Program sketching

Sketching is a synthesis methodology that aims to bridge the gap between a programmer’s high-level insights about a problem and the computer’s ability to manage low-level details. In sketching, the programmer uses a partial program, a sketch, to describe the desired implementation strategy, and leaves the low-level details of the implementation to an automated synthesis procedure. In order to generate an implementation from the programmer provided sketch, the synthesizer uses counterexample-guided inductive synthesis (CEGIS). Inductive synthesis refers to the process of generating candidate implementations from concrete examples of correct or incorrect behavior. CEGIS combines a SAT-based inductive synthesizer with an automated validation procedure, a bounded model-checker, that checks whether the candidate implementation produced by inductive synthesis is indeed correct and to produce new counterexamples. The result is a synthesis procedure that is able to handle complex problems from a variety of domains including ciphers, scientific programs, and even concurrent data-structures

Verifiable Reinforcement Learning via Policy Extraction

While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies. We propose an approach to verifiable reinforcement learning by training decision tree policies, which can represent complex policies (since they are nonparametric), yet can be efficiently verified using existing techniques (since they are highly structured). The challenge is that decision tree policies are difficult to train. We propose VIPER, an algorithm that combines ideas from model compression and imitation learning to learn decision tree policies guided by a DNN policy (called the oracle) and its Q-function, and show that it substantially outperforms two baselines. We use VIPER to (i) learn a provably robust decision tree policy for a variant of Atari Pong with a symbolic state space, (ii) learn a decision tree policy for a toy game based on Pong that provably never loses, and (iii) learn a provably stable decision tree policy for cart-pole. In each case, the decision tree policy achieves performance equal to that of the original DNN policy

SPT: Storyboard Programming Tool

We present Spt, a tool that helps programmers write low-level data-structure manipulations by combining various forms of insights such as abstract and concrete input-output examples as well as implementation skeletons. When programmers write such manipulations, they typically have a clear high-level intuition about how the manipulation should work, but implementing efficient low-level pointer manipulating code is error-prone. Our tool aims to bridge the gap between the intuition and the corresponding implementation by automatically synthesizing the implementation. The tool frames the synthesis problem as a generalization of an abstract-interpretation based shape analysis, and represents the problem as a set of constraints which are solved efficiently by the Sketch solver. We report the successful evaluation of our tool on synthesizing several linked list and binary search tree manipulations.National Science Foundation (U.S.) (Grant CCF-1116362

Synthesizing data-structure manipulations from storyboards

We present the Storyboard Programming framework, a new synthesis system designed to help programmers write imperative low-level data-structure manipulations. The goal of this system is to bridge the gap between the "boxes-and-arrows" diagrams that programmers often use to think about data-structure manipulation algorithms and the low-level imperative code that implements them. The system takes as input a set of partial input-output examples, as well as a description of the high-level structure of the desired solution. From this information, it is able to synthesize low-level imperative implementations in a matter of minutes. The framework is based on a new approach for combining constraint-based synthesis and abstract-interpretation-based shape analysis. The approach works by encoding both the synthesis and the abstract interpretation problem as a constraint satisfaction problem whose solution defines the desired low-level implementation. We have used the framework to synthesize several data-structure manipulations involving linked lists and binary search trees, as well as an insertion operation into an And Inverter Graph.National Science Foundation (U.S.). (Grant number CCF-1049406)Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laborator

Leveraging Sequential Computation for Programming Efficient and Reliable Distributed Systems

While sequential programs represent a simple and natural form for expressing functionality, corresponding distributed implementations get considerably more complex. We examine the possibility of using the sequential computation model for programming distributed systems and requirements for making that possible. The benefits of such an approach include easier specification and reasoning about behaviors in the system, as well as a possibility to directly reuse existing techniques for checking correctness and optimization of sequential programs to produce efficient and reliable distributed implementations

Euler: A System for Numerical Optimization of Programs

We give a tutorial introduction to Euler, a system for solving difficult optimization problems involving programs.National Science Foundation (U.S.) (Award 1156059)National Science Foundation (U.S.) (Award 1116362