Representing and Reasoning About the Rules of General Games With Imperfect Information

Schiffel, Stephan; Thielscher, Michael

doi:10.1613/jair.4115

Cited by 22 publications

(20 citation statements)

References 49 publications

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“…Games in GDL are described in terms of simple instructions based on first-order logic clauses, designed for deterministic games with perfect information. Extensions to this language also allow for imperfect information and epistemic games [6], [7]. The general intelligence required by GDL agents has led to several important research contributions [8], with one of the most popular and effective techniques being Monte Carlo tree search [9], [10], while the last winner of the IGGPC uses an approach based on constraint programming and symmetry detection [11].…”

Section: Introductionmentioning

confidence: 99%

An Empirical Evaluation of Two General Game Systems: Ludii and RBG

Piette

Stephenson

Soemers

et al. 2019

2019 IEEE Conference on Games (CoG)

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Although General Game Playing (GGP) systems can facilitate useful research in Artificial Intelligence (AI) for gameplaying, they are often computationally inefficient and somewhat specialised to a specific class of games. However, since the start of this year, two General Game Systems have emerged that provide efficient alternatives to the academic state of the artthe Game Description Language (GDL). In order of publication, these are the Regular Boardgames language (RBG), and the Ludii system. This paper offers an experimental evaluation of Ludii.Here, we focus mainly on a comparison between the two new systems in terms of two key properties for any GGP system: simplicity/clarity (e.g. human-readability), and efficiency.

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Section: Introductionmentioning

confidence: 99%

An Empirical Evaluation of Two General Game Systems: Ludii and RBG

Piette

Stephenson

Soemers

et al. 2019

2019 IEEE Conference on Games (CoG)

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“…With introduction of GDL-II, it was claimed that the GDL language can be considered complete (Thielscher, 2010;Schiffel and Thielscher, 2014), and additional elements can only serve for simplifying description or will be forcing setting extensions far beyond the concept of General Game Playing (e.g. open-world games or physical games like in General Video Game Playing (Perez et al, 2015)).…”

Section: Resultsmentioning

confidence: 99%

“…∆t . In order to summarize above in a formal definition, as in (Schiffel and Thielscher, 2014), we make use of the fact that any stratified set of clauses G has a unique stable model (Gelfond and Lifschitz, 1988). We denote it SM [G].…”

Section: Semanticsmentioning

confidence: 99%

Towards a Real-time Game Description Language

Kowalski

Kisielewicz

2016

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

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For the sake of the General Game Playing competition, the Game Description Language (GDL) has been developed as a high-level knowledge representation formalism, able to describe any finite, n-player, turnbased, deterministic, full-information game. The last two restrictions were removed by the later extension called GDL-II. In this paper, we discuss our extension of GDL, called rtGDL, that makes it possible to describe a large variety of games involving a real-time factor. We consider its effectiveness and expressiveness, arguing that this is a promising direction of research in the field of General Game Playing.

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“…Also, the GVGAI competition does not give the agents any previous information -before the game starts-about the task. This is different from GGP, which provides the rules of the game in a logical language (Schiffel & Thielscher, 2014). Consequently, in GVGAI, the agents can improve from interaction, a setting that has now become common in many AI evaluation platforms (Castelvecchi, 2016), using a very general reinforcement learning setting.…”

Section: General Video Game Aimentioning

confidence: 99%

AI Generality and Spearman’s Law of Diminishing Returns

Hernández-Orallo¹

2019

jair

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Many areas of AI today use benchmarks and competitions with larger and wider sets of tasks. This tries to deter AI systems (and research effort) from specialising to a single task, and encourage them to be prepared to solve previously unseen tasks. It is unclear, however, whether the methods with best performance are actually those that are most general and, in perspective, whether the trend moves towards more general AI systems. This question has a striking similarity with the analysis of the so-called positive manifold and general factors in the area of human intelligence. In this paper, we first show how the existence of a manifold (positive average pairwise task correlation) can also be analysed in AI, and how this relates to the notion of agent generality, from the individual and the populational points of view. From the populational perspective, we analyse the following question: is this manifold correlation higher for the most or for the least able group of agents? We contrast this analysis with one of the most controversial issues in human intelligence research, the so-called Spearman's Law of Diminishing Returns (SLODR), which basically states that the relevance of a general factor diminishes for most able human groups. We perform two empirical studies on these issues in AI. We analyse the results of the 2015 general video game AI (GVGAI) competition, with games as tasks and "controllers" as agents, and the results of a synthetic setting, with modified elementary cellular automata (ECA) rules as tasks and simple interactive programs as agents. In both cases, we see that SLODR doesnot appear. The data, and the use of just two scenarios, does not clearly support the reverse either, a Universal Law of Augmenting Returns (ULOAR), but calls for more experiments on this question.

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Representing and Reasoning About the Rules of General Games With Imperfect Information

Cited by 22 publications

References 49 publications

An Empirical Evaluation of Two General Game Systems: Ludii and RBG

An Empirical Evaluation of Two General Game Systems: Ludii and RBG

Towards a Real-time Game Description Language

AI Generality and Spearman’s Law of Diminishing Returns

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