Abstract Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-
playing bots or solving sequential decision problems. The method relies on intelligent tree …

This paper describes a method for generative player modeling and its application to the
automatic testing of game content using archetypal player models called procedural …

We present an approach to learn and deploy human-like playtesting in computer games
based on deep learning from player data. We are able to learn and predict the most" human" …

Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful
alternative to traditional single-objective optimization methods. They were initially applied to …

A common problem of mid-air interaction is excessive arm fatigue, known as the" Gorilla
arm" effect. To predict and prevent such problems at a low cost, we investigate user testing …

The video-game industry has recently grown from focused markets to mainstream. The
advancements the industry has been enjoying motivated researchers to propose techniques …

Video game testing has become a major investment of time, labor, and expense in the game
industry. Particularly the balancing of in-game units, characters, and classes can cause long …

Intelligent agents need to be able to make predictions about their environment. In this work
we present a novel approach to learn a forward simulation model via simple search over …

Real-Time Strategy (RTS) game unit generation is an unexplored area of Procedural
Content Generation (PCG) research, which leaves the question of how to automatically …

This paper presents a review of intrinsic motivation in player modeling, with a focus on
simulation-based game testing. Modern AI agents can learn to win many games; from a …