2026-09-20T00:25:08Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/180312022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Patrick H. Winston.
Shahdadi, Arian, 1980-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-06-02T19:42:09Z
2005-06-02T19:42:09Z
2003
2003
http://hdl.handle.net/1721.1/18031
57241319
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (leaf 101).
How can a computational system understand decisions in the domain of politics? In order to build computational systems that understand decisions in the abstract political space, we must first understand human decision-making and how human beings, in turn, are able to understand ideas in abstract realms such as politics. The work of Jintae Lee attempted to address the problem of understanding decision rationales in a non-domain specific way. His work falls short, however, when applied to decision problems in politics. I present a new representation, the Augmented Decision Rationale Language (ADRL) that attempts to address the shortcomings of Lee's work in this regard. ADRL expands Lee's vocabulary of relations to include forms of causation such as enablement and gating. ADRL also refines the relations and primitives of Lee's representation and focuses primarily on States, Actions and Goals as the basic units of decisions. Finally, ADRL grounds itself in spatial understanding using the Lexical Conceptual Semantics of Jackendoff, in contrast to the DRL, which ignores the text associated with a decision rationale. An implementation of a subset of this representation is displayed, along with a matcher that is able to create analogies between two decision scenarios cast in the representation. The matcher is presented an existence proof that this representation can be used to readily structure decision scenarios and make analogies between them.
by Arian Shahdadi.
M.Eng.
101 leaves
4961843 bytes
4973671 bytes
application/pdf
application/pdf
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.
Barnyard politics : a decision rationale representation for the analysis of simple political situations
Thesis
application/pdf
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="38d974e9-b022-4328-8146-f907afd3ea7f">
<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>Barnyard politics : a decision rationale representation for the analysis of simple political situations</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2003</PublicationDate>
<Authors>
<Author>
<DisplayName>Shahdadi, Arian, 1980-</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>How can a computational system understand decisions in the domain of politics? In order to build computational systems that understand decisions in the abstract political space, we must first understand human decision-making and how human beings, in turn, are able to understand ideas in abstract realms such as politics. The work of Jintae Lee attempted to address the problem of understanding decision rationales in a non-domain specific way. His work falls short, however, when applied to decision problems in politics. I present a new representation, the Augmented Decision Rationale Language (ADRL) that attempts to address the shortcomings of Lee's work in this regard. ADRL expands Lee's vocabulary of relations to include forms of causation such as enablement and gating. ADRL also refines the relations and primitives of Lee's representation and focuses primarily on States, Actions and Goals as the basic units of decisions. Finally, ADRL grounds itself in spatial understanding using the Lexical Conceptual Semantics of Jackendoff, in contrast to the DRL, which ignores the text associated with a decision rationale. An implementation of a subset of this representation is displayed, along with a matcher that is able to create analogies between two decision scenarios cast in the representation. The matcher is presented an existence proof that this representation can be used to readily structure decision scenarios and make analogies between them.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>