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» Evaluating Adaptive Problem Selection
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EWCBR
2008
Springer
13 years 10 months ago
Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning
This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve...
Bryan Auslander, Stephen Lee-Urban, Chad Hogg, H&e...
AOSE
2005
Springer
14 years 2 months ago
Using the Analytic Hierarchy Process for Evaluating Multi-Agent System Architecture Candidates
Abstract. Although much effort has been spent on suggesting and implementing new architectures of Multi-Agent Systems (MAS), the evaluation and comparison of these has often been d...
Paul Davidsson, Stefan J. Johansson, Mikael Svahnb...
CN
2008
106views more  CN 2008»
13 years 7 months ago
Optimization models and methods for planning wireless mesh networks
In this paper novel optimization models are proposed for planning Wireless Mesh Networks (WMNs), where the objective is to minimize the network installation cost while providing f...
Edoardo Amaldi, Antonio Capone, Matteo Cesana, Ila...
SELMAS
2005
Springer
14 years 2 months ago
Characterization and Evaluation of Multi-agent System Architectural Styles
We argue that it is useful to study classes of Multi-Agent System (MAS) architectures, corresponding to architectural styles in addition to particular tures. In this work we focus ...
Paul Davidsson, Stefan J. Johansson, Mikael Svahnb...
PRIS
2008
13 years 10 months ago
Comparison of Adaboost and ADTboost for Feature Subset Selection
Abstract. This paper addresses the problem of feature selection within classification processes. We present a comparison of a feature subset selection with respect to two boosting ...
Martin Drauschke, Wolfgang Förstner