Analysis of Artificial Intelligence Based Petri Net Approach to Intelligent Integration of Design

2006 ◽  
Author(s):  
Guo-yan Huang
Keyword(s):  
Artificial Intelligence ◽  
Petri Net

scholarly journals Human Resource Petri Net Allocation Model Based on Artificial Intelligence and Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weihuang Dai ◽  
Yi Hu ◽  
Zijiang Zhu ◽  
Xiaofang Liao
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Resource Allocation ◽  
Human Resources ◽  
Human Resource ◽  
Petri Net ◽  
Resource Scheduling ◽  
Accuracy Rate ◽  
Allocation Model ◽  
Resource Data

The reasonable allocation and use of human resources is an important content in the process of complex system analysis and design. This paper studies the human resource allocation model of Petri net based on artificial intelligence and neural network. In this paper, combined with the characteristics of human resource scheduling, human resource mobility, concurrency, and obvious classification characteristics, the human resource allocation model based on Petri net is implemented. In this paper, the model is trained with the python version of human resource analysis data set. The training parameters are 100, the error coefficient is 0.001, and the learning speed is 0.01. First, the coding rules of human resource data are established. Then, the parameters are input into the model, and the human resource data are trained in the model. Finally, the results of the model output layer are analyzed. The research study shows that the average prediction accuracy of this model is 78.85%. Model training requires the addition of 25 neurons for every 0.01 increase to improve the accuracy of predicting dynamic data of human resources. If the accuracy rate exceeds 75%, the increase in the number of neurons cannot be compensated for by the increase in the accuracy rate, but it is most efficient when the amount of data for human resource scheduling is 2000 to 4000. Therefore, this system can effectively allocate small- and medium-sized human resources and has a high accuracy.

A literature overview of knowledge sharing between Petri nets and ontologies

2016 ◽  
Vol 31 (3) ◽  
pp. 239-260 ◽  
Author(s):  
Haitao Cheng ◽  
Zongmin Ma
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Knowledge Sharing ◽  
Petri Nets ◽  
Petri Net ◽  
Knowledge Engineering ◽  
Future Research ◽  
Knowledge Domain ◽  
Literature Overview ◽  
Popular Knowledge

AbstractKnowledge representation is a subarea of artificial intelligence concerned with using formal symbols to represent a set of facts within a knowledge domain. Two popular knowledge representation languages, namely Petri net and ontology, are promising knowledge sharing and reusing methods in knowledge engineering. The combination of Petri net and ontology can facilitate achieving complementary advantages. Currently, many efforts have been done on knowledge sharing between Petri nets and ontologies. To investigate these issues and more importantly serve as identifying the direction of knowledge sharing between Petri nets and ontologies, in this paper we give a comprehensive literature overview of knowledge sharing between Petri net models and ontology models to satisfy the obvious need. In detail, we discuss the knowledge sharing from two aspects: the different knowledge representation approaches of ontology to represent and reason Petri net and issues of constructing Petri net from ontology. In addition, other important issues on applications and directions for future research are discussed in detail.

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence
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