# Dynamic Difficulty Adjustment on MOBA Games

## TL;DR

This paper proposes a dynamic difficulty adjustment system for MOBA games that adapts AI opponent behavior based on player performance to enhance entertainment and reduce frustration.

## Contribution

It introduces a novel difficulty adjustment mechanism that dynamically adapts AI difficulty in MOBA games based on real-time player performance metrics.

## Key findings

- The system effectively adapts to different player skill levels.
- Players perceive difficulty adjustments as more engaging.
- Player expertise influences perception of difficulty and adaptation.

## Abstract

This paper addresses the dynamic difficulty adjustment on MOBA games as a way to improve the player's entertainment. Although MOBA is currently one of the most played genres around the world, it is known as a game that offer less autonomy, more challenges and consequently more frustration. Due to these characteristics, the use of a mechanism that performs the difficulty balance dynamically seems to be an interesting alternative to minimize and/or avoid that players experience such frustrations. In this sense, this paper presents a dynamic difficulty adjustment mechanism for MOBA games. The main idea is to create a computer controlled opponent that adapts dynamically to the player performance, trying to offer to the player a better game experience. This is done by evaluating the performance of the player using a metric based on some game features and switching the difficulty of the opponent's artificial intelligence behavior accordingly. Quantitative and qualitative experiments were performed and the results showed that the system is capable of adapting dynamically to the opponent's skills. In spite of that, the qualitative experiments with users showed that the player's expertise has a greater influence on the perception of the difficulty level and dynamic adaptation.