C4.5: programs for machine learning
C4.5: programs for machine learning
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Multi-agent information classification using dynamic acquaintance lists
Journal of the American Society for Information Science and Technology
Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Incremental Algorithms for Hierarchical Classification
The Journal of Machine Learning Research
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
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Where users and agents, each with their own world model and expertise, work together it is essential to interpret both their beliefs correctly. It is therefore important to keep track of the differences of opinion that occur in such a way that it is understandable for both the agents as well as the user. This paper proposes a generic and flexible way or the user to interact with agents using a integrated world model. To enforce the user’s opinion a User Preference Redistribution rule (UPR) is proposed. Through a realistic numerical example we show the validity of this model and the new UPR in contrast to other belief conditioning rules.