John Deere's quest to solve agricultures deep-learning problems - [Spectral Lines]

Learning to Solve Constraint Problems

This paper explains why learning to solve constraint problems is so difficult, and describes a set of methods that has been effective on a broad variety of problem classes. The primary focus is on learning an effective search algorithm as a weighted subset of ordering heuristics. Experiments show the impact of several novel techniques on a variety of problems. When a planning problem is cast as...

Learning to Solve Problems from Exercises

Learning Adaptation to Solve Constraint Satisfaction Problems

Constraint-based problems are hard combinatorial problems and are usually solved by heuristic search methods. In this paper, we consider applying a machine learning approach to improve the performance of these search-based solvers. We apply reinforcement learning in the context of Constraint Satisfaction Problems (CSP) to learn a value function, which results in a novel solving strategy. The mo...

Preferences Improve Learning to Solve Constraint Problems

Search heuristics that embody domain knowledge often reflect preferences among choices. This paper proposes a variety of ways to identify a good mixture of search heuristics and to integrate the preferences they express. Two preference expression methods inspired by the voting literature in political science prove particularly reliable. On hard problems, good methods to integrate such heuristic...

Learning to Automatically Solve Algebra Word Problems

We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including either full equations or just the final a...