Program Synthesis

2017 ◽  
Vol 4 (1-2) ◽  
pp. 1-119 ◽  
Author(s):  
Sumit Gulwani ◽  
Oleksandr Polozov ◽  
Rishabh Singh
Keyword(s):  
Program Synthesis

scholarly journals Accelerating search-based program synthesis using learned probabilistic models

2018 ◽  
Vol 53 (4) ◽  
pp. 436-449 ◽  
Author(s):  
Woosuk Lee ◽  
Kihong Heo ◽  
Rajeev Alur ◽  
Mayur Naik
Keyword(s):  
Probabilistic Models ◽  
Program Synthesis

Reconciling enumerative and deductive program synthesis

2020 ◽  
Author(s):  
Kangjing Huang ◽  
Xiaokang Qiu ◽  
Peiyuan Shen ◽  
Yanjun Wang
Keyword(s):  
Program Synthesis ◽  
Deductive Program Synthesis

FlashMeta: a framework for inductive program synthesis

2015 ◽  
Vol 50 (10) ◽  
pp. 107-126 ◽  
Author(s):  
Oleksandr Polozov ◽  
Sumit Gulwani
Keyword(s):  
Program Synthesis

Learning Network Design Objectives Using A Program Synthesis Approach

2019 ◽  
Author(s):  
Yanjun Wang ◽  
Chuan Jiang ◽  
Xiaokang Qiu ◽  
Sanjay G. Rao
Keyword(s):  
Network Design ◽  
Program Synthesis ◽  
Learning Network ◽  
Synthesis Approach

scholarly journals Combining the top-down propagation and bottom-up enumeration for inductive program synthesis

2021 ◽  
Vol 5 (POPL) ◽  
pp. 1-28
Author(s):  
Woosuk Lee
Keyword(s):  
Program Synthesis ◽  
Top Down ◽  
Bottom Up

Question selection for interactive program synthesis

2020 ◽  
Author(s):  
Ruyi Ji ◽  
Jingjing Liang ◽  
Yingfei Xiong ◽  
Lu Zhang ◽  
Zhenjiang Hu
Keyword(s):  
Program Synthesis ◽  
Interactive Program ◽  
Question Selection ◽  
Selection For

A Logically Justified Model of Computation I

1981 ◽  
Vol 4 (1) ◽  
pp. 151-172
Author(s):  
Pierangelo Miglioli ◽  
Mario Ornaghi
Keyword(s):  
Computer Science ◽  
Program Synthesis ◽  
Completeness Theorem ◽  
Point Of View ◽  
Program Execution ◽  
Computation Model ◽  
Clear Explanation ◽  
Direct Interpretation ◽  
Adequacy Condition ◽  
General Explanation

The aim of this paper is to provide a general explanation of the “algorithmic content” of proofs, according to a point of view adequate to computer science. Differently from the more usual attitude of program synthesis, where the “algorithmic content” is captured by translating proofs into standard algorithmic languages, here we propose a “direct” interpretation of “proofs as programs”. To do this, a clear explanation is needed of what is to be meant by “proof-execution”, a concept which must generalize the usual “program-execution”. In the first part of the paper we discuss the general conditions to be satisfied by the executions of proofs and consider, as a first example of proof-execution, Prawitz’s normalization. According to our analysis, simple normalization is not fully adequate to the goals of the theory of programs: so, in the second section we present an execution-procedure based on ideas more oriented to computer science than Prawitz’s. We provide a soundness theorem which states that our executions satisfy an appropriate adequacy condition, and discuss the sense according to which our “proof-algorithms” inherently involve parallelism and non determinism. The Properties of our computation model are analyzed and also a completeness theorem involving a notion of “uniform evaluation” of open formulas is stated. Finally, an “algorithmic completeness” theorem is given, which essentially states that every flow-chart program proved to be totally correct can be simulated by an appropriate “purely logical proof”.

scholarly journals Program synthesis from polymorphic refinement types

2016 ◽  
Vol 51 (6) ◽  
pp. 522-538 ◽  
Author(s):  
Nadia Polikarpova ◽  
Ivan Kuraj ◽  
Armando Solar-Lezama
Keyword(s):  
Program Synthesis ◽  
Refinement Types
Sign in / Sign up

Export Citation Format

Share Document