1 citations · 1 across the 1 of their papers we have counts for
7 papers
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming
Daoming Lyu, Fangkai Yang, Bo Liu +1
Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry. By contrast, human l…
2019 Evolutionary Algorithms Review
Andrew N. Sloss, Steven Gustafson
Evolutionary algorithm research and applications began over 50 years ago. Like other artificial intelligence techniques, evolutionary algorithms will likely see increased use and d…
A Joint Planning and Learning Framework for Human-Aided Decision-Making
Daoming Lyu, Fangkai Yang, Bo Liu +1
Conventional reinforcement learning (RL) allows an agent to learn policies via environmental rewards only, with a long and slow learning curve, especially at the beginning stage. O…
Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform
Alexander Elkholy, Fangkai Yang, Steven Gustafson
Machine learning is becoming an essential part of developing solutions for many industrial applications, but the lack of interpretability hinders wide industry adoption to rapidly…
SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning
Daoming Lyu, Fangkai Yang, Bo Liu +1
Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretab…
A Practical Incremental Learning Framework For Sparse Entity Extraction
Hussein S. Al-Olimat, Steven Gustafson, Jason Mackay +2
This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation crit…