Neuroevolution in Games: State of the Art and Open Challenges

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyse the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The article also highlights important open research challenges in the field.Comment: - Added more references - Corrected typos - Added an overview table (Table 1

Effective Task Transfer Through Indirect Encoding

An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To this end, (1) the bird’s eye view (BEV) representation is introduced, which can represent different tasks on the same two-dimensional map. Because the BEV represents state information associated with positions instead of objects, it can be scaled to more objects without manipulation. In this way, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV, which is (2) demonstrated in this dissertation. Yet a challenge for such representation is that a raw two-dimensional map is highdimensional and unstructured. This dissertation demonstrates how this problem is addressed naturally by the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach. HyperNEAT evolves an indirect encoding, which compresses the representation by exploiting its geometry. The dissertation then explores further exploiting the power of such encoding, beginning by (3) enhancing the configuration of the BEV with a focus on iii modularity. The need for further nonlinearity is then (4) investigated through the addition of hidden nodes. Furthermore, (5) the size of the BEV can be manipulated because it is indirectly encoded. Thus the resolution of the BEV, which is dictated by its size, is increased in precision and culminates in a HyperNEAT extension that is expressed at effectively infinite resolution. Additionally, scaling to higher resolutions through gradually increasing the size of the BEV is explored. Finally, (6) the ambitious problem of scaling from the Keepaway task to the Half-field Offense task is investigated with the BEV. Overall, this dissertation demonstrates that advanced representations in conjunction with indirect encoding can contribute to scaling learning techniques to more challenging tasks, such as the Half-field Offense RoboCup soccer domain

Evolving Intelligent Multimodal Gameplay Agents and Decision Makers with Neuroevolution

�Super Mario Bros� is a difficult platforming game that requires the use of multiple behavioral modes to complete different gameplay elements such as: collecting coins, dodging enemies and getting to the end of the level. Methods for creating intelligent game playing agents have previously used human designed behavior policy for each gameplay state or by combining gameplay goals into a single task to be learned. This thesis assesses the development and method of training machines to promote multiple modes of behavior within neural network controllers. These controllers utilize the concept of evolution through multi-objective optimization for the test bench platform game system �MarioAI�. Artificial neural networks were evolved to exhibit complex and multimodal behavior using multiple sub objectives of the game; and thus overcome the non-linear, noisy, and fractured game environment. Experiments were conducted with the purpose of creating multiple Pareto-optimal solutions of quality with differing behavioral aspects. These solutions were then discerned by a Decision Maker Neural Network Ensemble that had been evolved to pick the best solution according to game level. This Decision Maker Ensemble proved to be able to learn on minimal information and provide the highest overall game score. The results of this thesis show that it�s possible to train agents on sub objectives to teach multiple forms of complex behavior that can then be abstractly chosen by an evolved Decision Maker to provide a better outcome than agents that were trained specifically towards that single solution.Electrical Engineerin

G-CSC Report 2010

The present report gives a short summary of the research of the Goethe Center for Scientific Computing (G-CSC) of the Goethe University Frankfurt. G-CSC aims at developing and applying methods and tools for modelling and numerical simulation of problems from empirical science and technology. In particular, fast solvers for partial differential equations (i.e. pde) such as robust, parallel, and adaptive multigrid methods and numerical methods for stochastic differential equations are developed. These methods are highly adanvced and allow to solve complex problems.. The G-CSC is organised in departments and interdisciplinary research groups. Departments are localised directly at the G-CSC, while the task of interdisciplinary research groups is to bridge disciplines and to bring scientists form different departments together. Currently, G-CSC consists of the department Simulation and Modelling and the interdisciplinary research group Computational Finance

Imaging of a fluid injection process using geophysical data - A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

Pass it on: towards a political economy of propensity

The paper argues that the work of Gabriel Tarde on imitation provides a fertile means of understanding how capitalism is forging a new affective technology which conforms to a logic of propensity rather than to means-end reasoning. This it does by drawing together a biological understanding of semiconscious cognition with various practical geometric arts so as to re-stage the world as a series of susceptible situations which can be ridden rather than rigidly controlled. The paper examines the advent of technologies which attend to the variable geometry of so-called animal spirits in the realm of business and then, using Tarde's work as a springboard, considers some alternative means of understanding imitative rays which have less instrumental undertones. The paper is an illustration of the way in which biology and culture have increasingly become intertwined