2026-09-18T19:51:31Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/531172022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Patrick Henry Winston.
Saliba, Gaylee (Gaylee Fouad)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-03-25T15:03:18Z
2010-03-25T15:03:18Z
2009
2009
http://hdl.handle.net/1721.1/53117
503138976
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (leaf 74).
If we are to successfully create intelligent machines, it is essential to learn how to ground abstract notions, such as possession, in the physical world. In this work, I develop a model for the knowledge about possession transfer, which ties the abstract world to the physical world. The model grounds itself in spatial and time understanding, by making use of Borchardt's work on time space representations. The model identifies a list of 11 prominent possession transfer verbs and establishes a hierarchy to classify the other pertinent verbs. It also defines 6 dimensions for the possession space spanning physical possession, mental state, desire, IOU, money, and moving party. 19 TSR learning templates are developed as the representation for all the cases of all the prominent possession transfer verbs. The salient features of the verbs and their representations are identified. With these salient features, a decision-making tree is created. Near-miss learning is demonstrated to be a good learning technique for the system via 2 descriptive examples. I address the 10 questions and answers that the system can answer with my representation. In addition, 5 questions are addressed which cannot be answered. The correlation between the representation and visual events is discussed and explained with an example, proving how my representation can serve to aid a visual system in understanding the visual events it perceives in the environment.
by Gaylee Saliba.
M.Eng.
74 leaves
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Modeling knowledge about possession transfer
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="345960bf-d140-4309-9adc-29aa6e0d6911">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Modeling knowledge about possession transfer</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2009</PublicationDate>
<Authors>
<Author>
<DisplayName>Saliba, Gaylee (Gaylee Fouad)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>If we are to successfully create intelligent machines, it is essential to learn how to ground abstract notions, such as possession, in the physical world. In this work, I develop a model for the knowledge about possession transfer, which ties the abstract world to the physical world. The model grounds itself in spatial and time understanding, by making use of Borchardt's work on time space representations. The model identifies a list of 11 prominent possession transfer verbs and establishes a hierarchy to classify the other pertinent verbs. It also defines 6 dimensions for the possession space spanning physical possession, mental state, desire, IOU, money, and moving party. 19 TSR learning templates are developed as the representation for all the cases of all the prominent possession transfer verbs. The salient features of the verbs and their representations are identified. With these salient features, a decision-making tree is created. Near-miss learning is demonstrated to be a good learning technique for the system via 2 descriptive examples. I address the 10 questions and answers that the system can answer with my representation. In addition, 5 questions are addressed which cannot be answered. The correlation between the representation and visual events is discussed and explained with an example, proving how my representation can serve to aid a visual system in understanding the visual events it perceives in the environment.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>