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Found 7 papers, 1 papers with code
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming
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.
A Joint Planning and Learning Framework for Human-Aided Decision-Making
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.
2019 Evolutionary Algorithms Review
Evolutionary algorithm research and applications began over 50 years ago.
Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform
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 build, test, deploy and validate machine learning models, in the sense that the insight of developing machine learning solutions are not structurally encoded, justified and transferred.
SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning
The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input.
A Practical Incremental Learning Framework For Sparse Entity Extraction
This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation.
PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making
Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents.
