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ATAL
2010
Springer
13 years 11 months ago
Frequency adjusted multi-agent Q-learning
Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such s...
Michael Kaisers, Karl Tuyls
JMLR
2002
138views more  JMLR 2002»
13 years 9 months ago
Learning Probabilistic Models of Link Structure
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning...
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin...
ATAL
2008
Springer
14 years 18 hour ago
Artificial agents learning human fairness
Recent advances in technology allow multi-agent systems to be deployed in cooperation with or as a service for humans. Typically, those systems are designed assuming individually ...
Steven de Jong, Karl Tuyls, Katja Verbeeck
CSE
2009
IEEE
14 years 1 months ago
Development and Validation of an Agent-Based Simulation Model of Juvenile Delinquency
This paper describes the development and validation of a dynamic multi-agent model to simulate social learning of adolescence-limited criminal behaviour. The parameters of the age...
Tibor Bosse, Charlotte Gerritsen, Michel C. A. Kle...
AAAI
2011
12 years 10 months ago
User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models
With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy conc...
Justin Cranshaw, Jonathan Mugan, Norman M. Sadeh