Global State Evaluation in StarCraft
By Graham Erickson, Michael Buro. 2014
39 citations recorded by the source corpus.
State evaluation and opponent modelling are important areasto consider when designing game-playing Artificial Intelligence.This paper presents a model for predicting whichplayer will win in the real-time strategy game StarCraft.Model weights are learned from replays using logistic regression.We also present some metrics for estimating player skillwhich can be used a features in the predictive model, includingusing a battle simulation as a baseline to compare playerperformance against.
Subfields: Artificial Intelligence, Developmental and Educational Psychology, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Pieter Abbeel
- Yoshua Bengio
- Timothy P. Lillicrap
- Yuxi Li
- Chelsea Finn
- Ruslan Salakhutdinov
- Quoc V. Le
In Developmental and Educational Psychology
- Timothy P. Lillicrap
- David Silver
- David Churchill
- Oriol Vinyals
- Sergey Bartunov
- Hado van Hasselt
- Balaraman Ravindran
- Yuxi Li
In Sociology and Political Science
Citations in the corpus
- StarCraft II: A New Challenge for Reinforcement Learning (2017, background): “, [22, 36]) or learning game outcome or build orders from replay data [9, 12].”
- Heuristic Search Techniques for Real-Time Strategy Games (2016, methodology): “UAlbertaBot and SparCraft have been used for the experimental results in recent publications on topics such as predicting RTS combat outcomes [76] [74], learning RTS combat models [85], global RTS game state evaluation [32], build placement optimization [4], high level strategy search [5], hierarchi”