Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Multi-Agent Systems (MAS) have emerged as powerful distributed computing environments for complex problem-solving, yet they inherently face challenges from conflicting agent goals, competing resource demands, and contradictory information. This article examines the critical techniques for resolving these conflicts to ensure system efficiency, reliability, and fairness. The article explores four major negotiation and economic mechanisms that facilitate bargaining and resource allocation through market-inspired protocols; information conflict resolution techniques that reconcile contradictory beliefs through merging algorithms and trust models; hierarchical and priority-based that establish structured decision frameworks; and emerging methods that integrate these techniques with machine learning and ethical considerations. Drawing on empirical evidence from diverse application domains including healthcare, transportation, manufacturing, and financial services, this analysis demonstrates how effective conflict resolution not only improves technical performance metrics but also enhances user trust, system resilience, and operational sustainability. As MAS deployments continue expanding into increasingly critical and socially sensitive domains, addressing these conflict resolution challenges represents a fundamental requirement for realizing the full potential of multi-agent technologies in real-world applications.