Game-Tree Search over High-Level Game States in RTS Games
By Alberto Uriarte, Santiago Ontañón. 2014
44 citations recorded by the source corpus.
From an AI point of view, Real-Time Strategy (RTS) games are hard because they have enormous state spaces, they are real-time and partially observable. In this paper, we present an approach to deploy game-tree search in RTS games by using game state abstraction. We propose a high-level abstract representation of the game state, that significantly reduces the branching factor when used for game-tree search algorithms. Using this high-level representation, we evaluate versions of alpha-beta search and of Monte Carlo Tree Search (MCTS). We present experiments in the context of StarCraft showing promising results in dealing with the large branching factors present in RTS games.
Subfields: Artificial Intelligence, 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
- Gabriel Synnaeve
- Nicolas Usunier
- Nantas Nardelli
- Zeming Lin
- Shimon Whiteson
- Yoshua Bengio
- Timothée Lacroix
- Soumith Chintala
In Sociology and Political Science
- Nantas Nardelli
- Zeming Lin
- Nicolas Usunier
- Gabriel Synnaeve
- Soumith Chintala
- Shimon Whiteson
- Gregory Farquhar
- David Churchill
Citations in the corpus
- 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].”