533 citations · 986 across the 32 of their papers we have counts for
32 papers
Policy-Value Alignment and Robustness in Search-based Multi-Agent Learning
Niko A. Grupen, Michael Hanlon, Alexis Hao +2
Large-scale AI systems that combine search and learning have reached super-human levels of performance in game-playing, but have also been shown to fail in surprising ways. The bri…
Graph Value Iteration
Dieqiao Feng, Carla P. Gomes, Bart Selman
In recent years, deep Reinforcement Learning (RL) has been successful in various combinatorial search domains, such as two-player games and scientific discovery. However, directly…
Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning
Dieqiao Feng, Carla Gomes, Bart Selman
Despite the success of practical solvers in various NP-complete domains such as SAT and CSP as well as using deep reinforcement learning to tackle two-player games such as Go, cert…
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances
Dieqiao Feng, Carla P. Gomes, Bart Selman
In recent years, we have witnessed tremendous progress in deep reinforcement learning (RL) for tasks such as Go, Chess, video games, and robot control. Nevertheless, other combinat…
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
Di Chen, Yiwei Bai, Sebastian Ament +7
Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials.…
Structure Amplification on Multi-layer Stochastic Block Models
Xiaodong Xin, Kun He, Jialu Bao +2
Much of the complexity of social, biological, and engineered systems arises from a network of complex interactions connecting many basic components. Network analysis tools have bee…