Sciweavers

Share
CORR
2000
Springer

Predicting the expected behavior of agents that learn about agents: the CLRI framework

9 years 3 months ago
Predicting the expected behavior of agents that learn about agents: the CLRI framework
We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the progression of an agent's error in its decision function, thereby telling us how the agent is expected to fare in the MAS. The equation relies on parameters which capture the agent's learning abilities, such as its change rate, learning rate and retention rate, as well as relevant aspects of the MAS such as the impact that agents have on each other. We validate the framework with experimental results using reinforcement learning agents in a market system, as well as with other experimental results gathered from the AI literature. Finally, we use PAC-theory to show how to calculate bounds on the values of the learning parameters. Multi-Agent Systems, Machine Learning, Complex Systems.
José M. Vidal, Edmund H. Durfee
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2000
Where CORR
Authors José M. Vidal, Edmund H. Durfee
Comments (0)
books