Recent developments in learning and competition with finite automata: (Extended abstract)

Abraham Neyman*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Consider a repeated two-person game. The question is how much smarter should a player be to effectively predict the moves of the other player. The answer depends on the formal definition of effective prediction, the number of actions each player has in the stage game, as well as on the measure of smartness. Effective prediction means that, no matter what the stage-game payoff function, the player can play (with high probability) a best reply in most stages. Neyman and Spencer [4] provide a complete asymptotic solution when smartness is measured by the size of the automata that implement the strategies.

Original languageEnglish
Title of host publicationInternet and Network Economics - Second International Workshop, WINE 2006, Proceedings
Pages1-2
Number of pages2
DOIs
StatePublished - 2006
Event2nd International Workshop on Internet and Network Economics, WINE 2006 - Patras, Greece
Duration: 15 Dec 200617 Dec 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4286 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Internet and Network Economics, WINE 2006
Country/TerritoryGreece
CityPatras
Period15/12/0617/12/06

Fingerprint

Dive into the research topics of 'Recent developments in learning and competition with finite automata: (Extended abstract)'. Together they form a unique fingerprint.

Cite this