Open Access

ARTICLE

Semantic Knowledge Based Reinforcement Learning Formalism for Smart Learning Environments

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*

1 Department of Software Engineering, Faculty of Information Technology, University of Lahore, Lahore, 54000, Pakistan
2 Department of Software Engineering, Faculty of IT & CS, University of Central Punjab, Lahore, 54000, Pakistan
3 Department of Computer Science, Faculty of Information Science & Technology, COMSATS University Islamabad, Lahore Campus, Lahore, 54000, Pakistan
4 Department of Software Convergence and Communication Engineering, Sejong Campus, Hongik University, Sejong-si, Seoul, 30016, Republic of Korea

* Corresponding Authors: Ibrar Hussain. Email: ; Byung-Seo Kim. Email:

Computers, Materials & Continua 2025, 85(1), 2071-2094. https://doi.org/10.32604/cmc.2025.068533

Received 31 May 2025; Accepted 23 July 2025; Issue published 29 August 2025

Abstract

Smart learning environments have been considered as vital sources and essential needs in modern digital education systems. With the rapid proliferation of smart and assistive technologies, smart learning processes have become quite convenient, comfortable, and financially affordable. This shift has led to the emergence of pervasive computing environments, where user’s intelligent behavior is supported by smart gadgets; however, it is becoming more challenging due to inconsistent behavior of Artificial intelligence (AI) assistive technologies in terms of networking issues, slow user responses to technologies and limited computational resources. This paper presents a context-aware predictive reasoning based formalism for smart learning environments that facilitates students in managing their academic as well as extra-curricular activities autonomously with limited human intervention. This system consists of a three-tier architecture including the acquisition of the contextualized information from the environment autonomously, modeling the system using Web Ontology Rule Language (OWL 2 RL) and Semantic Web Rule Language (SWRL), and perform reasoning to infer the desired goals whenever and wherever needed. For contextual reasoning, we develop a non-monotonic reasoning based formalism to reason with contextual information using rule-based reasoning. The focus is on distributed problem solving, where context-aware agents exchange information using rule-based reasoning and specify constraints to accomplish desired goals. To formally model-check and simulate the system behavior, we model the case study of a smart learning environment in the UPPAAL model checker and verify the desired properties in the model, such as safety, liveness and robust properties to reflect the overall correctness behavior of the system with achieving the minimum analysis time of 0.002 s and 34,712 KB memory utilization.

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