Finding optimal pedagogical content in an adaptive e-learning platform using a new recommendation approach and reinforcement learning

Madani, Youness; Ezzikouri, Hanane; Erritali, Mohammed; Hssina, Badr

doi:10.1007/s12652-019-01627-1

Cited by 29 publications

(9 citation statements)

References 24 publications

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“…Hybrid fuzzy systems are used in the past for understanding customer behavior [80], heart disease diagnosis [81], and diabetes prediction [81]. Fuzzy logic classifier along with reinforcement learning is used for the development of an intelligent power transformer [82], for handling continuous inputs and learning from continuous actions [83], for small lung nodules detection [84], for finding appropriate pedagogical content [85], for robotic soccer games [44,45], for water blasting system for ship hull corrosion cleaning [86], and for classification of diabetes [73]. All the above mentioned have used fuzzy systems to figure out the issues and solutions in different concepts, but none of them has prioritized the problems or solutions identified in the different concepts.…”

Section: Resultsmentioning

confidence: 99%

Hybrid fuzzy AHP–TOPSIS approach to prioritizing solutions for inverse reinforcement learning

Kukreja

2022

Complex Intell. Syst.

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Reinforcement learning (RL) techniques nurture building up solutions for sequential decision-making problems under uncertainty and ambiguity. RL has agents with a reward function that interacts with a dynamic environment to find out an optimal policy. There are problems associated with RL like the reward function should be specified in advance, design difficulties and unable to handle large complex problems, etc. This led to the development of inverse reinforcement learning (IRL). IRL also suffers from many problems in real life like robust reward functions, ill-posed problems, etc., and different solutions have been proposed to solve these problems like maximum entropy, support for multiple rewards and non-linear reward functions, etc. There are majorly eight problems associated with IRL and eight solutions have been proposed to solve IRL problems. This paper has proposed a hybrid fuzzy AHP–TOPSIS approach to prioritize the solutions while implementing IRL. Fuzzy Analytical Hierarchical Process (FAHP) is used to get the weights of identified problems. The relative accuracy and root-mean-squared error using FAHP are 97.74 and 0.0349, respectively. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) uses these FAHP weights to prioritize the solutions. The most significant problem in IRL implementation is of ‘lack of robust reward functions’ weighting 0.180, whereas the most significant solution in IRL implementation is ‘Supports optimal policy and rewards functions along with stochastic transition models’ having closeness of coefficient (CofC) value of 0.967156846.

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Section: Resultsmentioning

confidence: 99%

Hybrid fuzzy AHP–TOPSIS approach to prioritizing solutions for inverse reinforcement learning

Kukreja

2022

Complex Intell. Syst.

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“…Jensen-Shannon divergence (JSD) was used to find the difference in probability distribution between the target Gaussian mixture model and the current Gaussian mixture model [31]. To learn GMM in reinforcement learning, the distance of two Gaussian distributions Z 1 and Z 2 is explained in Equation (8).…”

Section: Weight Updatementioning

confidence: 99%

“…Conversely, deep reinforcement learning (DRL) has the potential for better scalability and generalization than non-learning methods for autonomous driving decision-making problems. For this reason, DRLs are widely studied in the fields of alarm systems [7], education [8], medicine [9], and sensors [10]. Huegle et al [11,12] proposed DeepSet-Q and DeepScene-Q, which considered multiple vehicles in a region of interest (ROI) and achieved state-of-the-art quality in 2019 and 2020.…”

Section: Introductionmentioning

confidence: 99%

Decision Making for Self-Driving Vehicles in Unexpected Environments Using Efficient Reinforcement Learning Methods

Kim

Eoh²,

Park

2022

Electronics

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Deep reinforcement learning (DRL) enables autonomous vehicles to perform complex decision making using neural networks. However, previous DRL networks only output decisions, so there is no way to determine whether the decision is proper. Reinforcement learning agents may continue to produce wrong decisions in unexpected environments not encountered during the learning process. In particular, one wrong decision can lead to an accident in autonomous driving. Therefore, it is necessary to indicate whether the action is a reasonable decision. As one such method, uncertainty can inform whether the agent’s decision is appropriate for practical application where safety must be guaranteed. Therefore, this paper provides uncertainty in the decision by proposing DeepSet-Q with Gaussian mixture (DwGM-Q), which converges the existing DeepSet-Q and mixture density network (MDN). Calculating uncertainty with the Gaussian mixture model (GMM) produced from MDN made it possible to calculate faster than the existing ensemble method. Moreover, it verified how the agent responds to the unlearned situation through the Simulation of Urban MObility (SUMO) simulator and compared the uncertainty of the decision between the learned and nontrained situation.

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“…To the best of our knowledge, this research is currently limited to literature [80]. User profiles are widely used in recommended systems [115,116]. Similarly, we can research hacker profiles which contain critical hacker information that is used for tracing cyber-attack source tracing, inferring attack target, realizing hacker alignment and tracking down criminals.…”

Section: Social Engineering As Intermediariesmentioning

confidence: 99%

How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

Lin¹

2022

Preprint

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Cybersecurity researchers always ignore the “people” behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— “How to solve cyberspace security issues via social engineering?” We briefly review existing frameworks for social engineering attacks and pro?pose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four “guidelines” for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field.

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Finding optimal pedagogical content in an adaptive e-learning platform using a new recommendation approach and reinforcement learning

Cited by 29 publications

References 24 publications

Hybrid fuzzy AHP–TOPSIS approach to prioritizing solutions for inverse reinforcement learning

Hybrid fuzzy AHP–TOPSIS approach to prioritizing solutions for inverse reinforcement learning

Decision Making for Self-Driving Vehicles in Unexpected Environments Using Efficient Reinforcement Learning Methods

How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

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