General video game rule generation

Khalifa, Ahmed; Green, Michael Cerny; Peŕez-Liebana, Diego; Togelius, Julian

doi:10.1109/cig.2017.8080431

Cited by 37 publications

(24 citation statements)

References 20 publications

(32 reference statements)

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“…Years later, Perez-Liebana et al [3] implemented a version of Schaul's initial framework in Java and organized the first General Video Game AI (GVGAI) competition in 2014 [8], which employed games developed in VGDL. In the following years, this framework was extended to accommodate two-player games [9], [10], level [11], rule [12] generation, and real-world physics games [13]. These competition tracks accumulate hundreds of submissions.…”

Section: The Gvgai Frameworkmentioning

confidence: 99%

General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

et al. 2019

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General Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demand of different research directions. The agents are required either to play multiple unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education and competitions five years after its birth. A future plan of framework improvements is also described.

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Section: The Gvgai Frameworkmentioning

confidence: 99%

General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

et al. 2019

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“…While the cases surveyed in Section IV focused mostly on the generation of levels and visuals, it is important to explore how QD algorithms can work with different facets of game content such as rules [50], music [51], etc. These facets come with their own challenges in defining quality or diversity.…”

Section: Open Problems and Outlookmentioning

confidence: 99%

Procedural Content Generation through Quality Diversity

Gravina

Khalifa

Liapis

et al. 2019

2019 IEEE Conference on Games (CoG)

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103

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Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single-and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.

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“…However, in the learning track, no forward model is given, a learning agent needs to learn in an trial-and-error way. There are two other tracks based on the GVGAI framework which focus more on game design: the rule generation [10] and the level generation [11]. In the rule generation track, a competition entry (generator) is required to generate game rules (interactions and game termination conditions) given a game level as input, while in the level generation track, an entry is asked to generate a level for a certain game.…”

Section: A General Video Game Ai Frameworkmentioning

confidence: 99%

Shallow Decision-Making Analysis in General Video Game Playing

Bravi

Peŕez-Liebana

Lucas

et al. 2018

2018 IEEE Conference on Computational Intelligence and Games (CIG)

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The General Video Game AI competitions have been the testing ground for several techniques for game-playing, such as evolutionary computation techniques, tree search algorithms, hyper-heuristic-based or knowledge-based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision-making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS-based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent's behaviours.

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General video game rule generation

Cited by 37 publications

References 20 publications

General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

Procedural Content Generation through Quality Diversity

Shallow Decision-Making Analysis in General Video Game Playing

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