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Found 14 papers, 7 papers with code
GPU accelerated program synthesis: Enumerate semantics, not syntax!
Program synthesis is an umbrella term for generating programs and logical formulae from specifications.
EcoSearch: A Constant-Delay Best-First Search Algorithm for Program Synthesis
Many approaches to program synthesis perform a combinatorial search within a large space of programs to find one that satisfies a given specification.
Revelations: A Decidable Class of POMDPs with Omega-Regular Objectives
Partially observable Markov decision processes (POMDPs) form a prominent model for uncertainty in sequential decision making.
Programmatic Reinforcement Learning: Navigating Gridworlds
The goal of this paper is to give first answers to these questions, initiating a theoretical study of programmatic RL.
Learning temporal formulas from examples is hard
We study the problem of learning linear temporal logic (LTL) formulas from examples, as a first step towards expressing a property separating positive and negative instances in a way that is comprehensible for humans.
WikiCoder: Learning to Write Knowledge-Powered Code
WikiCoder solves tasks that no program synthesizers were able to solve before thanks to the use of knowledge graphs, while integrating with recent developments in the field to operate at scale.
Scaling Neural Program Synthesis with Distribution-based Search
We investigate how to augment probabilistic and neural program synthesis methods with new search algorithms, proposing a framework called distribution-based search.
Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic
Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas.
The Complexity of Learning Linear Temporal Formulas from Examples
In this paper we initiate the study of the computational complexity of learning linear temporal logic (LTL) formulas from examples.
Optimal Transformations of Muller Conditions
In this paper, we are interested in automata over infinite words and infinite duration games, that we view as general transition systems.
Formal Languages and Automata Theory F.4.3
Data Generation for Neural Programming by Example
A particular challenge lies in generating meaningful sets of inputs and outputs, which well-characterize a given program and accurately demonstrate its behavior.
Verification of Neural Networks: Specifying Global Robustness using Generative Models
The success of neural networks across most machine learning tasks and the persistence of adversarial examples have made the verification of such models an important quest.
