5 papers
The DeepXube Software Package for Solving Pathfinding Problems with Learned Heuristic Functions and Search
Forest Agostinelli
DeepXube is a free and open-source Python package and command-line tool that seeks to automate the solution of pathfinding problems by using machine learning to learn heuristic fun…
Beyond Single-Step Updates: Reinforcement Learning of Heuristics with Limited-Horizon Search
Gal Hadar, Forest Agostinelli, Shahaf S. Shperberg
Many sequential decision-making problems can be formulated as shortest-path problems, where the objective is to reach a goal state from a given starting state. Heuristic search is…
Student Engagement in AI Assisted Complex Problem Solving: A Pilot Study of Human AI Rubik's Cube Collaboration
Kirk Vanacore, Jaclyn Ocumpaugh, Forest Agostinelli +3
Games and puzzles play important pedagogical roles in STEM learning. New AI algorithms that can solve complex problems offer opportunities for scaffolded instruction in puzzle solv…
A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks
Forest Agostinelli, Shahaf S. Shperberg, Alexander Shmakov +3
Efficiently solving problems with large action spaces using A* search remains a significant challenge. This is because, for each iteration of A* search, the number of nodes generat…
PDDLFuse: A Tool for Generating Diverse Planning Domains
Vedant Khandelwal, Amit Sheth, Forest Agostinelli
Various real-world challenges require planning algorithms that can adapt to a broad range of domains. Traditionally, the creation of planning domains has relied heavily on human im…