StarCraft Bots and Competitions

International audienceDefinition Real-Time Strategy (RTS) games is a sub-genre of strategy games where players need to build an economy (gathering resources and building a base) and military power (training units and researching technologies) in order to defeat their opponents (destroying their army and base). Artificial Intelligence (AI) problems related to RTS games deal with the behavior of an artificial player. Since 2010, many international competitions have been organized to match AIs, or bots, playing to the RTS game StarCraft. This chapter presents a review of all major international competitions from 2010 until 2015, and details some competing StarCraft bots. State of the Art Bots for StarCraft Thanks to the recent organization of international game AI competitions fo-cused around the popular StarCraft game, several groups have been working on integrating many of the techniques developed for RTS game AI into complete "bots", capable of playing complete StarCraft games. In this chapter we will overview some of the currently available top bots, and their results of recent competitions

Learning macromanagement in starcraft from replays using deep learning

The real-time strategy game StarCraft has proven to be a challenging environment for artificial intelligence techniques, and as a result, current state-of-the-art solutions consist of numerous hand-crafted modules. In this paper, we show how macromanagement decisions in StarCraft can be learned directly from game replays using deep learning. Neural networks are trained on 789,571 state-action pairs extracted from 2,005 replays of highly skilled players, achieving top-1 and top-3 error rates of 54.6% and 22.9% in predicting the next build action. By integrating the trained network into UAlbertaBot, an open source StarCraft bot, the system can significantly outperform the game's built-in Terran bot, and play competitively against UAlbertaBot with a fixed rush strategy. To our knowledge, this is the first time macromanagement tasks are learned directly from replays in StarCraft. While the best hand-crafted strategies are still the state-of-the-art, the deep network approach is able to express a wide range of different strategies and thus improving the network's performance further with deep reinforcement learning is an immediately promising avenue for future research. Ultimately this approach could lead to strong StarCraft bots that are less reliant on hard-coded strategies.Comment: 8 pages, to appear in the proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2017

Playing Smart - Artificial Intelligence in Computer Games

Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games

Imitative learning for designing intelligent agents for video games

Over the past decades, video games have become increasingly popular and complex. Virtual worlds have gone a long way since the first arcades and so have the artificial intelligence (AI) techniques used to control agents in these growing environments. Tasks such as world exploration, constrained pathfinding or team tactics and coordination just to name a few are now default requirements for contemporary video games. However, despite its recent advances, video game AI still lacks the ability to learn. In this work, we attempt to break the barrier between video game AI and machine learning and propose a generic method allowing real-time strategy (RTS) agents to learn production strategies from a set of recorded games using supervised learning. We test this imitative learning approach on the popular RTS title StarCraft II and successfully teach a Terran agent facing a Protoss opponent new production strategies