MODELING AND AUGMENTING GAME ENTERTAINMENT THROUGH CHALLENGE AND CURIOSITY

2007 ◽  
Vol 16 (06) ◽  
pp. 981-999 ◽  
Author(s):  
GEORGIOS N. YANNAKAKIS ◽  
JOHN HALLAM
Keyword(s):  
Real Time ◽  
Computer Games ◽  
Mixed Reality ◽  
Computer Game ◽  
Feedforward Neural Networks ◽  
Interactive Systems ◽  
Quantitative Measurements ◽  
Player Satisfaction ◽  
On Line ◽  
On Line Learning

This paper presents quantitative measurements/metrics of qualitative entertainment features within computer game environments and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. A human-verified metric of interest (i.e. player entertainment in real-time) for predator/prey games and a neuro-evolution on-line learning (i.e. during play) approach have already been reported in the literature to serve this purpose. In this paper, an alternative quantitative approach to entertainment modeling based on psychological studies in the field of computer games is introduced and a comparative study of the two approaches is presented. Feedforward neural networks (NNs) and fuzzy-NNs 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. We demonstrate that appropriate non-extreme levels of challenge and curiosity generate high values of entertainment and we project the extensibility of the approach to other genres of digital entertainment (e.g. mixed-reality interactive playgrounds).

Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽  
2020 ◽  
Vol 2020 (2) ◽  
pp. 9-11
Author(s):  
Tomohiro Fukuda
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Computer Games ◽  
Construction Projects ◽  
Mixed Reality ◽  
Semantic Segmentation ◽  
Environment Design ◽  
Aviation Training ◽  
Architecture And Design ◽  
World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

An Adaptive State Filtering Algorithm for Systems With Partially Known Dynamics

2002 ◽  
Vol 124 (3) ◽  
pp. 364-374 ◽  
Author(s):  
Alexander G. Parlos ◽  
Sunil K. Menon ◽  
Amir F. Atiya
Keyword(s):  
Neural Networks ◽  
Case Studies ◽  
Feedforward Neural Networks ◽  
Complex Case ◽  
State Equations ◽  
Estimation Errors ◽  
Filter Performance ◽  
On Line ◽  
Noise Statistics ◽  
On Line Learning

On-line filtering of stochastic variables that are difficult or expensive to directly measure has been widely studied. In this paper a practical algorithm is presented for adaptive state filtering when the underlying nonlinear state equations are partially known. The unknown dynamics are constructively approximated using neural networks. The proposed algorithm is based on the two-step prediction-update approach of the Kalman Filter. The algorithm accounts for the unmodeled nonlinear dynamics and makes no assumptions regarding the system noise statistics. The proposed filter is implemented using static and dynamic feedforward neural networks. Both off-line and on-line learning algorithms are presented for training the filter networks. Two case studies are considered and comparisons with Extended Kalman Filters (EKFs) performed. For one of the case studies, the EKF converges but it results in higher state estimation errors than the equivalent neural filter with on-line learning. For another, more complex case study, the developed EKF does not converge. For both case studies, the off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. On-line training further enhances filter performance, decoupling the eventual filter accuracy from the accuracy of the assumed system model.

scholarly journals Metazoa Ludens: Mixed Reality Environment for Playing Computer Games with Pets

2006 ◽  
Vol 5 (3) ◽  
pp. 53-58 ◽  
Author(s):  
Roger K. C. Tan ◽  
Adrian David Cheok ◽  
James K. S. Teh
Keyword(s):  
Real World ◽  
Computer Games ◽  
Virtual World ◽  
Mixed Reality ◽  
Leisure Activities ◽  
Computer Game ◽  
Technological Advancement ◽  
The Real ◽  
Computer Mediated ◽  
Pet Owner

For better or worse, technological advancement has changed the world to the extent that at a professional level demands from the working executive required more hours either in the office or on business trips, on a social level the population (especially the younger generation) are glued to the computer either playing video games or surfing the internet. Traditional leisure activities, especially interaction with pets have been neglected or forgotten. This paper introduces Metazoa Ludens, a new computer mediated gaming system which allows pets to play new mixed reality computer games with humans via custom built technologies and applications. During the game-play the real pet chases after a physical movable bait in the real world within a predefined area; infra-red camera tracks the pets' movements and translates them into the virtual world of the system, corresponding them to the movement of a virtual pet avatar running after a virtual human avatar. The human player plays the game by controlling the human avatar's movements in the virtual world, this in turn relates to the movements of the physical movable bait in the real world which moves as the human avatar does. This unique way of playing computer game would give rise to a whole new way of mixed reality interaction between the pet owner and her pet thereby bringing technology and its influence on leisure and social activities to the next level

scholarly journals Dynamic Control in Real-Time Heuristic Search

2008 ◽  
Vol 32 ◽  
pp. 419-452 ◽  
Author(s):  
V. Bulitko ◽  
M. Lustrek ◽  
J. Schaeffer ◽  
Y. Bjornsson ◽  
S. Sigmundarson
Keyword(s):  
Real Time ◽  
Computer Games ◽  
Heuristic Search ◽  
Dynamic Control ◽  
Computer Game ◽  
Action Planning ◽  
Search Algorithms ◽  
Problem Size ◽  
Planning Time ◽  
Large Computer

Real-time heuristic search is a challenging type of agent-centered search because the agent's planning time per action is bounded by a constant independent of problem size. A common problem that imposes such restrictions is pathfinding in modern computer games where a large number of units must plan their paths simultaneously over large maps. Common search algorithms (e.g., A*, IDA*, D*, ARA*, AD*) are inherently not real-time and may lose completeness when a constant bound is imposed on per-action planning time. Real-time search algorithms retain completeness but frequently produce unacceptably suboptimal solutions. In this paper, we extend classic and modern real-time search algorithms with an automated mechanism for dynamic depth and subgoal selection. The new algorithms remain real-time and complete. On large computer game maps, they find paths within 7% of optimal while on average expanding roughly a single state per action. This is nearly a three-fold improvement in suboptimality over the existing state-of-the-art algorithms and, at the same time, a 15-fold improvement in the amount of planning per action.

A Fixed Size Storage O(n3) Time Complexity Learning Algorithm for Fully Recurrent Continually Running Networks

1992 ◽  
Vol 4 (2) ◽  
pp. 243-248 ◽  
Author(s):  
Jürgen Schmidhuber
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Learning Algorithm ◽  
Fixed Size ◽  
Time Step ◽  
The Real ◽  
On Line ◽  
On Line Learning

The real-time recurrent learning (RTRL) algorithm (Robinson and Fallside 1987; Williams and Zipser 1989) requires O(n4) computations per time step, where n is the number of noninput units. I describe a method suited for on-line learning that computes exactly the same gradient and requires fixed-size storage of the same order but has an average time complexity per time step of O(n3).

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