MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage between Teams in MOBA Games

TL;DR

MOBA-Slice is a neural network-based framework that evaluates real-time advantage and predicts outcomes in MOBA games like DotA2, aiding AI development and game analysis.

Contribution

It introduces MOBA-Slice, a novel time slice evaluation framework using neural networks for real-time advantage assessment in MOBA games.

Findings

Achieves 3.7% higher accuracy than DotA Plus Assistant in result prediction.

Supports prediction of remaining game time.

Works effectively on arbitrary match replays.

Abstract

Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games. MOBA-Slice is a quantitative evaluation method based on learning, similar to the value network of AlphaGo. It establishes a foundation for further MOBA related research including AI development. In MOBA-Slice, with an analysis of the deciding factors of MOBA game results, we design a neural network model to fit our discounted evaluation function. Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a typical and popular MOBA game. Experiments on a large number of match replays show that our model works well on arbitrary matches. MOBA-Slice not only has an accuracy 3.7% higher than DotA Plus Assistant at result prediction, but also supports the prediction of the remaining time of the game, and then realizes the evaluation of relative advantage between teams.