Anytime Algorithm

Anytime algorithms are designed to provide a solution to a problem quickly, then iteratively improve that solution until a time limit is reached or an optimal solution is found. Current research focuses on developing and improving these algorithms within various frameworks, including heuristic search (like beam search and A*), partially observable Markov decision processes (POMDPs), and stochastic games, often incorporating parallel processing for efficiency. This work is significant because it enables real-time solutions for complex problems in robotics, planning, and decision-making under uncertainty, where obtaining an optimal solution might be computationally infeasible.