功能性面向对象网络的构建与扩展
Functional Object-Oriented Network: Construction & Expansion
摘要：
我们建立在功能面向对象的网络（FOON），一个结构化的知识表示，这是从人类活动和操作的观察。FOON可用于表示对象运动示能表示。通过图搜索的知识检索使我们能够使用跨越许多视频源的知识来获得新颖的操作序列，因此我们的方法具有新颖性。但是，我们仅限于收集的来源。为了进一步提高知识检索的性能，作为我们以前工作的后续，我们讨论了将知识推广到对象，这些对象与我们在FOON中的对象类似，而无需手动注释新的知识来源。我们讨论了两种泛化方法：1）通过使用对象相似性来扩展我们的网络，从我们已经拥有的功能单元中创建新的功能单元，以及2）通过对象类别而不是特定对象来压缩功能单元。我们讨论的实验，比较我们的知识检索算法的性能与扩展和压缩的类别。
英文摘要：
We build upon the functional object-oriented network (FOON), a structured knowledge representation which is constructed from observations of human activities and manipulations. A FOON can be used for representing object-motion affordances. Knowledge retrieval through graph search allows us to obtain novel manipulation sequences using knowledge spanning across many video sources, hence the novelty in our approach. However, we are limited to the sources collected. To further improve the performance of knowledge retrieval as a follow up to our previous work, we discuss generalizing knowledge to be applied to objects which are similar to what we have in FOON without manually annotating new sources of knowledge. We discuss two means of generalization: 1) expanding our network through the use of object similarity to create new functional units from those we already have, and 2) compressing the functional units by object categories rather than specific objects. We discuss experiments which compare the performance of our knowledge retrieval algorithm with both expansion and compression by categories.
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。
