Open Access

ARTICLE

Multi-Agent Reinforcement Learning Based Context-Aware Heterogeneous Decision Support System

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*, Changheun Oh⁴

1 Department of Software Engineering, Faculty of Information Technology, University of Lahore, Lahore, Pakistan
2 Department of Software Engineering, Faculty of IT & CS, University of Central Punjab, Lahore, Pakistan
3 Department of Computer Science, Faculty of Information Science & Technology, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
4 Department of Software and Communications Engineering, Sejong Campus, Hongik University, Sejong-City, Republic of Korea

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

Computers, Materials & Continua 2026, 87(3), 71 https://doi.org/10.32604/cmc.2026.077510

Received 10 December 2025; Accepted 17 February 2026; Issue published 09 April 2026

Abstract

The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence (AI) assistive reasoning with the incorporation of context-awareness. Context-awareness plays a significant role in fulfilling users’ needs whenever and wherever needed. Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems, perform reasoning using reinforcement learning (RL) or other reasoning techniques, and then adapt behavior. The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place. Generally, agents are rewarded for the correct actions and punished for incorrect actions. In an RL deployment setting, agents intend to get cumulative maximal rewards through the continuous learning process. These systems often operate in a highly decentralized environment and exhibit complex adaptive behavior. However, the agent’s actions on the imperfect nature of context may cause inconsistent reasoning behavior in terms of the agent’s reward policies. In this paper, we present a semantic knowledge-based Multi-agent Reinforcement Learning (MARL) formalism for a context-aware heterogeneous decision support system. This is a four-layered architecture to schedule user’s routine tasks where user’s data is acquired with limited or no human intervention and perform operations autonomously based on agent’s reward/punishment policies. For this, we develop a comprehensive case study considering three different domains’ ontologies; namely, Smart Home, Smart Shopping, and Smart Fridge Systems, with the prototypal implementation of the system and show the valid execution dynamics, correctness behavior, and verify the agent’s optimal reward policies.

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Keywords

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