For large state-space Markovian Decision Problems Monte-Carlo planning is one of the few
viable approaches to find near-optimal solutions. In this paper we introduce a new algorithm …

A Monte-Carlo evaluation consists in estimating a position by averaging the outcome of
several random continuations. The method can serve as an evaluation function at the leaves …

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 …

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 …

Deep reinforcement learning (RL) works impressively in some environments and fails
catastrophically in others. Ideally, RL theory should be able to provide an understanding of …

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

Immersive computer-generated environments (aka virtual reality, VR) are limited by the
physical space around them, eg, enabling natural walking in VR is only possible by …

Real-time strategy games offer a wide variety of fundamental AI research challenges. Most
of these challenges have applications outside the game domain. This paper provides a …

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 Multiplayer Online Battle Arena (MOBA) games rely primarily on combat to
determine the ultimate outcome of the game. Combat in these types of games is highly …