Combining Model-Based $Q$ -Learning With Structural Knowledge Transfer for Robot Skill Learning

2019 ◽  
Vol 11 (1) ◽  
pp. 26-35 ◽  
Author(s):  
Zhen Deng ◽  
Haojun Guan ◽  
Rui Huang ◽  
Hongzhuo Liang ◽  
Liwei Zhang ◽  
...  
2017 ◽  
Vol 21 (5) ◽  
pp. 1053-1076 ◽  
Author(s):  
Michal Kuciapski

Purpose Although mobile devices are ubiquitous among employees, their awareness and readiness to use mobile technologies for competence development is still not widespread and therefore requires further exploration. The purpose of this study is to propose a conceptual model based on the unified theory of acceptance and use of technology (UTAUT) to explain the determinants that affect employees’ intention to use mobile devices and software for knowledge transfer during the process of knowledge management. Design/methodology/approach A conceptual model based on the UTAUT with new variables concerning relative usability (RU) and user autonomy (UA) and new connections between the determinants was developed as a result of a subject matter literature review. A structural equation modelling approach was used to validate the model on the basis of data collected via a survey collected from 371 employees from 21 sectors, both public and private. Findings The UTAUT model extended by new variables like RU and UA explains employee acceptance of mobile technologies for knowledge transfer reasonably well. New proposed variables highlighted that the usability of technology compared to other solutions and user autonomy in the selection and the use of applications have the strongest impact on the employees’ intention to use mobile devices and software for knowledge transfer. Research limitations/implications This model explains the 55 per cent behavioral intention of employees to use mobile technologies for knowledge transfer. Even though it is quite high in terms of acceptance theories, some new variables should be explored. Furthermore, study does not verify whether m-learning acceptance for knowledge transfer is sector-specific. Practical implications Mobile technologies used for knowledge transfer by employees should allow for high UA through their ability to select solutions that they find convenient, use of preferred platforms, personalize applications and utilize devices and software in various environments. They should not be simplified and should have the same functionality and efficiency of use as alternative solutions like web and desktop applications, even if additional effort to learn them would be required. Mobile technologies that take into account UA and RU support the process of employees capturing, distributing and effectively using knowledge. Originality/value The elaborated model provides a valuable solution with practical implications for increasing mobile technologies acceptance for knowledge transfer. The study results contribute both to knowledge management and technology acceptance research fields by introducing two new determinants for the acceptance of technologies in knowledge transfer, such as UA and RU with several additional connections between existing UTAUT variables.


2019 ◽  
Vol 95 ◽  
pp. 02001
Author(s):  
Yuan Jianhua ◽  
Li Haofei ◽  
Ji Chenyu ◽  
Ji Yu

The recent development of the Energy Internet has urged the conventional inefficient utilization of single energy to change towards the more developed energy usage of optimal dispatch of the integrated energy system. In this context, the joint optimization scheduling framework of integrated energy system is established based on the energy hub. Then a typical integrated energy system model is developed considering carbon emission and energy supply costs with valve point effect. To solve this non-linear problem with non-convex, discontinuously differentiable characteristic, the cascaded algorithm combined with the knowledge transfer based Q-learning algorithm and interior point method is applied on the model. Meanwhile, the efficiency is greatly improved by knowledge transfer. Case studies have been carried out on a 33energy hubs test system to verify the effectiveness of the proposed model and algorithm.


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