你能用石头做什么？通过词嵌入的示能表示提取
What can you do with a rock? Affordance extraction via word embeddings
摘要：
自主智能体必须经常检测示能性：由某种情况促成的一系列行为。示能检测在具有大动作空间的域中特别有用，允许代理通过避免无用的行为来修剪其搜索空间。本文提出了一种通过在维基百科语料库上训练的词嵌入来提取示能表示的方法。由此产生的词向量被视为一个共同的知识库，可以使用线性代数查询。我们将这种方法应用于纯文本环境中的强化学习代理，并表明基于负担能力的动作选择在大多数情况下都能提高性能。我们的方法增加了每个学习步骤的计算复杂性，但显着减少了所需的步骤总数。此外，智能体的动作选择开始类似于人类的选择。
英文摘要：
Autonomous agents must often detect affordances: the set of behaviors enabled by a situation. Affordance detection is particularly helpful in domains with large action spaces, allowing the agent to prune its search space by avoiding futile behaviors. This paper presents a method for affordance extraction via word embeddings trained on a Wikipedia corpus. The resulting word vectors are treated as a common knowledge database which can be queried using linear algebra. We apply this method to a reinforcement learning agent in a text-only environment and show that affordance-based action selection improves performance most of the time. Our method increases the computational complexity of each learning step but significantly reduces the total number of steps needed. In addition, the agent's action selections begin to resemble those a human would choose.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
