Competition is a primary driver of player satisfaction and engagement in multiplayer online games. Traditional matchmaking systems aim at creating matches involving teams of similar aggregated individual skill levels, such as Elo score or TrueSkill. However, team dynamics cannot be solely captured using such linear predictors. Recently, it has been shown that nonlinear predictors that target to learn probability of winning as a function of player and team features significantly outperforms these linear skill-based methods. In this paper, we show that using final score difference provides yet a better prediction metric for competitive balance. We also show that a linear model trained on a carefully selected set of team and individual features achieves almost the performance of the more powerful neural network model while offering two orders of magnitude inference speed improvement. This shows significant promise for implementation in online matchmaking systems.

Generating realistic urban environments by scattering or placing buildings on maps is a challenging problem. Unlike the existing procedural methods, we employ a data-driven approach to this problem. We combine two recent advances in machine learning techniques, Generative Latent optimization (GLO) together with adversarial training, to learn a model that can easily generate and place buildings on a given map. Such a model enables its users, particularly artists, to easily generate areas with specific styles, e.g. residential or commercial, just by providing examples. In contrast, traditional procedural methods require lengthy manual tuning of hyper-parameters. Using a more flexible method like ours allows artists to iterate over their designs of urban layouts much faster. Finally, our experiments on real-world data show that our method outperforms state-of-the-art methods in visual quality and can better match the underlying distribution of the building placements.

Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this article, we study the problem of training intelligent agents in service of game development. Unlike the agents built to “beat the game,” our agents aim to produce human-like behavior to help with game evaluation and balancing. We discuss two fundamental metrics based on which we measure the human-likeness of agents, namely skill and style, which are multifaceted concepts with practical implications outlined in this article. We report four case studies in which the style and skill requirements inform the choice of algorithms and metrics used to train agents; ranging from A* search to state-of-the-art deep reinforcement learning (RL). Furthermore, we, show that the learning potential of state-of-the-art deep RL models does not seamlessly transfer from the benchmark environments to target ones without heavily tuning their hyperparameters, leading to linear scaling of the engineering efforts, and computational cost with the number of target domains.

Training a bipedal character to play basketball and interact with objects, or a quadruped character to move in various locomotion modes, are difficult tasks due to the fast and complex contacts happening during the motion. In this paper, we propose a novel framework to learn fast and dynamic character interactions that involve multiple contacts between the body and an object, another character and the environment, from a rich, unstructured motion capture database. We use one-on-one basketball play and character interactions with the environment as examples. To achieve this task, we propose a novel feature called local motion phase, that can help neural networks to learn asynchronous movements of each bone and its interaction with external objects such as a ball or an environment. We also propose a novel generative scheme to reproduce a wide variation of movements from abstract control signals given by a gamepad, which can be useful for changing the style of the motion under the same context. Our scheme is useful for animating contact-rich, complex interactions for real-time applications such as computer games.

The digital game industry has recently adopted recommender systems to deliver the most relevant content and suggest the most suitable activities to players. Because of diverse game designs and dynamic experiences, recommender systems typically operate in highly heterogeneous and time-sensitive environments. In this paper, we describe a recommender system at a digital game company which aims to provide recommendations for a large variety of use-cases while being easy to integrate and operate. The system leverages a unified data platform, standardized context and tracking data pipelines, robust naive linear contextual multi-armed bandit algorithms, and experimentation platform for extensibility as well as flexibility. Several games and applications have successfully launched with the recommender system and have achieved significant improvements.

Achieving highly detailed terrain models spanning vast areas is crucial to modern computer graphics. The pipeline for obtaining such terrains is via amplification of a low-resolution terrain to refine the details given a desired theme, which is a time-consuming and labor-intensive process. Recently, data-driven methods, such as the sparse construction tree, have provided a promising direction to equip the artist with better control over the theme. These methods learn to amplify terrain details by using an exemplar of high-resolution detailed terrains to transfer the theme. In this paper, we propose Generative Adversarial Terrain Amplification (GATA) that achieves better local/global coherence compared to the existing data-driven methods while providing even more ways to control the theme. GATA is comprised of two key ingredients. Thefi rst one is a novel embedding of themes into vectors of real numbers to achieve a single tool for multi-theme amplification. The theme component can leverage existing LIDAR data to generate similar terrain features. It can also generate newfi ctional themes by tuning the embedding vector or even encoding a new example terrain into an embedding. The second one is an adversarially trained model that, conditioned on an embedding and a low-resolution terrain, generates a high-resolution terrain adhering to the desired theme. The proposed integral approach reduces the need for unnecessary manual adjustments, can speed up the development, and brings the model quality to a new level. Our implementation of the proposed method has proved successful in large-scale terrain authoring for an open-world game.

The digital game industry has recently adopted recommendation systems to provide suitable game and content choices to players. Recommendations in digital games have several unique applications and challenges compared to other well known recommendation system such as those for movies and books. Designers must adopt different architectures and algorithms to overcome these challenges. In this talk, we describe the game recommendation system at Electronic Arts. It leverages heterogeneous player data across many games to provide intelligent recommendations. We discuss three example applications: recommending games for purchase, suitable game map, and game difficulty.

Video game crash events are characterized primarily by large media payloads and by highly bursty traffic patterns, with hundreds of thousands or millions of reports being issued in only a few minutes. These events are invaluable in quickly responding to game breaking issues that directly impact user experience. Even the slightest delay in capturing, processing and reporting these events can lead to user abandonment and significant financial cost.

Matchmaking connects multiple players to participate in online player-versus-player games. Current matchmaking systems depend on a single core strategy: create fair games at all times. These systems pair similarly skilled players on the assumption that a fair game is best player experience. We will demonstrate, however, that this intuitive assumption sometimes fails and that matchmaking based on fairness is not optimal for engagement.

Large organizations have a multitude of data sources across the enterprise and want to obtain business value from all of them. While the majority of these data sources may be consolidated in an enterprise data warehouse, many business units have their own data marts where analysis is carried out against data stored in multidimensional data structures. It is often critical to pose queries which span both these sources. This is a challenge since these sources have differing models and query languages (SQL vs MDX). The Siebel Analytics Server enables this requirement to be fulfilled. In this paper, we describe how the multidimensional metadata is modeled relationally within Siebel Analytics, efficient SQL to MDX translation algorithms and the conversion protocols required to convert a multidimensional result into a relational rowset.

Datacube queries compute aggregates over database relations at a variety of granularities. Often one wants only datacube output tuples whose aggregate value satisfies a certain condition, such as exceeding a given threshold. We develop algorithms for processing a datacube query using the selection condition internally during the computation. Thus, we can safely prune parts of the computation and end up with a more efficient computation of the answer Our first technique, called "specialization", uses the fact that a tuple in the datacube does not meet the given threshold to infer that all finer level aggregates cannot meet the threshold. Our second technique is called "generalization", and applies in the case where the actual value of the aggregate is not needed in the output, but used just to compare with the threshold. We demonstrate the efficiency of these techniques by implementing them within the sparse datacube algorithm of Ross and Srivastava. We present a performance study using synthetic and real-world data sets. Our results indicate substantial performance improvements for queries with selective conditions.

Existing datacube precomputation schemes materialize selected datacube tuples on disk, choosing the most beneficial cuboids (i.e., combinations of dimensions) to materialize given a space limit. However in the context of a data-warehouse receiving frequent "append" updates to the database, the cost of keeping these disk-resident cuboids up-to-date can be high. In this paper we propose a main memory based framework which provides rapid response to queries and requires considerably less maintenance cost than a disk based scheme in an append-only environment. For a given datacube query, we first look among a set of previously materialized tuples for a direct answer. If not found, we use a hash based scheme reminiscent of partial match retrieval to rapidly compute the answer to the query from the finest-level data stored in a special in-memory data structure. Our approach is limited to the important class of applications in which the finest granularity tuples of the datacube fit in main memory. We present analytical and experimental results demonstrating the benefits of our approach.