Implementation of Machine Learning Methods to solve Political Problems

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...

Applications of machine learning: matching problems to tasks and methods

The terminology of Machine Learning and Data Mining methods does not always allow a simple match between practical problems and methods. While some problems look similar from the user’s point of view, but require different methods to be solved, some others look very different, yet they can be solved by applying the same methods and tools. Choosing appropriate Machine Learning methods for proble...

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...