350 citations · 371 across the 12 of their papers we have counts for
12 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…
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…
Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems
Vedant Khandelwal, Amit Sheth, Forest Agostinelli
Pathfinding problems are found throughout robotics, computational science, and natural sciences. Traditional methods to solve these require training deep neural networks (DNNs) for…
Trust and ethical considerations in a multi-modal, explainable AI-driven chatbot tutoring system: The case of collaboratively solving Rubik's Cube
Kausik Lakkaraju, Vedant Khandelwal, Biplav Srivastava +6
Artificial intelligence (AI) has the potential to transform education with its power of uncovering insights from massive data about student learning patterns. However, ethical and…
On Solving the Rubik's Cube with Domain-Independent Planners Using Standard Representations
Bharath Muppasani, Vishal Pallagani, Biplav Srivastava +1
Rubik's Cube (RC) is a well-known and computationally challenging puzzle that has motivated AI researchers to explore efficient alternative representations and problem-solving meth…
Independent Modular Networks
Hamed Damirchi, Forest Agostinelli, Pooyan Jamshidi
Monolithic neural networks that make use of a single set of weights to learn useful representations for downstream tasks explicitly dismiss the compositional nature of data generat…
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…
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi
We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to a…
Solving the Rubik's Cube Without Human Knowledge
Stephen McAleer, Forest Agostinelli, Alexander Shmakov +1
A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision. Recently, deep reinforcement learning algorithms…
Learning Activation Functions to Improve Deep Neural Networks
Forest Agostinelli, Matthew Hoffman, Peter Sadowski +1
Artificial neural networks typically have a fixed, non-linear activation function at each neuron. We have designed a novel form of piecewise linear activation function that is lear…