A data mining approach to strategy prediction

By Ben George Weber, Michael Mateas. 2009

273 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Economics and Econometrics

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Citations in the corpus

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games (2016, methodology): “Other models are trained on offline datasets of highly skilled players [25, 18, 19, 21].”
Learning macromanagement in starcraft from replays using deep learning (2017, methodology): “Case-based reasoning [9, 19, 30], feature-expanded decision trees [4], and several traditional machine learning algorithms [4] have been used to predict the opponent’s strategy in RTS games by learning from replays.”
Introducing Archetypal Analysis for Player Classification in Games (2011, methodology): “In comparison, [24] utilized a series of classification algorithms for recognizing player strategy in over 5000 replays of StarCraft, employing regression algorithms in order to predict when specific unit or building types would be produced.”
A Bayesian model for opening prediction in RTS games with application to StarCraft (2011, methodology): “We used this replay feature to extract players actions and learn the probabilities of tech trees to happen at a given time and, in this paper, also given a labeled opening.”
A Review of Real-Time Strategy Game AI (2014, methodology): “…can be considered to attempt to make the best decisions possible, particularly when using units with varied abilities (figure 3), but the problem space is not nearly as large as that of the full game, making feasible exploratory approaches to learning domain knowledge (Weber and Mateas 2009).”
Global State Evaluation in StarCraft (2014, methodology): “1) Although there has been a recent trend of using StarCraft replays as data for machine learning tasks (Weber and Mateas 2009) (Synnaeve and Bessière 2011) (Dereszynski et al.”
Applying reinforcement learning to small scale combat in the real-time strategy game StarCraft:Broodwar (2012, background): “[21] examines a large number of StarCraft replays in order to predict an opponents strategy through their build order.”
A Dataset for StarCraft AI & an Example of Armies Clustering (2012, background): “Another approach is to use the gamers’ vocabulary of strategies (and openings) to abstract even more what strategies represent (a set of states, of sequences and of intentions) as in (Weber and Mateas 2009; Synnaeve and Bessi`ere 2011a).”
Guns, swords and data: Clustering of player behavior in computer games in the wild (2012, methodology): “Weber & Mateas [24] employed a series of”
Esports Research: A Literature Review (2019, background): “In 2009, informatics (Reeves, Brown, & Laurier, 2009; Weber & Mateas, 2009) and law (Thiborg, 2009) saw their first esports-related publications.”
Autonomous agents modelling other agents: A comprehensive survey and open problems (2017, methodology): “Similarly, an agent may attempt to classify the strategy of another agent by using classifiers which were trained with statistical machine learning on data collected from recorded interactions [281].”
Experience-Driven Procedural Content Generation (2011, background): “different playing patterns within a game [72], [73], [74], [75] belong to the model-free class of gameplay-based PEM.”
A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft (2013, unknown): “One the other hand, large datasets of replays have been created [4], [5], from where strategies, trends or plans have been tried to learn.”