Abstract-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.

Abstract. 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.

No abstract

The following figure and table are both are in the article subsection: Analyzing users: Revealing user-enacted values. Figure 9 is in the sub-subsection Making identify expression from player data. This table, based on data collected with the Heroes of Elibca interface implemented using our AIRvatar system, helps to demonstrate user-enacted biases when customizing player characters. Table 1 is in the subsubsection Examples of user-enacted biases. This table helps to elucidate the user-customized attribute data collected as reported in this article.

A great deal of research has gone into understanding the relationships between social behaviors, player preferences, and identities in both real-world and virtual environments. In this works-in-progress report, we describe AIRvatar, a tool that telemetrically collects data such as session-based time durations and click events during the process of avatar customization within a videogame of our own creation. We present results from a user-study of 181 players, highlighting how social phenomena such as gender-related stereotypes of users can be revealed, particularly when players' self-identified real world genders contrast with the gender identities of their avatars.