Computational and algorithmic models of strategies in turn-based games
| Authors |
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| Publication date | 2014 |
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| 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) |
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| Event | 36th Annual Meeting of the Cognitive Science Society |
| Volume | Issue number | 3 |
| Pages (from-to) | 1778-1783 |
| Publisher | Austin, TX: Cognitive Science Society |
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| 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.
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| Document type | Conference contribution |
| Language | English |
| Published at | https://escholarship.org/uc/item/6pd0k6bk |
| Downloads |
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