NDLI: Emergence of Conventions for Efficiently Resolving Conflicts in Complex Networks

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IAT 2014 Preface

IAT 2014 Non-Program Committee Reviewers

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Emergence of Conventions for Efficiently Resolving Conflicts in Complex Networks

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IAT 2014 Author Index

IAT 2014 Publisher's Information

Emergence of Conventions for Efficiently Resolving Conflicts in Complex Networks

Content Provider	ACM Digital Library
Author	Sugawara, Toshiharu
Abstract	We investigated the emergence of conventions for conflict resolutions in agent networks with various structures through pair wise reinforcement learning. Whereas coordinated agents encounter conflict situations in the course of actions, their resolutions are complex and computationally expensive due to mutual analysis of subsequent actions by both agents and communication costs of the interactions. Norms and conventions are expected to reduce these costs by regulating agent actions in recurrent conflicts. This paper describes a typical conflict situation using a Markov game and we investigated whether or not agents with a certain attitude to conflicts could learn the conventions of agent networks that had complex structures. We first examined the emergence of conventions and their characteristics in fully connected networks. Then, we compared them with the results from other agent network structures such as BA and CNN networks. We found the network structure strongly affected their emergence and the agents could sometimes learn no conventions although they could learn locally consistent actions for resolutions.
Starting Page	222
Ending Page	229
Page Count	8
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.171
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Subject Keyword	Norm convention conflict resolution reinforcement learning markov game
Content Type	Text
Resource Type	Article

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