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» Learning the required number of agents for complex tasks
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ATAL
2011
Springer
12 years 7 months ago
Learning action models for multi-agent planning
In multi-agent planning environments, action models for each agent must be given as input. However, creating such action models by hand is difficult and time-consuming, because i...
Hankz Hankui Zhuo, Hector Muñoz-Avila, Qian...
PPSN
2010
Springer
13 years 5 months ago
Feature Selection for Multi-purpose Predictive Models: A Many-Objective Task
The target of machine learning is a predictive model that performs well on unseen data. Often, such a model has multiple intended uses, related to different points in the tradeoff ...
Alan P. Reynolds, David W. Corne, Michael J. Chant...
ATAL
2011
Springer
12 years 7 months ago
Online anomaly detection in unmanned vehicles
Autonomy requires robustness. The use of unmanned (autonomous) vehicles is appealing for tasks which are dangerous or dull. However, increased reliance on autonomous robots increa...
Eliahu Khalastchi, Gal A. Kaminka, Meir Kalech, Ra...
KAIS
2010
80views more  KAIS 2010»
13 years 6 months ago
Semi-supervised learning by disagreement
In many real-world tasks there are abundant unlabeled examples but the number of labeled training examples is limited, because labeling the examples requires human efforts and exp...
Zhi-Hua Zhou, Ming Li
AROBOTS
2011
13 years 2 months ago
Time-extended multi-robot coordination for domains with intra-path constraints
Abstract— Many applications require teams of robots to cooperatively execute complex tasks. Among these domains are those where successful coordination solutions must respect con...
Edward Gil Jones, M. Bernardine Dias, Anthony Sten...