22 citations · 26 across the 3 of their papers we have counts for
10 papers
PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agent
Daoming Lyu, Bo Liu, Jianshu Chen
We consider the problem of multi-task reasoning (MTR), where an agent can solve multiple tasks via (first-order) logic reasoning. This capability is essential for human-like intell…
TDM: Trustworthy Decision-Making via Interpretability Enhancement
Daoming Lyu, Fangkai Yang, Hugh Kwon +3
Human-robot interactive decision-making is increasingly becoming ubiquitous, and trust is an influential factor in determining the reliance on autonomy. However, it is not reasonab…
Variance-Reduced Off-Policy Memory-Efficient Policy Search
Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh +3
Off-policy policy optimization is a challenging problem in reinforcement learning (RL). The algorithms designed for this problem often suffer from high variance in their estimators…
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…
Proceedings 35th International Conference on Logic Programming (Technical Communications)
Bart Bogaerts, Esra Erdem, Paul Fodor +7
Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are sought in all ar…
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…