MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage between Teams in MOBA Games
This work addresses the challenge of real-time evaluation and prediction in complex MOBA games for researchers and AI developers, though it is incremental as it builds on existing value network concepts.
The paper tackles the problem of evaluating real-time game situations and predicting outcomes in MOBA games by introducing MOBA-Slice, a time slice-based framework that uses a neural network model; it achieves a 3.7% higher accuracy in result prediction compared to an existing method and supports predicting remaining game time.
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.