Vanessa Volz

Demonstrating the Feasibility of Automatic Game Balancing

Game balancing is an important part of the (computer) game design process, in which designers adapt a game prototype so that the resulting gameplay is as entertaining as possible. In industry, the evaluation of a game is often based on costly playtests with human players. It suggests itself to automate this process using artificial players for the prediction of gameplay and outcome. In this paper, the feasibility of automatic balancing is investigated for the card game top trumps using simulation- and deck-based objectives. Additionally, the necessity of a multi-objective approach is asserted by assessing the only published (single-objective) method. We apply a multi-objective evolutionary algorithm to obtain decks that optimise objectives developed to express the fairness and the excitement of a game of top trumps, e.g. win rate and average number of tricks. The results are compared with decks from published top trumps games using the aforementioned objectives. The possibility to generate decks better or at least as good as decks from published top trumps decks in terms of these objectives is demonstrated. Our results indicate that automatic balancing with the presented approach is feasible even for more complex games such as real-time strategy games.

MCTS/EA hybrid GVGAI players and game difficulty estimation

In the General Video Game Playing competitions of the last years, Monte-Carlo tree search as well as Evolutionary Algorithm based controllers have been successful. However, both approaches have certain weaknesses, suggesting that certain hybrids could outperform both. We envision and experimentally compare several types of hybrids of two basic approaches, as well as some possible extensions. In order to achieve a better understanding of the games in the competition and the strength and weaknesses of different controllers, we also propose and apply a novel game difficulty estimation scheme based on several observable game characteristics.

An integrated process for game balancing

Game balancing is a recurring problem that currently requires a lot of manual work, usually following a game designers intuition or rules-of-thumb. To what extent can or should the balancing process be automated? We establish a process model that integrates both manual and automated balancing approaches. Artificial agents are employed to automatically assess the desirability of a game. We demonstrate the feasibility of implementing the model and analyze the resulting solutions from its application to a simple video game.

Evolving mario levels in the latent space of a deep convolutional generative adversarial network

Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video games could benefit from such models, especially for games where there is a pre-existing corpus of levels to emulate. This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus. The approach successfully generates a variety of levels similar to one in the original corpus, but is further improved by application of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Specifically, various fitness functions are used to discover levels within the latent space of the GAN that maximize desired properties. Simple static properties are optimized, such as a given distribution of tile types. Additionally, the champion A* agent from the 2009 Mario AI competition is used to assess whether a level is playable, and how many jumping actions are required to beat it. These fitness functions allow for the discovery of levels that exist within the space of examples designed by experts, and also guide the search towards levels that fulfill one or more specified objectives.

Surrogate-Assisted Partial Order-Based Evolutionary Optimisation

In this paper, we propose a novel approach SAPEO to support the survival selection process in evolutionary multi-objective algorithms with surrogate models. The approach dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population. We introduce multiple SAPEO variants that differ in terms of the uncertainty they allow for survival selection and evaluate their anytime performance on the BBOB bi-objective benchmark. In this paper, we use a Kriging model in conjunction with an SMS-EMOA for SAPEO. We compare the obtained results with the performance of the regular SMS-EMOA, as well as another surrogate-assisted approach. The results open up general questions about the applicability and required conditions for surrogate-assisted evolutionary multi-objective algorithms to be tackled in the future.

Investigating uncertainty propagation in surrogate-assisted evolutionary algorithms

Uncertainty propagation is a technique to incorporate individuals with uncertain fitness estimates in evolutionary algorithms. The Surrogate-Assisted Partial Order-Based Evolutionary Optimisation Algorithm (SAPEO) uses uncertainty propagation of fitness predictions from a Kriging model to reduce the number of function evaluations. The fitness predictions are ranked with partial orders and the corresponding individuals are only evaluated if they are indistinguishable otherwise or the risk of uncertainty propagation exceeds a steadily decreasing error tolerance threshold. In this paper, we investigate the effects of using uncertainty propagation according to SAPEO on single-objective problems. To this end, we present and apply different ways of measuring the deviations of SAPEO from the underlying CMA-ES. We benchmark the algorithms on the BBOB testbed to assess the effects of uncertainty propagation on their performance throughout the runtime of the algorithm on a variety of problems. Additionally, we examine thoroughly the differences per iteration between the evolution paths of SAPEO and CMA-ES based on a model for the rank-one update. The BBOB results suggest that the success of SAPEO generally improves the performance but depends heavily on function and dimension, which is supported by the analysis of the evolution paths.

Measuring strategic depth in games using hierarchical knowledge bases

This paper presents a measure intended to quantify the relative strategic depth of games as experienced by human players. The measure is based on the complexity (number and specificity of rules) of a hierarchical knowledge base that is extracted from playtraces. As a proof-of-concept, we compute the proposed measure for three arcade-style games and compare the results to the strategic depth reportedly perceived by human players in a survey. We find that our measure is practicable and able to capture the differences in strategic depth very accurately.

ACM-W scholarships

ACM-W provides support for women undergraduate and graduate students in Computer Science and related programs to attend research conferences. This exposure to the CS research world can encourage a student to continue on to the next level (Undergraduate to Graduate, Masters to Ph.D, Ph.D. to an industry or academic position). The student does not have to present a paper at the conference she attends.

GECCO'16 Student Workshop Chairs' Welcome & Organization

The workshop is a joint event where undergraduate and graduate students can present their scientific work. Additionally, we aim to provide the students with useful feedback on their papers, their presentations, and possible future research directions. To this end, we encouraged detailed reviews of the papers from our program committee. More importantly, we were able to engage three established researchers for our mentor panel, who have access to all accepted papers and will attend the presentations, so that they are in an optimal position to formulate constructive criticism during the designated discussion periods.