2026-09-18T22:54:06Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1431722022-06-16T03:49:51Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tenenbaum, Joshua B.
Sechopoulos, Theodoros
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-06-15T13:01:16Z
2022-06-15T13:01:16Z
2022-02
2022-02-22T18:32:01.826Z
https://hdl.handle.net/1721.1/143172
Program synthesis is the task of automatically writing computer programs given a specification for their behavior. Program synthesis is challenging due to the combinatorial nature of the search space. In the short term, improving program synthesis could make people vastly more productive, by transforming how they communicate with computers. In the long term, improving program synthesis could bring us a step closer to understanding human intelligence and to building machines with human-like intelligence. In this work we discuss how symbolic properties (which are themselves programs) can help program synthesis performance. Specifically, building on the formulation of properties in Odena and Sutton (2020) we present PropsimFit, a novel online synthesis algorithm that uses properties for program search and show that it outperforms naive non-property baselines in the Rule (2020) list function dataset. Finally, we discuss future ways to use properties for synthesis based on the insights gained from PropsimFit and its limitations.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Program Synthesis with Symbolic Properties
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="27ea1e75-8fc1-458d-bcd1-ccbc462d0063">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Program Synthesis with Symbolic Properties</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2022-02</PublicationDate>
<Authors>
<Author>
<DisplayName>Sechopoulos, Theodoros</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>Program synthesis is the task of automatically writing computer programs given a specification for their behavior. Program synthesis is challenging due to the combinatorial nature of the search space. In the short term, improving program synthesis could make people vastly more productive, by transforming how they communicate with computers. In the long term, improving program synthesis could bring us a step closer to understanding human intelligence and to building machines with human-like intelligence. In this work we discuss how symbolic properties (which are themselves programs) can help program synthesis performance. Specifically, building on the formulation of properties in Odena and Sutton (2020) we present PropsimFit, a novel online synthesis algorithm that uses properties for program search and show that it outperforms naive non-property baselines in the Rule (2020) list function dataset. Finally, we discuss future ways to use properties for synthesis based on the insights gained from PropsimFit and its limitations.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>