Combinatorial Reconfiguration

Combinatorial reconfiguration studies the transformation between feasible solutions of combinatorial problems, focusing on whether one solution can be reached from another through a sequence of allowable intermediate solutions. Current research explores efficient algorithms for various models, including those representing modular robots (e.g., sliding cubes) and abstract problems like dominating sets, often employing techniques like Answer Set Programming to find optimal or near-optimal reconfiguration paths. This field is significant for its applications in diverse areas such as network analysis and robotics, offering insights into the complexity of transforming systems and optimizing reconfiguration processes.