Results 31 to 40 of about 234,397 (303)

Learning-Based Procedural Content Generation [PDF]

open access: yesIEEE Transactions on Computational Intelligence and AI in Games, 2015
13 pages, 9 figures, manuscript submitted to IEEE Transactions on Computational Intelligence and AI Games (Also a technical report, School of Computer Science, The University of Manchester)
Roberts, Jonathan, Chen, Ke
openaire   +5 more sources

Can’t see the Forest for the Trees: Perceiving Realism of Procedural Generated Trees in First-Person Games [PDF]

open access: yesEAI Endorsed Transactions on Creative Technologies, 2021
This paper explores a bio-mimetic approach for procedurally generated trees and forests, emphasizing the need to deliver a unique gameplay experience.
Peter Jæger   +2 more
doaj   +1 more source

IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm

open access: yesApplied Sciences, 2022
In this work we present the intelligent orchestrator of random generators (IORand), a hybrid procedural content generation (PCG) algorithm, driven by game experience, based on reinforcement learning and semi-random content generation methods.
Marco A. Moreno-Armendáriz   +3 more
doaj   +1 more source

Experience-Driven Procedural Content Generation [PDF]

open access: yesIEEE Transactions on Affective Computing, 2011
Procedural content generation (PCG) is an increasingly important area of technology within modern human-computer interaction (HCI) design. Personalization of user experience via affective and cognitive modeling, coupled with real-time adjustment of the content according to user needs and preferences are important steps toward effective and meaningful ...
Yannakakis, Georgios N.   +1 more
openaire   +1 more source

Increasing generality in machine learning through procedural content generation [PDF]

open access: yesNature Machine Intelligence, 2020
Procedural Content Generation (PCG) refers to the practice, in videogames and other games, of generating content such as levels, quests, or characters algorithmically. Motivated by the need to make games replayable, as well as to reduce authoring burden, limit storage space requirements, and enable particular aesthetics, a large number of PCG methods ...
Sebastian Risi, Julian Togelius
openaire   +4 more sources

Start small: Training controllable game level generators without training data by learning at multiple sizes

open access: yesAlexandria Engineering Journal, 2023
A level generator is a tool that generates game levels from noise. Training a generator without a dataset suffers from feedback sparsity, since it is unlikely to generate a playable level via random exploration. A common solution is shaped rewards, which
Yahia Zakaria   +2 more
doaj   +1 more source

Study of Artificial Intelligent Algorithms Applied in Procedural Content Generation in Video Games

open access: yesEludamos, 2020
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Martín González-Hermida   +3 more
doaj   +1 more source

A Survey on the Procedural Generation of Virtual Worlds

open access: yesMultimodal Technologies and Interaction, 2017
This survey presents algorithms for the automatic generation of content for virtual worlds, in particular for games. After a definition of the term procedural content generation, the algorithms to generate realistic objects such as landscapes and ...
Jonas Freiknecht, Wolfgang Effelsberg
doaj   +1 more source

A Multimodal Approach for Real Time Recognition of Engagement towards Adaptive Serious Games for Health

open access: yesSensors, 2022
In this article, an unobtrusive and affordable sensor-based multimodal approach for real time recognition of engagement in serious games (SGs) for health is presented.
Konstantinos Mitsis   +6 more
doaj   +1 more source

Testing Autonomous Robot Control Software Using Procedural Content Generation [PDF]

open access: yes, 2013
We present a novel approach for reducing manual effort when testing autonomous robot control algorithms. We use procedural content generation, as developed for the film and video game industries, to create a diverse range of test situations.
A. Wardziński   +5 more
core   +2 more sources

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