Combining Local and Global Optimisation for Virtual Camera Control

Controlling a virtual camera in 3D computer games is a complex task. The camera is required to react to dynamically changing environments and produce high quality visual results and smooth animations. This paper proposes an approach that combines local and global search to solve the virtual camera control problem. The automatic camera control problem is described and it is decomposed into sub-problems; then a hierarchical architecture that solves each sub-problem using the most appropriate optimisation technique is proposed. The approach is compared to pure local search solutions to showcase the advantages of the proposed architecture in terms of visual performance and robustness.peer-reviewe

Game and player feature selection for entertainment capture

The authors would like to thank Henrik Jorgensen and all children of Henriette Horlucks and Rosengardskolen Schools, Odense, Denmark that participated in the experiments. The tiles were designed by C. Isaksen from Isaksen Design and parts of their hardware and software implementation were collectively done by A. Derakhshan, F. Hammer, T. Klitbo and J. Nielsen. KOMPAN, Mads Clausen Institute, and Danfoss Universe also participated in the development of the tiles.The notion of constructing a metric of the degree to which a player enjoys a given game has been presented previously. In this paper, we attempt to construct such metric models of children’s ‘fun’ when playing the Bug Smasher game on the Playware platform. First, a set of numerical features derived from a child’s interaction with the Playware hardware is presented. Then the Sequential Forward Selection and the n- Best feature selection algorithms are employed together with a function approximator based on an artificial neural network to construct feature sets and function that model the child’s notion of ‘fun’ for this game. Performance of the model is evaluated by the degree to which the preferences predicted by the model match those expressed by the children in a survey experiment. The results show that an effective model can be constructed using these techniques and that the Sequential Forward Selection method performs better in this task than n-Best. The model reveals differing preferences for game parameters between children who react fast to game events and those who react slowly. The limitations and the use of the methodology as an effective adaptive mechanism to entertainment augmentation are discussed.This work was in part supported by the Danish National Research Council (project no: 274-05-0511).peer-reviewe

Player modeling using self-organization in Tomb Raider : Underworld

We present a study focused on constructing models of players for the major commercial title Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level playing behavior data obtained from 1365 players that completed the TRU game. The unsupervised learning approach utilized reveals four types of players which are analyzed within the context of the game. The proposed approach automates, in part, the traditional user and play testing procedures followed in the game industry since it can inform game developers, in detail, if the players play the game as intended by the game design. Subsequently, player models can assist the tailoring of game mechanics in real-time for the needs of the player type identified.peer-reviewe

A generic approach for generating interesting interactive Pac-Man opponents

This paper follows on from our previous work focused on formulating an efficient generic measure of user's satisfaction (`interest') when playing predator /prey games. Viewing the game from the predators' (i.e. opponents') perspective, a robust on-line neuroevolution learning mechanism has been presented capable of increasing --- independently of the initial behavior and playing strategy --- the well known Pac-Man game's interest as well as keeping that interest at high levels while the game is being played. This mechanism has also demonstrated high adaptability to changing PacMan playing strategies in a relatively simple playing stage. In the work presented here, we attempt to test the on-line learning mechanism over more complex stages and to explore the relation between the interest measure and the topology of the stage. Results show that the interest measure proposed is independent of the stage's complexity and topology, which demonstrates the approach 's generality for this game.peer-reviewe

Modeling player experience in Super Mario Bros

This paper investigates the relationship between level design parameters of platform games, individual playing characteristics and player experience. The investigated design parameters relate to the placement and sizes of gaps in the level and the existence of direction changes; components of player experience include fun, frustration and challenge. A neural network model that maps between level design parameters, playing behavior characteristics and player reported emotions is trained using evolutionary preference learning and data from 480 platform game sessions. Results show that challenge and frustration can be predicted with a high accuracy (77.77% and 88.66% respectively) via a simple single-neuron model whereas model accuracy for fun (69.18%) suggests the use of more complex non-linear approximators for this emotion. The paper concludes with a discussion on how the obtained models can be utilized to automatically generate game levels which will enhance player experience.peer-reviewe

Modelling children’s entertainment in the playware playground

This paper introduces quantitative measurements/metrics of qualitative entertainment features within interactive playgrounds inspired by computer games and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. For this purpose the innovative Playware playground is presented and a quantitative approach to entertainment modeling based on psychological studies in the field of computer games is introduced. Evolving artificial neural networks (ANNs) are used to model player satisfaction (interest) in real-time and investigate quantitatively how the qualitative factors of challenge and curiosity contribute to human entertainment according to player reaction time with the game. The limitations of the methodology and the extensibility of the proposed approach to other genres of digital entertainment are discussedpeer-reviewe

Predicting player behavior in Tomb Raider : Underworld

This paper presents the results of an explorative study on predicting aspects of playing behavior for the major commercial title Tomb Raider: Underworld (TRU). Various supervised learning algorithms are trained on a large-scale set of in-game player behavior data, to predict when a player will stop playing the TRU game and, if the player completes the game, how long will it take to do so. Results reveal that linear regression models and other non-linear classification techniques perform well on the tasks and that decision tree learning induces small yet well-performing and informative trees. Moderate performance is achieved from the prediction models, which indicates the complexity of predicting player behavior based on a constrained set of gameplay metrics and the noise existent in the dataset examined, a generic problem in large-scale data collection from millions of remote clients.peer-reviewe

Real-time adaptation of augmented-reality games for optimizing player satisfaction

A first endeavor for optimizing player satisfaction in augmented-reality games through the dasiaPlaywarepsila physical interactive platform is presented in this paper. Constructed user models, reported in the literature, map individual playing characteristics to reported entertainment preferences of augmented-reality game players. An adaptive mechanism then adjusts controllable game parameters in real-time in order to improve the entertainment value of the game for the player. The basic approach presented here applies gradient ascent to such a model to reveal the direction toward games of higher entertainment value while a rule-based system exploits the derivative information to adjust specific game parameters to augment the entertainment value. Those adjustments take place frequently during the game in small time intervals that maintain the constructed model's accuracy. Performance of the adaptation mechanism is evaluated using a game survey experiment. Results reveal that children show a notable preference for the adaptive versus the static Bug-Smasher (dasiaPlaywarepsila test-bed) game variant even when simple adaptive approaches like the one proposed are used. The limitations and the use of the methodology as a baseline effective adaptive mechanism to entertainment augmentation are discussed.peer-reviewe

Real-time challenge balance in an RTS game using rtNEAT

Abstract — This paper explores using the NEAT and rtNEAT neuro-evolution methodologies to generate intelligent opponents in real-time strategy (RTS) games. The main objective is to adapt the challenge generated by the game opponents to match the skill of a player in real-time, ultimately leading to a higher entertainment value perceived by a human player of the game. Results indicate the effectiveness of NEAT and rtNEAT but demonstrate their limitations for use in real-time strategy games. I