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TSMC
2008
146views more  TSMC 2008»
15 years 7 months ago
Decentralized Learning in Markov Games
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is tha...
Peter Vrancx, Katja Verbeeck, Ann Nowé
JSAC
2007
139views more  JSAC 2007»
15 years 7 months ago
Reverse-Engineering MAC: A Non-Cooperative Game Model
— This paper reverse-engineers backoff-based random-access MAC protocols in ad-hoc networks. We show that the contention resolution algorithm in such protocols is implicitly part...
Jang-Won Lee, Ao Tang, Jianwei Huang, Mung Chiang,...
ATAL
2007
Springer
16 years 1 months ago
A globally optimal algorithm for TTD-MDPs
In this paper, we discuss the use of Targeted Trajectory Distribution Markov Decision Processes (TTD-MDPs)—a variant of MDPs in which the goal is to realize a specified distrib...
Sooraj Bhat, David L. Roberts, Mark J. Nelson, Cha...
GECCO
2010
Springer
244views Optimization» more  GECCO 2010»
15 years 7 months ago
Implicit fitness and heterogeneous preferences in the genetic algorithm
This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested a...
Justin T. H. Smith
AI
2008
Springer
15 years 7 months ago
Alternating-offers bargaining with one-sided uncertain deadlines: an efficient algorithm
In the arena of automated negotiations we focus on the principal negotiation protocol in bilateral settings, i.e. the alternatingoffers protocol. In the scientific community it is...
Nicola Gatti, Francesco Di Giunta, Stefano Marino