Bot playing to microRTS ( and exploiting GHOST ( to solver optimization problems under uncertainty
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Compiling our bot

First, clone the develop brach of our GHOST framework with: git clone --single-branch -b develop

Enter into the GHOST folder and compile it with the following commands (you need cmake and g++ or clang): ./ (on Linux, do not forget to run "sudo ldconfig" when the script finished)

Then, build the solver required by our bot:

$> cd problem_model $> make all_rts

It will compile and place the executable "solver_cpp" in the src/ai/poadaptive folder where our bot is.

You can run the script "" to be sure everything is (locally) ok!


microRTS is a small implementation of an RTS game, designed to perform AI research. The advantage of using microRTS with respect to using a full-fledged game like Wargus or Starcraft (using BWAPI) is that microRTS is much simpler, and can be used to quickly test theoretical ideas, before moving on to full-fledged RTS games.

microRTS is deterministic and real-time (i.e. players can issue actions simultaneously, and actions are durative). It is possible to experiment both with fully-observable and partially-observable games. Thus, it is not adequate for evaluating AI techniques designed to deal with non-determinism (although future versions of microRTS might include non-determinism activated via certain flags). As part of the implementation, I include a collection of hard-coded, and game-tree search techniques (such as variants of minimax, Monte Carlo search, and Monte Carlo Tree Search).

microRTS was developed by Santiago Ontañón.

For a video of how microRTS looks like when a human plays see a youtube video

An AI competition was organized aroung microRTS in the IEEE-CIG 2017 conference, and again in 2018. For more information on the competition see the competition website

To cite microRTS, please cite this paper:

Santiago Ontañón (2013) The Combinatorial Multi-Armed Bandit Problem and its Application to Real-Time Strategy Games, In AIIDE 2013. pp. 58 - 64.


The LSI AI was contributed by Alexander Shleyfman, Antonin Komenda and Carmel Domshlak (the theory behind the AI is described in this paper.


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