Game AI Research Articles

AIIDE 2012 StarCraft Competition

In 2012, AIIDE will host the Third Annual StarCraft AI Competition. Participants are given the task of building the best performing AI system for the popu- lar real-time strategy game StarCraft Brood War, which was released by Blizzard Entertainment in 1998. The goals of the competition are to provide a testbed for real-time AI systems and to promote game AI research by exhibiting AI techniques such as scripting, planning, optimization, spatial reasoning, and opponent modeling in a fast-paced popular video game.

AIIDE 2013 StarCraft Competition

In 2013, AIIDE will host the Fourth Annual Star-Craft AI Competition. Participants are given the task of building the best performing AI system for the popular real-time strategy game StarCraft Brood War (Blizzard Entertainment). Thee goals of the competition are to provide a testbed for real-time AI systems and to promote game AI research by exhibiting AI techniques such as scripting, planning, optimization, spatial reasoning, and opponent modeling in a fast-paced popular video game.

Development of the AI-based Experiential Education Program Connected to the SW Education

이 연구의 목적은 초등교육과정의 SW 교육과 연계한 인공지능 기반의 체험교육 프로그램을 개발하는 것이다. 이를 위하여 인공지능의 다양한 용어와 교육 관련 내용을 살펴보고, 인공지능 관련 내용을 SW 교육과 연계하여 적용할 수 있는 분야를 탐색하였다. 현재 초등교육 과정에서 사용하는 실과 교과서의 로봇 및 SW (관련) 단원에서 인공지능과 연계할 수 있는 내용을 분석하였다. 이를 기초로 현재 초등 실과 교육과정에서 로봇 교육 및 SW 교육과 연계할 수 있는 엔트리 SW의 다양한 인공지능의 관련 내용을 파악하였고, 이를 기반으로 초등 실과교육에서 로봇 교육과 SW 교육과 연계한 인공지능 기반 체험 교육프로그램을 5개 과정으로 선정하였다. 5개 과정의 체험교육 프로그램은 ㈜네오피아와 함께 개발한 자율주행 자동차의 체험교육 프로그램, 생체 인식 체험교육 프로그램, IoT의 생활 체험교육 프로그램, AI 플레이어 체험교육 프로그램, 머신러닝 체험교육 프로그램이다. 이러한 체험 교육프로그램을 로봇 교육 및 SW 교육과 연계한 인공지능 체험교육 프로그램을 구안하여 학습지도안 등과 함께 제시하였고, 다양한 웹 버전의 제공자와 함께 운영될 수 있게 하였다. 이러한 자료는 교육대학의 예비교사와 현직 교사들에게 필요한 인공지능에 대한 기본 지식과 이해에 필요한 내용과 체험교육용 자료로 제공할 수 있을 것이다. 또한 이러한 자료는 초등현장에서 필요한 SW 교육과 연계한 인공지능 관련 교육을 위한 지침으로써 인공지능 교육의 현장 적용이 가능한 수업자료와 체험교육 프로그램으로 제공될 수 있을 것이다. 개발된 프로그램을 예비교사와 현장에 적용하여 인공지능 관련 체험교육을 진행하는데 발생하는 문제점을 수정하고 보완하여 더 완성도 높은 프로그램이 되기를 기대한다.The purpose of this study is to develop an AI-based experiential education program that is connected to the SW education of the elementary school curriculum. To do so, various terms and education-related contents of AI were examined, and the fields that could be applied by linking AI-related contents with SW education were explored. The contents that can be linked to AI in the robot unit and SW unit of the practical arts textbook currently used in the elementary education field were analyzed. Using this knowledge as a foundation, the contents of various AIs provided that can be used by utilizing the entry SW linked to robot and SW education in the current experiential education course were compared. Based on this, an AI-based experiential education program that is linked to robot education and SW education was selected into five courses. The five experiential education programs are as follows: an autonomous vehicle experiential education program developed with Neopia, a biometrics experiential education program, an Internet of Things (IoT), life experiential education program, an AI player experiential education program, and a machine learning experiential education program. These developed programs will be able to provide the necessary basic knowledge and understanding of AI as well as experiential education and practice to the teachers and pre-service teachers of the University of Education. In addition, as a guideline for AI-related education in connection with SW required in the elementary field, it can be provided as instructional materials and experiential education programs that can be applied to the field of AI education. In the future, it is expected that the developed program will be applied to pre-service teachers and on-site to correct and supplement the problems that occur in AI-related experiential education.

Organizers

List of organizers of the Experimental Artificial Intelligence in Games 2014 AIIDE Workshop.

Preface

It is our pleasure to present to you the three papers that were accepted for presentation at this second AIIDE workshop on AI for adversarial real-time games, covering building placement optimization, sequential pattern mining for achieving short and long-term goals, and high-level representation for search in RTS games.

Game AI Planning Analytics: The Case of Three First-Person Shooters

We present a general framework for Game Artificial Intelligence Planning (AIP) Analytics. The objective is to provide analytic tools to study and improve AIP components and their use in video-games. Extraction and formatting of AI data is first described and discussed. Then AIP metrics are listed with examples and illustrations from three popular First-Person Shooters: F.E.A.R. (2005), KillZone 3 (2011) and Transformers 3: Fall of Cybertron (2012). The patterns we discovered in our study clearly show the AIP component is called more often by the game over the years.

Walling in Strategy Games via Constraint Optimization

This paper presents a constraint optimization approach to walling in real-time strategy (RTS) games. Walling is a specific type of spatial reasoning, typically employed by human expert players and not currently fully exploited in RTS game AI, consisting on finding configurations of buildings to completely or partially block paths. Our approach is based on local search, and is specifically designed for the real-time nature of RTS games. We present experiments in the context of the RTS game StarCraft showing promising results.

AIIDE 2014 StarCraft Competition

In 2014, AIIDE will host the Fifth Annual StarCraft AI Competition. Participants are given the task of building the best performing AI system for the popular real-time strategy game StarCraft Brood War (Blizzard Entertainment). The goals of the competition are to provide a testbed for real-time AI systems and to promote game AI research by ex- hibiting AI techniques such as scripting, planning, optimization, spatial reasoning, and opponent modeling in a fast-paced popular video game.

"Press Space to Fire": Automatic Video Game Tutorial Generation

We propose the problem of tutorial generation for games, i.e. to generate tutorials which can teach players to play games, as an AI problem. This problem can be approached in several ways, including generating natural language descriptions of game rules, generating instructive game levels, and generating demonstrations of how to play a game using agents that play in a human-like manner. We further argue that the General Video Game AI framework provides a useful testbed for addressing this problem.

Improving Terrain Analysis and Applications to RTS Game AI

This paper presents a new terrain analysis algorithm for RTS games. The proposed algorithms significantly improves the analysis time of the state of the art via contour tracing, and also offers better chokepoint detection. We demonstrate that our approach (BWTA2) is at least 10 times faster than the commonly used BWTA in a collection of StarCraft maps. Additionally, we show the usefulness of terrain analysis in tasks such as pathfinding and discuss potential applications to strategic decision making tasks.

Tuning Belief Revision for Coordination with Inconsistent Teammates

Coordination with an unknown human teammate is a notable challenge for cooperative agents. Behavior of human players in games with cooperating AI agents is often sub-optimal and inconsistent leading to choreographed and limited cooperative scenarios in games. This paper considers the difficulty of cooperating with a teammate whose goal and corresponding behavior change periodically. Previous work uses Bayesian models for updating beliefs about cooperating agents based on observations. We describe belief models for on-line planning, discuss tuning in the presence of noisy observations, and demonstrate empirically its effectiveness in coordinating with inconsistent agents in a simple domain. Further work in this area promises to lead to techniques for more interesting cooperative AI in games.

Automated Gameplay Generation from Declarative World Representations

An open area of research for AI in games is how to provide unique gameplay experiences that present specialized game content to users based on their preferences, in-game actions, or the system's goals. The area of procedural content generation (PCG) focuses on creating or modifying game worlds, assets, and mechanics to generate tailored or personalized game experiences. Similarly, the area of interactive narrative (IN) focuses on creating or modifying story worlds, events, and domains to generate tailored or personalized story experiences. In this paper we describe a game engine that utilizes a PCG pipeline to generate and control a range of gameplay experiences from an underlying IN experience management construct.

Automated Decomposition of Game Maps

Video game worlds are getting increasingly large and complex. This poses challenges to the game AI for both pathfinding and strategic decisions, not least in real-time strategy games. One way to alleviate the problem is to manually pre-label the game maps with information about regions and critical choke points, which the game AI can then take advantage of. We present a method for automatically decomposing game maps into non-uniform sized regions. The method uses a flooding algorithm at its core and has the benefit, in addition to its effectiveness, to be relatively intuitive both conceptually and in implementing. Empirical evaluation on game maps shows that the automatic decomposition results in intuitive regions of a comparable standard to human-made labeling. Furthermore, we show that our automatic decomposition, when used by a pathfinding algorithm capable of taking advantage of pre-labeled regions, significantly improves search effectiveness.

Hierarchical Portfolio Search: Prismata's Robust AI Architecture for Games with Large Search Spaces

Online strategy video games offer several unique challenges to the field of AI research. Due to their large state and action spaces, existing search algorithms have difficulties in making strategically strong decisions. Additionally, the nature of competitive on-line video games adds the requirement that game designers be able to tweak game properties regularly when strategic imbalances are found. This means that an AI system for a game like this needs to be robust to such changes and less reliant on expert knowledge. This paper makes two main contributions to advancing the state of the art for AI in modern strategy video games which have large state and action spaces. The first is a novel method for performing hierarchical search using a portfolio of algorithms to reduce the search space while maintaining strong action candidates. The second contribution is an overall AI architecture for strategy video games using this portfolio search method. The proposed methods are used as the AI system for Prismata, an online turn-based strategy game by Lunarch Studios. This system is evaluated using three experiments: on-line play vs.~human players, off-line AI tournaments to test the relative strengths of the AI bots, and a survey to determine user satisfaction of the system so far. Our result show that this system achieves a skill level in the top 25% of human players on the ranked ladder, can be modified quickly to create different difficulty settings, is robust to changes in game unit properties, and creates an overall AI experience which is user rated more enjoyable than those currently found in similar video games.

Team Sports for Game AI Benchmarking Revisited

Sport games are among the oldest and best established genres of computer games. Sport-inspired environments, such as RoboCup, have been used for AI benchmarking for years. We argue that, in spite of the rise of increasingly more sophisticated game genres, team sport games will remain an important testbed for AI benchmarking due to two primary factors. First, there are several genre-specific challenges for AI systems that are neither present nor emphasized in other types of games, such as team AI and frequent replanning. Second, there are unmistakable nonskill-related goals of AI systems, contributing to player enjoyment, that are most easily observed and addressed within a context of a team sport, such as showing creative and emotional traits. We analyze these factors in detail and outline promising directions for future research for game AI benchmarking, within a team sport context.

A Computer-Based Method for the Investigation of Human Behavior in the Iterative Chicken Game.

The present study develops an artificial agent that plays the iterative chicken game based on a computational model that describes human behavior in competitive social interactions in terms of fairness. The computational model we adopted in this study, named as the self-concept fairness model, decides the agent’s action according to the evaluation of fairness of both opponent and self. We implemented the artificial agent in a computer program with a set of parameters adjustable by researchers. These parameters allow researchers to determine the extent to which the agent behaves aggressively or cooperatively. To demonstrate the use of the proposed method for the investigation of human behavior, we performed an experiment in which human participants played the iterative chicken game against the artificial agent. Participants were divided into two groups, each being informed to play with either a person or the computer. The behavioral analysis results showed that the proposed method can induce changes in the behavioral pattern of human players by changing the agent’s behavioral pattern. Also, we found that participants tended to be more sensitive to fairness when they played with a human opponent than with a computer opponent. These results support that the artificial agent developed in this study will be useful to investigate human behavior in competitive social interactions.

Knocking in the Game of Gin Rummy

We perform an empirical study of Gin Rummy knocking strategies, drawing insight from a population of AI players that vary in both discarding and knocking strategies. For our best performing player, simple linear regression yielded a knocking strategy that both affirmed the features expert players give attention to in making knock decisions, and yet called into question the way such features are conventionally used.

Game Ai Using Parametrized Behavior Monitoring

Game AI with a Non-Player Controlled Bots is the systems that are built into a game. These systems offer competition or objectives that the player must overcome to progress. Although in most situations, these NPCs provide an acceptable level of competition, over time, they become predictable and no longer pose any challenge to the players. Game AIs and NPCs use a basic detection and computational system to perform actions that seem to oppose the players, and these are even lesser of an opposition to players and will lead to loss of worth in the section of the game that would be more enjoyable to those if the opposition would be more challenging.

A Rule-Based AI Method for an Agent Playing Big Two

Big Two is a popular multiplayer card game in Asia. This research proposes a new method, named rule-based AI, for an agent playing Big Two. The novel method derives rules based on the number of cards the AI agent has left and the control position. The rules for two to four cards left are used to select the card combination to discard based on the number of cards remaining in the agent’s hand. The rules for more than four cards left conditionally prioritize discarding the card combination in the classified cards with lower priority. A winning strategy provides guidelines to guarantee that the AI agent will win when a win is achievable within three moves. We also design the rules for the AI agent without control for holding cards and splitting cards. The experimental results show that our proposed AI agent can play Big Two well and outperform randomized AI, conventional AI, and human players, presenting winning rates of 89.60%, 73.00%, and 55.05%, respectively, with the capability of maximizing the winning score and minimizing the number of cards left when the chance of winning is low.

Modelling and Control of Trojan Propagation via Online Game Accelerators

The widespread use of online game accelerators also induces them to become a medium for hackers to spread Trojan horses. In this paper, we propose a novel compartment model which considered the heterogeneity of online computer game players aiming to characterize the Trojan propagation. Specifically, we distinguish rational game players from impulsive game players in our model. The spreading threshold is obtained, and the global stability of equilibrium is also verified. Moreover, Trojan’s control problem is studied by using Pontryagin’s maximum principle. Numerical results confirm the stability of the system and the effectiveness of the optimal control strategy. Besides, more numerical results also show that some control strategies such as warning and caution should be taken at the very beginning of game player downloading the malicious accelerator.