Video games are a staple of today's popular We would like to thank the members of our Program culture, and have been called the preeminent Committee for their service and the AAAI form of entertainment of the 21st century. Artificial Staff, particularly Carol McKenna Hamilton and intelligence is a major component contributing to Keri Harvey, who helped organize the conference this success, while creating some of the most difficult and were a pleasure to work with.

Autonomous agents in real-time strategy (RTS) games lack an integrated framework for reasoning about choke points and regions of open space in their environment. This paper presents an algorithm which partitions the environment into a set of polygonal regions and computes optimal choke points between adjacent regions. This representation can be used as a component for AI agents to reason about terrain, plan multiple routes of attack, and make other tactical decisions. The algorithm is tested on a set of popular maps commonly used in international Starcraft competitions and evaluated against answers made by human participants. The algorithm identified 97% of the choke points that the participants found and also identified a number of bottlenecks that human participants did not recognize as choke points.

This paper proposes an automatic model-based approach that enables adaptive decision making in modern virtual games. It builds upon the Integrated MDP and POMDP Learning AgeNT (IMPLANT) architecture which has shown to provide plausible adaptive decision making in modern games. However, it suffers from highly time-consuming manual model specification problems. By incorporating an automated priority sweeping based model builder for the MDP, as well as using the Tactical Agent Personality for the POMDP, the work in this paper aims to resolve these problems. Empirical proof of concept is shown based on an implementation in a modern game scenario, whereby the enhanced IMPLANT agent is shown to exhibit superior adaptation performance over the old IMPLANT agent whilst eliminating manual model specifications and at the same time still maintaining plausible speeds.

Realistic simulations of the movement of infantry in urban environments with 3D models and at interactive rates is of wide and enduring interest. Many video games have attempted it, and military simulations are increasingly doing the same. Previous attempts have fallen short in two areas: properly coordinating movement, and adequate modeling of the detection of hostile targets. Novel algorithms to simulate fireteam movement and visual scanning appropriate to urban environments are described. Measurements of the computational performance of the most expensive components of the approach are provided.

Our goal is to enable Non Player Characters (NPC) in computer games to exhibit natural behaviors. The quality of behaviors affects the game experience especially in story-based games, which rely on player-NPC interactions. We used Reinforcement Learning to enable NPC companions to develop preferences for actions. We implemented our RL technique in BioWare Corp.’s Neverwinter Nights. Our experiments evaluate an NPC companion’s behaviors regarding traps. Our method enables NPCs to rapidly learn reasonable behaviors and adapt to changes in the game.

Chris Jurney (Lead Programmer, Double Fine Productions) Sumit Basu (Microsoft Research) Chris Jurney is a rock and roll experimental game For those who can play an instrument or have a respectable programmer at Double Fine Productions, with 11 singing voice, music can be a wonderful years experience in games and simulation. He has means of creative expression, social engagement, shipped 4 titles in the games industry: Company of and fun. For many others, though, it can be frustrating Heroes, Frontline: Fuel of War, Dawn of War 2, and and inaccessible: even if an inspired youth Brutal Legend. Jurney frequently speaks on the topic has great musical ideas, she may not have the of game AI, having presented at the Game Developers knowledge or ability to get her latest song out from Conference (GDC), GDC China, Columbia her head and into her MP3 player. In this talk, Basu will show three vignettes of how he and his colleagues University, the University of Pennsylvania, and the have used interactive machine learning to New Jersey and Philadelphia chapters of the International extend the creative reach of aspiring musicians: a Game Developers Association (IGDA).

Players begin games at different skill levels and develop their skill at different rates—so that even the best-designed games are uninterestingly easy for some players and frustratingly difficult for others. A proposed answer to this challenge is Dynamic Difficulty Adjustment (DDA), a general category of approaches that alter games during play, in response to player performance. However, nearly all these techniques are focused on basic parameter tweaking, while the difficulty of many games is connected to aspects that are more challenging to adjust dynamically, such as level design. Further, most DDA techniques are based on designer intuition, which may not reflect actual play patterns. Responding to these challenges, we have created Polymorph, which employs techniques from level generation and machine learning to understand level difficulty and player skill, dynamically constructing levels for a 2D platformer game with continually-appropriate challenge. We present the results of the user study on which Polymorph's model of level difficulty is based, as well as a discussion of the unique features of the model. We believe Polymorph creates a play experience that is unique because the changes are both personalized and structural, while also providing an example of a new application of machine learning to aid game design.

Procedural content generation is becoming more and more relevant to solve the problem of content creation for the ever growing virtual worlds of games, simulations and other applications. However, these procedures are often unintuitive or use vague parameters, making it somewhat difficult for a designer to express his or her creative intent. Even worse, most of these techniques lack an accessible and easy to use interface.We have developed a generic layout solving approach to automatically create sensible content for virtual worlds. In that context, this paper proposes a high-level scene description language that allows designers to specify particular types of scenes. This description language allows designers to easily specify which objects need to be present in a scene, their attributes, and possible interrelationships. Application of the language, based on the rich vocabulary taken from a semantic library, is illustrated with several examples, showing its flexibility, intuitiveness and ease of use.

This research aims at building a preference-based player model of Civilization IV players. Our model incorporates attributes which are defined for AI players. We use a sequential minimal optimization (SMO) classifier to build the player model based on a training set with observations of a large number of games between six AI players. The model was validated on a test set of games between the same six AI players. While it did not seem to generalize well to the preferences of different AI players, it did manage to accurately predict some of the preferences for a veteran human player. Further tests showed that AI players with the same play styles but different preference values were often confused by the model. We conclude that for a complex game such as Civilization IV a model that attempts to accurately predict specific preference values is hard to construct. A model that focusses on play styles might succeed better.

Game environments are becoming more and more mutable from the actions of both Players and Non Player Characters (NPCs). However, current generation AI agents do not take advantage of the tactical abilities these mutable worlds provide. We propose a method to make the game agents aware of the mutability of the world by extending their repertoire of abilities to include world alteration commands and some evaluation functions, which determine when and where to alter the world for the greatest tactical gain. Primarily, our work focuses on the Adversarial Navigation Mesh Alteration (ANMA) algorithm, which evaluates potential changes to the map in adversarial environments from an attacker and defender point of view. We present an empirical evaluation of the ANMA algorithm in a Capture The Flag (CTF) simulation environment with several teams of agents. One group of agents (adaptive) lacks the ability to initiate world deformations, but they can respond and re-plan to take advantage of world modifications. The second team of agents (builders) can only generate additional paths through the world using the attacker portion of ANMA. The third team of agents (universal) is able to fully deform the world by generating new paths or removing existing paths using both the attacker and defender sections of ANMA. We evaluated these teams and observed that builder agents beat adaptive agents at a rate of 1.33 to 1. The more advanced universal agents beat adaptive agents at a rate of 2.75 to 1 and builder agents 1.4 to 1.