Abstract
BackgroundThe development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action.FindingsThe question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF).ConclusionsOur evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM/F1 score than using the CNN encoder.
Highlights
Robot learning is a research field at the intersection of machine learning and robotics
A novel approach proposed by Perera V et al [1] enables a mobile service robot to understand questions about the history of tasks it has executed. They frame the problem of understanding such questions as grounding an input sentence to a query that can be executed on the logs recorded by the robot during its runs, by defining a query as an operation followed by a set of filters
Proposed method Our research focuses on question answering using the Deep Learning approach
Summary
Robot learning is a research field at the intersection of machine learning and robotics. A novel approach proposed by Perera V et al [1] enables a mobile service robot to understand questions about the history of tasks it has executed. We would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF). Our model get 82.43% F1 score and the RNN based encoder will give a higher EM/F1 score than using the CNN encoder
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