49 research outputs found
Application of Retrograde Analysis to Fighting Games
With the advent of the fighting game AI competition, there has been recent interest in two-player fighting games. Monte-Carlo Tree-Search approaches currently dominate the competition, but it is unclear if this is the best approach for all fighting games. In this thesis we study the design of two-player fighting games and the consequences of the game design on the types of AI that should be used for playing the game, as well as formally define the state space that fighting games are based on. Additionally, we also characterize how AI can solve the game given a simultaneous action game model, to understand the characteristics of the solved AI and the impact it has on game design
IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION
The gaming industry has become one of the largest and most profitable industries today. According to market research, the industry revenues will pass 20 Billion in 2024. With the industry growing rapidly, players have become more demanding, expecting better content and quality. This means that game studios need new and innovative ways to make their games more enjoyable. One technique used to improve the player experience is DDA (Dynamic Difficulty Adjustment). It leverages the current player state to perform different adjustments during the game to tune the difficulty delivered to the player to be more in line with their expectations and capabilities. In this thesis, we will explore and test the ability to obtain the difficulty level in which a player should be placed initially, by using previous gaming information from platforms like Steam, combined with different machine learning (ML) algorithms and data analyses., In doing so, we can create a pre-assessment of the player as a way of improving DDA’s initial state and the overall gaming experience of players
BNAIC 2008:Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Foundations of Trusted Autonomy
Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie
Experience-driven MAR games: Personalising Mobile Augmented Reality games using Player Models
PhD ThesesWe are witnessing an unprecedented growth of Mobile Augmented Reality
(MAR) technologies, one of the main research areas being MAR games. While
this field is still in its early days, researchers have shown the physical health
benefits of playing these type of games. Computational models have been used
in traditional (non-AR) digital games to predict player experience (PX). These
models give designers insights about PX, and can also be used within games for
real-time adaptation or personalised content generation. Following these findings,
this thesis investigates the potential of creating models that use movement
data and game metrics to predict PX.
An initial pilot study is conducted to evaluate the use of movement data
and game metrics to predict players’ emotional preferences between different
game levels of an exploration-based MAR game. Results indicate that emotional
preferences regarding frustration (≈ 93%) and challenge (≈ 93%) can
be predicted to a reliable and reasonable degree of accuracy. To determine if
these techniques can be applied to serious games for health, an AR exergame
is developed for experiments two, three and four of this thesis. The second and
third experiments aim to predict key experiential constructs, player competence
and immersion, that are important to PX. These experiments further validate
the use of movement data and game metrics to model different aspects of PX
in MAR games. Results suggest that players’ competence (≈ 73%) and sense
of mastery (≈ 81%) can be predicted to a reasonable degree of accuracy. For
the final experiment, this mastery model is used to create a dynamic difficulty
adaptation (DDA) system. The adaptive exergame is then evaluated against
a non-adaptive variant of the same game. Results indicate that the adaptive
game makes players feel a higher sense of confidence during gameplay and that
the adaptation mechanics are more effective for players who do not engage in
regular physical activity.
Across the four studies presented, this thesis is the first known research activity
that investigates using movement data and game metrics to model PX
for DDA in MAR games and makes the following novel contributions: i) movement
data and game metrics can be used to predict player’s sense of mastery or
competence reliably compared to other aspects of PX tested, ii) mastery-based
game adaptation makes players feel greater confidence during game-play, and iii)
mastery-based game adaptation is more effective for players who do not engage
in physical activity. This work also presents a new methodology for PX prediction
in MAR games and a novel adaptation engine driven by player mastery. In
summary, this thesis proposes that PX modelling can be successfully applied to
MAR games, especially for DDA which results in a highly personalised PX and
shows potential as a tool for increasing physical activity