Introducing real world physics and macro-actions to general video game ai

Peŕez-Liebana, Diego; Stephenson, Matthew; Gaina, Raluca D.; Renz, Jochen; Lucas, Simon M.

doi:10.1109/cig.2017.8080443

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…In a similar line, Perez-Liebana et al [13] employed macroactions for GVGAI games that used continuous (rather than grid-based) physics. These games have a larger state space, which in turn delays the effects of the player's actions and modifies the way agents navigate through the level.…”

Section: B Tree Search Methodsmentioning

confidence: 99%

“…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%

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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: B Tree Search Methodsmentioning

confidence: 99%

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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“…The term rolling horizon refers to the fact that planning occurs until a limited look-ahead in the game, after which the agent must replan. RHEA has been subject to several enhancement proposals (e.g., [240,241]) and RHEA-based approaches have been used with some degree of success in the GVGAI competition [2].…”

Section: Evolutionary Planningmentioning

confidence: 99%

A Survey of Planning and Learning in Games

et al. 2020

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In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the multiple methodologies that have been proposed to integrate planning and learning in the context of games. In order to provide a richer contextualization, the paper also presents learning and planning techniques commonly used in games, both in terms of their theoretical foundations and applications.

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“…Games used in this competition are defined using the Video Game Description Language (VGDL), an alternative to GDL that allows for the creation of 2D arcade-style video games. This language has been significantly extended over the years to allow for new types of video games, such as the addition of a rudimentary physics engine in 2017 [46]. Several new games are often added each year to the official GVGAI game collection that is included with the available system code, 3 but many additional games have been created by other research groups to investigate certain specific hypotheses [47].…”

Section: B Gvgai Competitionmentioning

confidence: 99%

Ludii as a Competition Platform

Stephenson

Piette

Soemers

et al. 2019

2019 IEEE Conference on Games (CoG)

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Ludii is a general game system being developed as part of the ERC-funded Digital Ludeme Project (DLP). While its primary aim is to model, play, and analyse the full range of traditional strategy games, Ludii also has the potential to support a wide range of AI research topics and competitions. This paper describes some of the future competitions and challenges that we intend to run using the Ludii system, highlighting some of its most important aspects that can potentially lead to many algorithm improvements and new avenues of research. We compare and contrast our proposed competition motivations, goals and frameworks against those of existing general game playing competitions, addressing the strengths and weaknesses of each platform.

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Introducing real world physics and macro-actions to general video game ai

Cited by 11 publications

References 19 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

A Survey of Planning and Learning in Games

Ludii as a Competition Platform

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