Symbolic planning with metric time

1992 ◽  
Author(s):  
T. R. MacMillan
Keyword(s):  
Symbolic Planning

Chapter 10. Explainable Neuro-Symbolic Hierarchical Reinforcement Learning

2021 ◽  
Author(s):  
Daoming Lyu ◽  
Fangkai Yang ◽  
Hugh Kwon ◽  
Bo Liu ◽  
Wen Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Black Box ◽  
Influential Factor ◽  
Hierarchical Reinforcement Learning ◽  
Interactive Decision Making ◽  
Symbolic Approach ◽  
Trust Systems ◽  
Symbolic Planning

Human-robot interactive decision-making is increasingly becoming ubiquitous, and explainability is an influential factor in determining the reliance on autonomy. However, it is not reasonable to trust systems beyond our comprehension, and typical machine learning and data-driven decision-making are black-box paradigms that impede explainability. Therefore, it is critical to establish computational efficient decision-making mechanisms enhanced by explainability-aware strategies. To this end, we propose the Trustworthy Decision-Making (TDM), which is an explainable neuro-symbolic approach by integrating symbolic planning into hierarchical reinforcement learning. The framework of TDM enables the subtask-level explainability from the causal relational and understandable subtasks. Besides, TDM also demonstrates the advantage of the integration between symbolic planning and reinforcement learning, reaping the benefits of both worlds. Experimental results validate the effectiveness of proposed method while improving the explainability in the process of decision-making.

scholarly journals Logic-Based Sequential Decision-Making

2019 ◽  
Vol 33 ◽  
pp. 9995-9996
Author(s):  
Daoming Lyu ◽  
Fangkai Yang ◽  
Bo Liu ◽  
Daesub Yoon
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
High Dimensional ◽  
Great Success ◽  
Sequential Decision ◽  
Sensory Inputs ◽  
Hierarchical Decision ◽  
High Level ◽  
Data Efficiency ◽  
Symbolic Planning

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options. This framework features a planner – controller – meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. 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. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches.

Coordinating heterogeneous teams of robots using temporal symbolic planning

2013 ◽  
Vol 34 (4) ◽  
pp. 277-294 ◽  
Author(s):  
Kai M. Wurm ◽  
Christian Dornhege ◽  
Bernhard Nebel ◽  
Wolfram Burgard ◽  
Cyrill Stachniss
Keyword(s):  
Heterogeneous Teams ◽  
Symbolic Planning
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