Personalised track design in car racing games

Georgiou, Theodosis; Demiris, Yiannis

doi:10.1109/cig.2016.7860435

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Procedural Generation (PG) or Procedural Content Generation (PCG) are first adopted in video game industry [13]. It refers to the practice of utilizing algorithms to automatically generate game content including levels, maps, racing tracks, etc [23], [24], [25]. Many machine learning concepts like data augmentation [26], [27], domain randomization [28], [29] and Generative Adversarial Network [30] have been proposed to help design PG algorithms, making the game playable and alleviating the burden of game designers.…”

Section: Related Workmentioning

confidence: 99%

Improving the Generalization of End-to-End Driving through Procedural Generation

Li,

Peng,

Zhang

et al. 2020

Preprint

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Recently there is a growing interest in the end-toend training of autonomous driving where the entire driving pipeline from perception to control is modeled as a neural network and jointly optimized. The end-to-end driving is usually first developed and validated in simulators. However, most of the existing driving simulators only contain a fixed set of maps and a limited number of configurations. As a result the deep models are prone to overfitting training scenarios. Furthermore it is difficult to assess how well the trained models generalize to unseen scenarios. To better evaluate and improve the generalization of end-to-end driving, we introduce an open-ended and highly configurable driving simulator called PGDrive. PGDrive first defines multiple basic road blocks such as ramp, fork, and roundabout with configurable settings. Then a range of diverse maps can be assembled from those blocks with procedural generation, which are further turned into interactive environments. The experiments show that the driving agent trained by reinforcement learning on a small fixed set of maps generalizes poorly to unseen maps. We further validate that training with the increasing number of procedurally generated maps significantly improves the generalization of the agent across scenarios of different traffic densities and map structures. Code is available at https: //decisionforce.github.io/pgdrive.

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Section: Related Workmentioning

confidence: 99%

Improving the Generalization of End-to-End Driving through Procedural Generation

Li,

Peng,

Zhang

et al. 2020

Preprint

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“…Multiple level generation focused contents tend to alter the map creation process in form of either a platform where the player plays the game such as mazes [57], [58], cave systems [59], or tracks of traversable areas [50], [60], [61]. Another result also shows map generation focused on games that involve procedurally generated dungeon mechanism [62]- [64] which makes an elaborate level multi-level playable area based on a particular game genre/mechanics.…”

Section: Focused Contentmentioning

confidence: 99%

A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation

Hafis

Tolle

Supianto

2019

JITeCS

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Procedural Content Generation (PCG) is an emerging field of study in computer science that focuses on automating the process of generating content by using algorithm, making the content generation process with less human effort. However, a more specific empirical evidence on how it is being used in a game-related implementation are still lacking. This paper presents the findings of review performed in the past 5 years looking on how PCG are being applied in game-related content, whether it is from the basic paper characteristic to analyze the trends, the field of PCG itself, and the game domain of game model and game genre. The studies had shown that PCG are being used extensively in game-related content but has seen more uses on specific type of contents rather than being used ubiquitously in all sorts of contents. The result shown that there are not specific best type of PCG method or algorithm being used instead an array of approach can be used based on what content being created. Result also shown that PCG are being used in multiple type of games, but similarly, based on the paper found, only certain types of game benefits PCG extensively such as action and platforming games while other model and genre of games have not seen much PCG application yet. Further studies are also required to analyze how experimentation and evaluation of PCG are being done as well as PCG domain in educational games as well as game-based learning, the quality catachrestic being analyzed on the papers are also worth mentioning to understand the underlying result of PCG usage in game-related contents.

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“…Using the constraints and spatial knowledge, the engine creates a scene template. Then the objects are optimally placed [8].…”

Section: Related Workmentioning

confidence: 99%

Terrain Generation from User Text using Cellular Automata

Zachariah*¹,

G²,

R³

et al. 2019

IJRTE

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Last decade has been witnessed for the dramatic advancement in computer games. Latest studies shows that procedurally generated content makes the players remain engaged for a long time. Incorporating the user's preferences and creativity in the games is still a challenging task. In this paper, we focus on the diversity of terrain generation in terms of its components. Personalization of user experience via effective modeling, combined with real-time adjustment of the content helps for meaningful content generation. Our engine accepts input from the user in Terrain development language (TDL).The user has to specify the type and features of the terrain. The engine extracts features through Terrain Development Algorithm (TDA) and generates individual components according to the information. The end result is to provide a user skill-matched terrain, which can be rendered for various games.

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Personalised track design in car racing games

Cited by 8 publications

References 13 publications

Improving the Generalization of End-to-End Driving through Procedural Generation

Improving the Generalization of End-to-End Driving through Procedural Generation

A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation

Terrain Generation from User Text using Cellular Automata

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