In this article, we explore a new approach to solving problems using machine learning. The main goal is to understand how to use heuristics, or rules of thumb, to make decisions more efficiently. The author provides an overview of the problem and explains that previous methods have limitations due to their dependence on complex models.
Sampling
To address these limitations, we focus on sampling, which involves obtaining estimates of the cost-to-goal without relying on computationally expensive heuristic functions. The author explains that searching for these values can be costly and exponential in the size of the task. Instead, they propose using regression to obtain an upper bound on the cost-to-goal, making the process more efficient.
Regression
To generate complete states from partial ones, we use regression. The author compares this approach to other methods in the literature and highlights its advantages, including faster coverage and less time required.
Comparison to Other Methods
While we cannot conduct a comprehensive comparison of our method with other learning-based approaches due to differences in machine configurations and limitations, we provide some insights into how our approach fares against Ferber et al. (2022) and O’Toole et al. (2022). The author notes that the proposed approach achieves better coverage in less time compared to prior methods.
Summary
In summary, this article presents a new approach to solving problems using machine learning heuristics. By focusing on sampling and regression, we can make decisions more efficiently without relying on complex models. While we cannot compare our method directly to other approaches due to differences in configurations, preliminary experiments suggest that our approach achieves better coverage in less time. This work provides a promising direction for improving the efficiency of machine learning heuristics in solving complex problems.
