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Learning payoff functions in infinite games
Yevgeniy Vorobeychik
, Michael P. Wellman
, Satinder Singh
Department of Computer Science & Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
51
Scopus citations
Overview
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Keyphrases
Payoff Function
100%
Infinite Games
100%
Relative Utility
50%
Per Se
25%
Learning Model
25%
Learning Performance
25%
Supervised Learning
25%
Analytical Form
25%
Market-based
25%
Function Learning
25%
Representation Function
25%
Regression Problem
25%
Multi-agent Environment
25%
Strategy Profile
25%
Strategy Space
25%
Payoff Information
25%
First-price Sealed-bid Auction
25%
Scheduling Game
25%
Computer Science
Payoff Function
100%
Learning Performance
25%
Supervised Learning
25%
multi-agent
25%
Regression Problem
25%
Strategy Profile
25%
Analytical Form
25%
Economics, Econometrics and Finance
Auction
100%
Price
100%
Mathematics
Payoff Function
100%
Function Representation
25%