Multi-agent reinforcement learning: independent vs. cooperative agents
Readings in agents
Uncertainty Management in Information Systems: From Needs to Solutions
Uncertainty Management in Information Systems: From Needs to Solutions
A Mixture Approach to Novelty Detection Using Training Data with Outliers
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Learning Classifier Systems Meet Multiagent Environments
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Reasoning about Uncertainty
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
Collaborative Knowledge Building by Smart Sensors
BT Technology Journal
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
D-SCIDS: distributed soft computing intrusion detection system
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Future scenarios of parallel computing: Distributed sensor networks
Journal of Visual Languages and Computing
Rule responder: RuleML-based agents for distributed collaboration on the pragmatic web
ICPW '07 Proceedings of the 2nd international conference on Pragmatic web
Sharing in teams of heterogeneous, collaborative learning agents
International Journal of Intelligent Systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Toward a generalized theory of uncertainty (GTU)--an outline
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
Adaptive agents applied to intrusion detection
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent 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
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
Quantitative Emergence -- A Refined Approach Based on Divergence Measures
SASO '10 Proceedings of the 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Online Intrusion Alert Aggregation with Generative Data Stream Modeling
IEEE Transactions on Dependable and Secure Computing
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Learning by an exchange of knowledge and experiences enables humans to act efficiently in a very dynamic environment. Thus, it would be highly desirable to enable intelligent distributed systems to behave in a way which follows that biological archetype. We believe that knowledge exchange will become increasingly important in many application areas such as intrusion detection, driver assistance, or robotics. Constituents of a distributed system such as software agents, cars equipped with smart sensors, or intelligent robots may learn from each other by exchanging knowledge in form of classification rules, for instance. This article proposes techniques for the exchange of classification rules that represent uncertain knowledge. For that purpose, we introduce methods for knowledge acquisition in dynamic environments, for gathering and using meta-knowledge about rules (i.e., experience), and for rule exchange in distributed systems. The methods are based on a probabilistic knowledge modeling approach. We describe the results of two case studies where we show that knowledge exchange (exchange of learned rules) may be superior to information exchange (exchange of raw observations, i.e. samples) and demonstrate that the use of experiences (meta-knowledge concerning the rules) may improve that rule exchange process further. Some possible real application scenarios are sketched briefly and an application in the field of intrusion detection in computer networks is elaborated in more detail.