This literature review covers AI techniques used for real-time strategy video games, focusing
specifically on StarCraft. It finds that the main areas of current academic research are in …

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the
precision of tree search with the generality of random sampling. It has received considerable …

This paper presents an overview of the existing work on AI for real-time strategy (RTS)
games. Specifically, we focus on the work around the game StarCraft, which has emerged in …

In general, games pose interesting and complex problems for the implementation of
intelligent agents and are a popular domain in the study of artificial intelligence. In fact …

A new paradigm for search, based on Monte-Carlo simulation, has revolutionised the
performance of computer Go programs. In this article we describe two extensions to the …

Plan recognition, activity recognition, and intent recognition together combine and unify
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …

The ancient oriental game of Go has long been considered a grand challenge for artificial
intelligence. For decades, computer Go has defied the classical methods in game tree …

We present a data mining approach to opponent modeling in strategy games. Expert
gameplay is learned by applying machine learning techniques to large collections of game …

Game tree search in games with large branching factors is a notoriously hard problem. In
this paper, we address this problem with a new sampling strategy for Monte Carlo Tree …

Abstract Domain knowledge is crucial for effective performance in autonomous control
systems. Typically, human effort is required to encode this knowledge into a control …