Case-Based Planning and Execution for Real-Time Strategy Games

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TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games (2016, background): “Related work: Classical AI approaches normally involving planning and search [2, 15, 24, 7] are extremely challenged by the combinatorial action space and the complex dynamics of RTS games, making simulation (and thus Monte Carlo tree search) difficult [8, 22].”
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): “Ontañón et al. (2007) use the ideas of behaviors, goals, and alive-conditions from A Behavior Language (ABL, introduced by Mateas and Stern (2002)) combined with the ideas from earlier CBP systems to form a case-based system for playing Wargus.”
A Bayesian model for RTS units control applied to StarCraft (2011, background): “In real game setups, RL models have to deal with the fact that the state spaces to explore is enormous, so learning will be slow or shallow.”