Intelligent Decision Model for Home Robot Based on Structured and Unstructured Data Processing

Abstract

In order to enhance the user’s service experience and help the robot to make more intimate service decisions, this paper proposes a service task cognition and decision model based on structured and unstructured data processing. Firstly, all kinds of information mentioned in user instructions are extracted through natural language processing, including object information, namely structured data and environment information, namely unstructured data. For structured data, it is mapped to the predefined ontology knowledge base to obtain its location attributes and state attributes, and then obtain service instructions. Adaptive fuzzy Petri net (AFPN) is constructed based on fuzzy rules of temperature, humidity and other environmental information. The unstructured data is taken as the input parameter of AFPN, and the service instruction deduced as the output. Then, according to the user’s needs, the network weight can be continuously adjusted. If the user does not mention the environmental information, the environment information is periodically detected by the sensor, and the service instruction reasoning of the unstructured data is performed. Finally, back propagation neural network (BPNN) is introduced to combine the service inference of two kinds of data to eliminate the heterogeneity of different service instructions. Experimental results show that the model can provide different personalized services for users’ preferences.