Computational and algorithmic models of strategies in turn-based games

Open Access
Authors
  • S. Wierda
Publication date 2014
Host editors
  • P. Bello
  • M. Guarini
  • M. McShane
  • B. Scassellati
Book title CogSci 2014
Book subtitle cognitive science meets artificial intelligence: human and artifical agents in interactive contexts: 36th Annual Cognitive Science Conference: Quebec City, Canada, Jul 23-Jul 26
ISBN (electronic)
  • 9780991196708
Event 36th Annual Meeting of the Cognitive Science Society
Volume | Issue number 3
Pages (from-to) 1778-1783
Publisher Austin, TX: Cognitive Science Society
Organisations
  • Interfacultary Research - Institute for Logic, Language and Computation (ILLC)
Abstract
We study two different models of a turn-based game called the Marble Drop Game, which is an experimental paradigm designed to investigate higher-order social reasoning. Our first model is a computational-level description of the game, associating cognitive difficulty of a game trial with its structural properties. Our second model is an algorithmic-level model postulating a forward reasoning plus back-tracking strategy for solving the game, rather than backward induction as prescribed by game theory. Our experiment shows that the algorithmic-level model is more predictive for the participants’ reaction times. This research illustrates how various methods of logic and computer science may be used for building computational cognitive models.
Document type Conference contribution
Language English
Published at https://escholarship.org/uc/item/6pd0k6bk
Downloads
paper309 (Final published version)
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