Year: 2021
Type: article
Abstract: In this article, we compare four different “game spaces” in terms of their usefulness in characterizing multiplayer tabletop games, with a particular interest in any underlying change to a game’s characteristics as the number of players changes. In each case, we take a 16-D feature space and reduce it to a 2-D visualizable landscape. We find that a space obtained from optimization of parameters in Monte Carlo tree search is most directly interpretable to characterize our set of games in terms of the relative importance of imperfect information, adversarial opponents, and reward sparsity. These results do not correlate with a space defined using attributes of the game tree. This dimensionality reduction does not show any general effect as the number of players changes. Therefore, we consider the question using the original features to classify the games into two sets: 1) those for which the characteristics of the game change significantly as the number of players changes and 2) those for which there is no such effect.
Source: IEEE Transactions on Games
Institution: Queen Mary University of London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4206506568