William L. Raffe

Neuroevolution of content layout in the PCG: Angry bots video game

This paper demonstrates an approach to arranging content within maps of an action-shooter game. Content here refers to any virtual entity that a player will interact with during game-play, including enemies and pick-ups. The content layout for a map is indirectly represented by a Compositional Pattern-Producing Networks (CPPN), which are evolved through the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This representation is utilized within a complete procedural map generation system in the game PCG: Angry Bots. In this game, after a player has experienced a map, a recommender system is used to capture their feedback and construct a player model to evaluate future generations of CPPNs. The result is a content layout scheme that is optimized to the preferences and skill of an individual player. We provide a series of case studies that demonstrate the system as it is being used by various types of players.

A survey of procedural terrain generation techniques using evolutionary algorithms

This paper provides a review of existing approaches to using evolutionary algorithms (EA) during procedural terrain generation (PTG) processes in video games. A reliable PTG algorithm would allow game maps to be created partially or completely autonomously, reducing the development cost of a game and providing players with more content. Specifically, the use of EA raises possibilities of more control over the terrain generation process, as well as the ability to tailor maps for individual users. In this paper we outline the prominent algorithms that use EA in terrain generation, describing their individual advantages and disadvantages. This is followed by a comparison of the core features of these approaches and an analysis of their appropriateness for generating game terrain. This survey concludes with open challenges for future research.

Player-Computer Interaction Features for Designing Digital Play Experiences across Six Degrees of Water Contact

Physical games involving the use of water or that are played in a water environment can be found in many cultures throughout history. However, these experiences have yet to see much benefit from advancements in digital technology. With advances in interactive technology that is waterproof, we see a great potential for digital water play. This paper provides a guide for commencing projects that aim to design and develop digital water-play experiences. A series of interaction features are provided as a result of reflecting on prior work as well as our own practice in designing playful experiences for water environments. These features are examined in terms of the effect that water has on them in relation to a taxonomy of six degrees of water contact, ranging from the player being in the vicinity of water to them being completely underwater. The intent of this paper is to prompt forward thinking in the prototype design phase of digital water-play experiences, allowing designers to learn and gain inspiration from similar past projects before development begins.

Integrated Approach to Personalized Procedural Map Generation Using Evolutionary Algorithms

In this paper, we propose the strategy of integrating multiple evolutionary processes for personalized procedural content generation (PCG). In this vein, we provide a concrete solution that personalizes game maps in a top-down action-shooter game to suit an individual players preferences. The need for personalized PCG is steadily growing as the player market diversifies, making it more difficult to design a game that will accommodate a broad range of preferences and skills. In the solution presented here, the geometry of the map and the density of content within that geometry are represented and generated in distinct evolutionary processes, with the players preferences being captured and utilized through a combination of interactive evolution and a player model formulated as a recommender system. All these components were implemented into a test bed game and experimented on through an unsupervised public experiment. The solution is examined against a plausible random baseline that is comparable to random map generators that have been implemented by independent game developers. Results indicate that the system as a whole is receiving better ratings, that the geometry and content evolutionary processes are exploring more of the solution space, and that the mean prediction accuracy of the player preference models is equivalent to that of existing recommender system literature. Furthermore, we discuss how each of the individual solutions can be used with other game genres and content types.

Predicting player churn in destiny: A Hidden Markov models approach to predicting player departure in a major online game

Destiny is, to date, the most expensive digital game ever released with a total operating budget of over half a billion US dollars. It stands as one of the main examples of AAA titles, the term used for the largest and most heavily marketed game productions in the games industry. Destiny is a blend of a shooter game and massively multi-player online game, and has attracted dozens of millions of players. As a persistent game title, predicting retention and churn in Destiny is crucial to the running operations of the game, but prediction has not been attempted for this type of game in the past. In this paper, we present a discussion of the challenge of predicting churn in Destiny, evaluate the area under curve (ROC) of behavioral features, and use Hidden Markov Models to develop a churn prediction model for the game.

An Adaptive Training Framework for Increasing Player Proficiency in Games and Simulations

To improve a players proficiency at a particular video game, the player must be presented with an appropriate level of challenge. This level of challenge must remain relative to the player as their proficiency changes. The current fixed difficulty settings (e.g. easy, medium or hard) provide a limited range of difficulty for the player. This work aims to address this problem through developing an adaptive training framework that utilities existing work in Dynamic Difficulty Adjustment to construct an adaptive AI opponent. The framework also provides a way to measure the players proficiency, by analysing the level of challenge the adaptive AI opponent provides for the player. This work tests part of the proposed adaptive training framework through a pilot study that uses a real-time fighting game.

Learning Options for an MDP from Demonstrations

The options framework provides a foundation to use hierarchical actions in reinforcement learning. An agent using options, along with primitive actions, at any point in time can decide to perform a macro-action made out of many primitive actions rather than a primitive action. Such macro-actions can be hand-crafted or learned. There has been previous work on learning them by exploring the environment. Here we take a different perspective and present an approach to learn options from a set of experts demonstrations. Empirical results are also presented in a similar setting to the one used in other works in this area.

A dual-layer clustering scheme for real-time identification of plagiarized massive multiplayer games (MMG) assets

Theft of virtual assets in massive multiplayer games (MMG) is a significant issue. Conventional image based pattern and object recognition techniques are becoming more effective identifying copied objects but few results are available for effectively identifying plagiarized objects that might have been modified from the original objects especially in the real-time environment where a large sample of objects are present. In this paper we present a dual-layer clustering algorithm for efficient identification of plagiarized MMG objects in an environment with real-time conditions, modified objects and large samples of objects are present. The proposed scheme utilizes a concept of effective pixel banding for the first pass clustering and then uses Hausdorff Distance mechanism for further clustering. The experimental results demonstrate that our method drastically reduces execution time while achieving good performance of identification rate, with a genuine acceptance rate of 88%.

Personalized procedural map generation in games via evolutionary algorithms

In digital games, the map (sometimes referred to as the level) is the virtual environment that outlines the boundaries of play, aids in establishing rule systems, and supports the narrative. It also directly influences the challenges that a player will experience and the pace of gameplay, a property that has previously been linked to a players enjoyment of a game [1]. In most industry leading games, creating maps is a lengthy manual process conducted by highly trained teams of designers. However, for many decades procedural content generation (PCG) techniques have posed as an alternative to provide players with a larger range of experiences than would normally be possible. In recent years, PCG has even been proposed as a means of tailoring game content to meet the preferences and skills of a specific player, in what has been termed Experience-driven PCG (EDPCG) [2].

Measuring player skill using dynamic difficulty adjustment

Video games have a long history of use for educational and training purposes, as they provided increased motivation and learning for players. One of the limitations of using video games in this manner is, players still need to be tested outside of the game environment to test their learning outcomes. Traditionally, determining a players skill level in a competitive game, requires players to compete directly with each other. Through the application of the Adaptive Training Framework, this work presents a novel method to determine the skill level of the player after each interaction with the video game. This is done by measuring the effort of a Dynamic Difficult Adjustment agent, without the need for direct competition between players. The experiments conducted in this research show that by measuring the players Heuristic Value Average, we can obtain the same ranking of players as state-of-the-art ranking systems, without the need for direct competition.