MICSL: Multiple Iterative Constraint Satisfaction based Learning

MICSL: Multiple Iterative Constraint Satisfaction based Learning

A novel concept learning algorithm named, MICSL: Multiple Iterative Constraint Satisfaction based Learning, is presented. The algorithm utilizes mathematical programming and constraint satisfaction techniques towards uniform representation and management of both data and background knowledge. It offers a flexible enough learning framework and respective services. The representation flexibility ...

Nogood Learning for Constraint Satisfaction

The basic algorithm is derived from the weak-commitment algorithm (WC) of Yokoo [17], which has been shown to outperform breakout and min-conflict backtracking on certain classes of problems. Weakcommitment search is that part of WC which results from removing the learning mechanism; this will be called WC-NL. WC-NL is coupled with learning-by-merging to form the new algorithm WC-NG. Experiment...

Solving a Constraint Satisfaction Problem through Iterative Reordering Augmentation

Survey Propagation: Iterative Solutions to Constraint Satisfaction Problems

Iterative algorithms, such as the well known Belief Propagation algorithm, have had much success in solving problems in statistical inference and coding and information theory. Survey Propagation attempts to apply iterative message passing algorithms to solve difficult combinatorial problems, in particular constraint satisfaction problems such as k-sat and coloring problems. Intuition from stat...

Iterative Improvement Techniques for Solving Tight Constraint Satisfaction Problems

In this thesis, we explore two iterative improvement techniques: a heuristic hill-climbing strategy (denoted LS) and a multi-agent based search (denoted ERA). We focus our investigations on one small but challenging real-world application, which is the assignment of Graduate Teaching Assistants (GTA) to academic tasks. We design and implement the LS and ERA mechanisms to solve this application....