We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft. The game state data was recorded every 3 frames which ensures suitability for a wide variety of machine learning tasks such as strategy classification, inverse reinforcement learning, im...

Design is an essential part of all games and narratives, yet designing and implementing believable game strategies can be time consuming and error-prone. This motivates the development and application of systems AI methodologies. Here we demonstrate for the first time the iterative development of agent behaviour for a real-time strategy game (here StarCraft) utilising Behaviour Oriented Design ...

Portfolio Online Evolution is a novel method for playing real-time strategy games through evolutionary search in the space of assignments of scripts to individual game units. This method builds on and recombines two recently devised methods for playing multi-action games: (1) Portfolio Greedy Search, which searches in the space of heuristics assigned to units rather than in the space of actions...

Abstract Esports offer a unique opportunity to conduct human performance studies, as they use modern hardware and software an operation platform. Insights on gameplay underlying processes may push the development of new optimal practice methods. The aim this study was investigate indicators from in-game data predict outcome matches in StarCraft II: Legacy Void. Data 6509 games (game records) pr...

The problem of comparing the performance of different Real-Time Strategy (RTS) Intelligent Agents (IA) is non-trivial. And often different research groups employ different testing methodologies designed to test specific aspects of the agents. However, the lack of a standard process to evaluate and compare different methods in the same context makes progress assessment difficult. In order to add...