With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and ...
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With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, there is a need for more effective methods to mine a large amount of multi-source information and to exploit the explicit and implicit relationships between the knowledge contained in process knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning decision-making phase as a sequential recommendation of process knowledge. Then, this paper designs in detail the state representation method that combines process decision sequences and potential relationships between processes. This paper also creates the composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure has more accurate recommendation results than other methods.
We have established an emotional model to enhance a virtual worker simulation, which could be also used to support robots in a joined human-robot work-task inside an industrial setting. The robot is able to understand...
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ISBN:
(纸本)9781479974061
We have established an emotional model to enhance a virtual worker simulation, which could be also used to support robots in a joined human-robot work-task inside an industrial setting. The robot is able to understand people's individual and specific knowledge as well as capabilities, which are ultimately linked to an emotional consequence. As a result, the emotional model outputs the emotional valence calculated as positive or negative values, respective to reward and punishment. This output is applied as value function for a reinforcement learning agent. There we use an actor critic algorithm extended by eligibility traces and task specific conditions to learn the optimal action sequences. We show the influence of emotional reward leads to differences in the learned action sequences in comparison to a simple task performance evaluation reward. Therefore the robot is able to calculate emotional feelings of a human during a given working task, is able to decide if there is a better, more emotional stable path to doing this working task and moreover the robot is able to decide when the human is needed help or even not.
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