学习中,并通过仿真机器人实验来验证学习算法.仿真机器人通过 增强式学习学会了在特定感觉下正确的行为:躲避障碍物和向目标前进.在 行为参数发生变化时,机器人很快地学会了新情况下的正确行为,体现了很 好的适应性.同时,仿真机器人通过联想学习也学会了简单的机器人语言逻 辑和良好的习惯. 仿真实验通过Agent与主程序的交互作用来模拟机器人与环境之间的关 系.实验证明,本文采用的学习算法能够使机器人学会正确的行为和语言逻 辑.学习后的机器人具有更高的自主性和适应性.
其他摘要This thesis points out that the goal of building the intelligent robot is to construct an intelligent agent, who has high degree of autonomy, situatedness and learning ability. Autonomy, situatedness and learning ability are essential measurements of intelligent robots. When constructing the intelligent robot, traditional Artificial Intelligence (AI~ manually builds the world model and represents the world with symbols. Then the robot could achieve his goals by planning and reasoning with the symbols. However, the traditional intelligent robot could hardly work well in real dynamic environment, because of the complexity and time consuming of building world model. As a researcher in the new generation of AI scientists, Brooks believes that the intelligence could be achieved through the interaction between the robot and the environment. He thinks it unnecessary to establish the symbol model of the world. He argues that the robot just responses to the situation, which has no explicit world model and reasoning procedure, and could complete his task perfectly. Brooks' s machine quickly navigates in the dynamic environment, although it can not achieve complex goals. Different from the opinions that researchers held, the thesis argue that the world is the one, which was sensed by robots. Robots can acquire the knowledge of the world with the sensors and the actuators and use symbols to represent the knowledge. In contrast to traditional AI, the symbol world is not built by people, but by robots themselves. Therefore, robots could use the symbols, which are called robot language, to plan, reason and achieve high-level intelligence. The thesis describes the steps of building intelligent robots: First, the robot need to classify the signals of the sensor; Then the robot manages to learn the proper reactions in particular situation and the logical reasoning of robot language. Because robot learning could improve the degree of robots' autonomy and situatedness, the thesis introduces two learning strategies into robot learning and tests the algorithms in a simulated robot. Using reinforcement learning, the simulated robot manages to learn the correct responses in specific situations: avoiding obstacles and marching toward the goal. Moreover, the robot can quickly master new actions when the parameters of his actuators vary. The robot adopts the associative learning methodology to acquire simple logic relations in robot language and shape a healthy habit. The simulation experiment uses the interaction between the agent and the application to simulate the interaction between the robot and his environment. The analysis and the result in the experiment validate the learning strategies. After learning, the robot gains more autonomy and situatedness.
馆藏号XWLW586
其他标识符586
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7320
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
罗彤. 基于Agent的智能机器人的自主性和自适应性研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2000.
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