Predicting Dominance Rankings for Score-Based Games

Samothrakis, Spyridon; Pérez, Diego; Lucas, Simon M.; Rohlfshagen, Philipp

doi:10.1109/tciaig.2014.2346242

Cited by 13 publications

(12 citation statements)

References 20 publications

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“…To fix this, the position of each stimulus would need to be represented by a minimum of two parameters. Attempts to extend the Elo rating system directly, such as Glickman’s “Glicko” and “Glicko-2” algorithms (described by Samothrakis et al, 2016) provide a framework for introducing additional parameters, but their additional model complexity can create scenarios in which ratings become trapped in local minima, as well as relying on hyperparameters that must be set ad hoc to govern how dispersion evolves over time. It would be preferable to keep action selection and value updating be as simple and as computationally efficient as possible.…”

Section: Model-based Solutions: Inference About Orderingmentioning

confidence: 99%

Discovering Implied Serial Order Through Model-Free and Model-Based Learning

2019

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Humans and animals can learn to order a list of items without relying on explicit spatial or temporal cues. To do so, they appear to make use of transitivity, a property of all ordered sets. Here, we summarize relevant research on the transitive inference (TI) paradigm and its relationship to learning the underlying order of an arbitrary set of items. We compare six computational models of TI performance, three of which are model-free ( Q -learning, Value Transfer, and REMERGE) and three of which are model-based (RL-Elo, Sequential Monte Carlo, and Betasort). Our goal is to assess the ability of these models to produce empirically observed features of TI behavior. Model-based approaches perform better under a wider range of scenarios, but no single model explains the full scope of behaviors reported in the TI literature.

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Section: Model-based Solutions: Inference About Orderingmentioning

confidence: 99%

Discovering Implied Serial Order Through Model-Free and Model-Based Learning

2019

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“…Beating strong players should be worth more than beating weak players, especially in the event that the competition is flooded with a number of very similar weak players that a mediocre player has been designed to exploit. Also, the players may be heterogeneous, as in the Ghosts vs. Pac-Man competition for example [7], [32].…”

Section: Discussionmentioning

confidence: 99%

The 2014 General Video Game Playing Competition

Peŕez-Liebana

Samothrakis

Togelius

et al. 2016

IEEE Trans. Comput. Intell. AI Games

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146

113

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Abstract-This paper presents the framework, rules, games, controllers and results of the first General Video Game Playing Competition, held at the IEEE Conference on Computational Intelligence and Games in 2014. The competition proposes the challenge of creating controllers for general video game play, where a single agent must be able to play many different games, some of them unknown to the participants at the time of submitting their entries. This test can be seen as an approximation of General Artificial Intelligence, as the amount of game-dependent heuristics needs to be severely limited.The games employed are stochastic real-time scenarios (where the time budget to provide the next action is measured in milliseconds) with different winning conditions, scoring mechanisms, sprite types and available actions for the player. It is a responsibility of the agents to discover the mechanics of each game, the requirements to obtain a high score and the requisites to finally achieve victory. This paper describes all controllers submitted to the competition, with an in-depth description of four of them by their authors, including the winner and the runner-up entries of the contest. The paper also analyzes the performance of the different approaches submitted, and finally proposes future tracks for the competition.

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“…Samothrakis et al [6] considered the issue of slower convergence achieved by the Elo system, along with competitor pairing and accurate performance ranking. In their paper, they look at the Ms Pac-Man vs Ghosts Competition, in which agents can be submitted either for Pac-Man or the group of ghosts.…”

Section: Related Researchmentioning

confidence: 99%

The 2016 Two-Player GVGAI Competition

et al. 2018

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Abstract-This paper showcases the setting and results of the first Two-Player General Video Game AI competition, which ran in 2016 at the IEEE World Congress on Computational Intelligence and the IEEE Conference on Computational Intelligence and Games. The challenges for the general game AI agents are expanded in this track from the single-player version, looking at direct player interaction in both competitive and cooperative environments of various types and degrees of difficulty. The focus is on the agents not only handling multiple problems, but also having to account for another intelligent entity in the game, who is expected to work towards their own goals (winning the game). This other player will possibly interact with first agent in a more engaging way than the environment or any non-playing character may do. The top competition entries are analyzed in detail and the performance of all agents is compared across the four sets of games. The results validate the competition system in assessing generality, as well as showing Monte Carlo Tree Search continuing to dominate by winning the overall Championship. However, this approach is closely followed by Rolling Horizon Evolutionary Algorithms, employed by the winner of the second leg of the contest.

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Predicting Dominance Rankings for Score-Based Games

Cited by 13 publications

References 20 publications

Discovering Implied Serial Order Through Model-Free and Model-Based Learning

Discovering Implied Serial Order Through Model-Free and Model-Based Learning

The 2014 General Video Game Playing Competition

The 2016 Two-Player GVGAI Competition

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