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通过学习阅读的行为
Learning to Behave by Reading |
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| 课程网址: |
http://videolectures.net/aaai2012_barzilay_learning/
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| 主讲教师: |
Regina Barzilay
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| 开课单位: |
麻省理工学院
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| 开课时间: |
2012-08-17
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| 课程语种: |
英语
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| 中文简介: |
在本次讲座中, 我将讨论在控制应用中的接地语言分析问题, 如游戏玩法和机器人导航。我们假设访问描述控制算法所需行为的自然语言文档 (如游戏策略指南)。我们的目标是证明从此类文档中自动提取的知识可以显著提高目标应用程序的性能。首先, 我将提出一个强化学习算法, 用于学习将自然语言指令映射到可执行的操作。此技术实现了到目前为止需要人员参与的任务的自动化 — 例如, 通过咨询操作指南自动配置软件。接下来, 我将介绍一个用于游戏玩的蒙特卡洛搜索算法, 该算法包含了来自游戏策略指南的信息。在此框架中, 文本解释任务被表述为基于蒙特卡罗搜索反馈进行训练的概率模型。当应用于文明战略游戏时, 语言授权的玩家的表现大大优于传统玩家。
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| 课程简介: |
In this talk, I will address the problem of grounding linguistic analysis in control applications, such as game playing and robot navigation. We assume access to natural language documents that describe the desired behavior of a control algorithm (such as game strategy guides). Our goal is to demonstrate that knowledge automatically extracted from such documents can dramatically improve performance of the target application. First, I will present a reinforcement learning algorithm for learning to map natural language instructions to executable actions. This technique has enabled automation of tasks that until now have required human participation — for example, automatically configuring software by consulting how-to guides. Next, I will present a Monte-Carlo search algorithm for game playing that incorporates information from game strategy guides. In this framework, the task of text interpretation is formulated as a probabilistic model that is trained based on feedback from Monte-Carlo search. When applied to the Civilization strategy game, a language-empowered player outperforms its traditional counterpart by a significant margin.
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| 关 键 词: |
计算机科学; 人工智能; 自然语言处理
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-14：liush
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| 阅读次数: |
42
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图 书 馆