In Real-Time Strategy (RTS) video games, players (controlled by humans or computers) build structures and recruit armies, fight for space and resources in order to control strategic points, destroy the opposing force and ultimately win the game. Players need to predict where and how the opponents will strike in order to best defend themselves. Conversely, assessing how the opponents will defend...

Case-based planning (CBP) is based on reusing past successful plans for solving new problems. CBP is particularly useful in environments where the large amount of time required to traverse extensive search spaces makes traditional planning techniques unsuitable. In particular, in real-time domains, past plans need to be retrieved and adapted in real time and efficient plan adaptation techniques...

The computer game industry has grown to a million dollar industry with new titles coming out every month. However, with all these great achievements, the video game industry does have one significant problem: games are played in similar ways. One particular genre for which this is true is the group of real time strategy games. Almost all of these games have the same structure, in which players ...

In real-time strategy game, the game artificial intelligence is not smart enough. That makes people feel boring. In this paper, we suggest a novel method about a cooperative learning of build-order improving the artificial intelligence in real-time strategy game in order to make games funny. We use the huge game replay file for it.

Computer games in general, and Real Time Strategy games in particular is a challenging task for both AI research and game AI programmers. The player, or AI bot, must use its workers to gather resources. They must be spent wisely on structures such as barracks or factories, mobile units such as soldiers, workers and tanks. The constructed units can be used to explore the game world, hunt down th...

Real-time strategy (RTS) games pose challenges to AI research on many levels, ranging from selecting targets in unit combat situations, over efficient multi-unit pathfinding, to high-level economic decisions. Due to the complexity of RTS games, writing competitive AI systems for these games requires high speed adaptive algorithms and simplified models

We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch [9]. This white paper argues for using RTS games as a benchmark for AI research, and describes the design and components of TorchCraft.

This paper presents algorithms for the automatic synthesis of real time controllers by nding a winning strategy for certain games de ned by the timed automata of Alur and Dill In such games the outcome depends on the players actions as well as on their timing We believe that these results will pave the way for the application of program synthesis techniques to the construction of real time embe...