Multi-agent reinforcement learning: independent vs. cooperative agents
Readings in agents
A Mixture Approach to Novelty Detection Using Training Data with Outliers
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Neural Networks for Novelty Detection in Airframe Strain Data
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Information Sciences: an International Journal
Functional knowledge exchange within an intelligent distributed system
ARCS'07 Proceedings of the 20th international conference on Architecture of computing systems
Fast and efficient training of RBF networks
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Organic Computing - Understanding Complex Systems
Organic Computing - Understanding Complex Systems
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Collaborative learning with logic-based models
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Online Intrusion Alert Aggregation with Generative Data Stream Modeling
IEEE Transactions on Dependable and Secure Computing
A survey and taxonomy of on-chip monitoring of multicore systems-on-chip
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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Intelligent agents often have the same or similar tasks and sometimes they cooperate to solve a given problem. These agents typically know how to observe their local environment and how to react on certain observations, for instance, and this knowledge may be represented in form of rules. However, many environments are dynamic in the sense that from time to time novel rules are required or old rules become obsolete. In this article we propose and investigate new techniques for knowledge acquisition by novelty detection and reaction as well as obsoleteness detection and reaction that an agent may use for self-adaptation to new situations. For that purpose we consider classifiers based on probabilistic rules. Premises of new rules are learned autonomously while conclusions are either obtained from human experts or from other agents which have learned appropriate rules in the past. By means of knowledge exchange, agents will efficiently be enabled to cope with situations they were not confronted with before. This kind of collaborative intelligence follows the human archetype: Humans are able to learn from each other by communicating learned rules. We demonstrate some properties of the knowledge acquisition techniques using artificial data as well as data from the field of intrusion detection.