NDLI: Strategic adaptation of humans playing computer algorithms in a repeated constant-sum game

Content Provider	Springer Nature Link
Author	Spiliopoulos, Leonidas
Copyright Year	2012
Abstract	This paper examines strategic adaptation in participants’ behavior conditional on the type of their opponent. Participants played a constant-sum game for 100 rounds against each of three pattern-detecting computer algorithms designed to exploit regularities in human behavior such as imperfections in randomizing and the use of simple heuristics. Significant evidence is presented that human participants not only change their marginal probabilities of choosing actions, but also their conditional probabilities dependent on the recent history of play. A cognitive model incorporating pattern recognition is proposed that capture the shifts in strategic behavior of the participants better than the standard non-pattern detecting model employed in the literature, the Experience Weighted Attraction model (and by extension its nested models, reinforcement learning and fictitious play belief learning).
Starting Page	131
Ending Page	160
Page Count	30
File Format	PDF
ISSN	13872532
Journal	Autonomous Agents and Multi-Agent Systems
Volume Number	27
Issue Number	1
e-ISSN	15737454
Language	English
Publisher	Springer US
Publisher Date	2012-07-06
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Learning Pattern detection Computer algorithms Constant sum games Experience weighted attraction Repeated games Artificial Intelligence (incl. Robotics) Computer Systems Organization and Communication Networks Computing Methodologies Software Engineering/Programming and Operating Systems User Interfaces and Human Computer Interaction
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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