Chong-U Lim

The 2014 General Video Game Playing Competition

This paper presents the framework, rules, games, controllers, and results of the first General Video Game Playing Competition, held at the IEEE Conference on Computational Intelligence and Games in 2014. The competition proposes the challenge of creating controllers for general video game play, where a single agent must be able to play many different games, some of them unknown to the participants at the time of submitting their entries. This test can be seen as an approximation of general artificial intelligence, as the amount of game-dependent heuristics needs to be severely limited. The games employed are stochastic real-time scenarios (where the time budget to provide the next action is measured in milliseconds) with different winning conditions, scoring mechanisms, sprite types, and available actions for the player. It is a responsibility of the agents to discover the mechanics of each game, the requirements to obtain a high score and the requisites to finally achieve victory. This paper describes all controllers submitted to the competition, with an in-depth description of four of them by their authors, including the winner and the runner-up entries of the contest. The paper also analyzes the performance of the different approaches submitted, and finally proposes future tracks for the competition.

Evolving behaviour trees for the commercial game DEFCON

Behaviour trees provide the possibility of improving on existing Artificial Intelligence techniques in games by being simple to implement, scalable, able to handle the complexity of games, and modular to improve reusability. This ultimately improves the development process for designing automated game players. We cover here the use of behaviour trees to design and develop an AI-controlled player for the commercial real-time strategy game DEFCON. In particular, we evolved behaviour trees to develop a competitive player which was able to outperform the game’s original AI-bot more than 50% of the time. We aim to highlight the potential for evolving behaviour trees as a practical approach to developing AI-bots in games.

An approach to general videogame evaluation and automatic generation using a description language

In this paper, we present an approach for automated evaluation and generation of videogames made with PuzzleScript, a description-based scripting language for authoring games, which was created by game designer Stephen Lavelle [1]. We have developed a system that automatically discovers solutions for a multitude of videogames that each possess different game mechanics, rules, level designs, and win conditions. In our approach, we first developed a set of general level state heuristics, which estimates how close a given game level is to being solved. It is used to adapt the best-first search algorithm to implement a general evaluation approach for PuzzleScript games called GEBestFS. Next, we developed an evolutionary framework that automatically generates novel game mechanics from scratch by evolving game design rulesets and evaluating them using GEBestFS. This was achieved by developing a set of general ruleset heuristics to assess the playability of a game based on its game mechanics. From the results of our approach, we showcase that a description-based language enables the development of general methods for automatically evaluating games authored with it. Additionally, we illustrate how an evolutionary approach can be used together with these methods to to automatically design alternate or novel game mechanics for authored games.

Computationally Modeling Narratives of Social Group Membership with the Chimeria System

Narratives are often used to form, convey, and reinforce memberships in social groups. Our system, called Chimeria, implements a model of social group membership. Here, we report upon the Chimeria Social Narrative Interface (Chimeria-SN), a component of the Chimeria system, that conveys this model to users through narrative. This component is grounded in a sociolinguistics model of conversational narrative, with some adaptations and extensions in order for it to be applied to an interactive social networking domain. One eventual goal of this work is to be able to extrapolate social group membership by analyzing narratives in social networks; this paper deals with the inverse of that problem, namely, synthesizing narratives from a model of social group membership dynamics.

Culturally-Grounded Analysis of Everyday Creativity in Social Media: A Case Study in Qatari Context

In deploying social media and other information technologies often not designed with MENA (the Middle East and North Africa) cultures in mind, users generate creative approaches to self-representation using virtual identities while preserving their cultural values. To understand and further empower such approaches, we present a mixed-method of computational and qualitative study, focusing on Qatar as a case of such communities in the MENA region. We analyzed a dataset of over 42,000 publicly available social media profiles using computational approaches (archetypal analysis) and qualitatively analyzed a separate set of 255 profiles. We augmented our descriptions with semi-structured interviews. As a result, we delineate a set of five needs/values exhibited by Qatari users supporting their creativity in effectively using virtual identities: Khaleeji features, self-expression, social connections, social monitoring, and physical and virtual identity contrasts. Finally, we propose an initial set of guidelines to support developers of virtual identity systems in better serving these users while preserving their cultural values and creative agency.

Reimagining the avatar dream: modeling social identity in digital media

Explore the limits of using the computer to imagine yourself as whomever or whatever you want to be.