Attempting to Discover Infinite Combos in Fighting Games Using Hidden Markov Models

Designing for balance is core in competitive games. Ensuring fairness in player vs player games is a design goal that any game that features this sort of interaction should, at least to some extent, strive for. Unfortunately, it often happens that the whole of the possibilities given to a player exceeds the designer's expectations, creating combinations and exploits that sometimes threaten the game's reliability as a balanced and competitive title. Focusing on searching for an automated solution to one of the main flaws of fighting games, specifically infinite or unfair combos, this work discusses the use of Hidden Markov Models to predict if a subset of player commands would result in a combo. To this goal we study two different approaches: predicting the most likely sequence of player inputs in each frame that would result in a combo and the most likely sequence of player actions, regardless of frame information, that also could result in a combo. Experiments were performed on a fighting game of our own design. Both supervised and unsupervised learning algorithms were applied, however, due of the excess of noise in the first approach and particularities of the implemented model, the first approach was unable to successfully predict combos. We then change our minimal discrete time interval to a player action, rather than game frame. In this last scenario the HMM is capable of identifying small combos but, when asked to find larger ones, it can only append smaller combos that cannot be performed in the actual game. Despite that, our discussions in the matter and our findings are presented in this paper and should be relevant to this overall discussion.