\chapter*{Motivation}
In Multiplayer Online Battle Arena (MOBA) games such as League of Legends, itemization is a core strategic component that significantly impacts gameplay outcomes. Choosing the right items can mean the difference between success and failure in a given match, especially at higher levels of competitive play. However, the decision-making process is complex: it depends on dynamic variables such as enemy team composition, champion roles, in-game gold availability, and the current phase of the match.

Despite the existence of community-curated build guides and statistical recommendations, these resources are static and often do not account for the unique real-time conditions of each match. Players are frequently left to make item choices under pressure, relying on personal experience, intuition, or trial and error. This leads to suboptimal builds, particularly for new or intermediate players who may not fully grasp the nuanced impact of itemization in various game states.

Fuzzy logic offers a compelling solution to this problem. Unlike traditional binary or rule-based systems, fuzzy logic excels in environments characterized by uncertainty, partial truths, and overlapping conditions, all of which are inherent in MOBA gameplay. By modeling vague concepts such as 'high threat', 'low durability' or 'need for utility' with fuzzy variables, it becomes possible to create an intelligent and adaptable item recommendation system that better reflects the complexity of real-world decision making in game.

This thesis proposes a novel application of fuzzy logic for real-time, context-aware item recommendation in MOBA games. By evaluating multiple input criteria (such as enemy damage type, crowd control threat, champion durability, and game phase) and mapping them to a multi-criteria output (like need for damage, defense, and utility), the system aims to assist players in making smarter, situationally appropriate itemization decisions.



