In Multiplayer Online Battle Arena (MOBA) games such as League of Legends, selecting the appropriate items for a champion is a crucial yet often complex decision. Players must adapt their builds in real time based on multiple factors such as enemy team composition, threat level, available gold, and game phase. Current item recommendation systems are typically static and do not sufficiently reflect the contextual nuances of live gameplay. This thesis proposes a fuzzy logic-based decision support system designed to recommend context-aware items based on dynamic in-game variables.

The system models key game-state inputs—such as enemy magic damage threat, crowd control presence, champion durability, and match timing—using fuzzy sets and linguistic rules. These inputs are then processed through a fuzzy inference engine to generate output needs such as damage, defense, magic resist, or utility. Items are evaluated against these needs using a multi-criteria scoring function, allowing for the recommendation of items that serve multiple strategic purposes simultaneously.
