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SIGECOM
2009
ACM
114views ECommerce» more  SIGECOM 2009»
15 years 6 months ago
Policy teaching through reward function learning
Policy teaching considers a Markov Decision Process setting in which an interested party aims to influence an agent’s decisions by providing limited incentives. In this paper, ...
Haoqi Zhang, David C. Parkes, Yiling Chen
CIA
1998
Springer
15 years 4 months ago
Multiagent Systems in Information-Rich Environments
Information-rich environments are the open environments that characterize most of the modern applications of computing technology. The applications include ubiquitous information a...
Michael N. Huhns, Munindar P. Singh
ATAL
2006
Springer
15 years 3 months ago
A constraint optimization framework for fractured robot teams
In dangerous and uncertain environments initial plans must be revised. Communication failures hamper this replanning. We introduce fractured subteams as a novel formalism for mode...
Mary Koes, Katia P. Sycara, Illah R. Nourbakhsh
ATAL
2008
Springer
15 years 1 months ago
An agent-based electrical power market
This demonstration shows an agent-based model for the electricity power market, in which the optimal power flow is determined in a bottom-up fashion. Here, each agent controls a s...
Jaime Cerda Jacobo, David De Roure, Enrico H. Gerd...
ATAL
2010
Springer
15 years 29 days ago
To teach or not to teach?: decision making under uncertainty in ad hoc teams
In typical multiagent teamwork settings, the teammates are either programmed together, or are otherwise provided with standard communication languages and coordination protocols. ...
Peter Stone, Sarit Kraus