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CIIA
2009
15 years 26 days ago
Dynamic Scheduling in Petroleum Process using Reinforcement Learning
Petroleum industry production systems are highly automatized. In this industry, all functions (e.g., planning, scheduling and maintenance) are automated and in order to remain comp...
Nassima Aissani, Bouziane Beldjilali
ICCBR
2007
Springer
15 years 5 months ago
An Analysis of Case-Based Value Function Approximation by Approximating State Transition Graphs
We identify two fundamental points of utilizing CBR for an adaptive agent that tries to learn on the basis of trial and error without a model of its environment. The first link co...
Thomas Gabel, Martin Riedmiller
SIGECOM
2011
ACM
259views ECommerce» more  SIGECOM 2011»
14 years 2 months ago
Designing adaptive trading agents
ended abstract summarizes the research presented in Dr. Pardoe’s recently-completed Ph.D. thesis [Pardoe 2011]. The thesis considers how adaptive trading agents can take advantag...
David Pardoe, Peter Stone
GECCO
2006
Springer
175views Optimization» more  GECCO 2006»
15 years 3 months ago
A computational theory of adaptive behavior based on an evolutionary reinforcement mechanism
Two mathematical and two computational theories from the field of human and animal learning are combined to produce a more general theory of adaptive behavior. The cornerstone of ...
J. J. McDowell, Paul L. Soto, Jesse Dallery, Saule...
ATAL
2009
Springer
15 years 6 months ago
Adaptive learning in evolving task allocation networks
In this paper, we study multi-agent economic systems using a recent approach to economic modeling called Agent-based Computational Economics (ACE): the application of the Complex ...
Tomas Klos, Bart Nooteboom