Using Q-Learning to Personalize Pedagogical Policies for Addition Problems

Shen, Danyating; Truong, T.; Weintz, Cortney

doi:10.1109/conf-spml54095.2021.00043

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2023

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“…Thus, this paper introduced the combination of Fuzzy and Q-Learning to handle cyber risks and mapped that with insurance active policies. Several research related to fuzzy rules are presented on other applications and able to handle uncertainty [18,19], whereby Q-Learning is one of the algorithms in Reinforcement Learning with the ability to cater on policy approach [22]. On policy in a nutshell is to ensure there is no misguide and very details in every step adopted and very useful to tackle active policy and global cyber risk.…”

Section: Government Agenciesmentioning

confidence: 99%

An Active Cyber Insurance Policy Against Cybersecurity Risks Using Fuzzy Q-Learning

Ramli

2023

ARASET

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Insurance is an extremely diversified from one clear-cut policy to another depending on a rising demand. Internet based businesses are a booming industry and falls under categories of small, medium and enterprise. It is no exception that cybersecurity risks are exponentially presents as a continues fears in this business environment. To ensure this insecurity is handled effectively, cyber insurance policies introduced by insurance companies. However, the nature of cyber risks must be addressed in much more robust and complex algorithms. Autonomic computing with the combination of Fuzzy and Q-Learning is introduced to ensure active policies are ready to handle the uncertainty and together with the ability to learn and mitigate the unrest situation.

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Section: Government Agenciesmentioning

confidence: 99%

An Active Cyber Insurance Policy Against Cybersecurity Risks Using Fuzzy Q-Learning

Ramli

2023

ARASET

View full text Add to dashboard Cite

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Using Q-Learning to Personalize Pedagogical Policies for Addition Problems

Cited by 1 publication

References 7 publications

An Active Cyber Insurance Policy Against Cybersecurity Risks Using Fuzzy Q-Learning

An Active Cyber Insurance Policy Against Cybersecurity Risks Using Fuzzy Q-Learning

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