Learning from others: Exchange of classification rules in intelligent distributed systems
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. (C) 2012 Elsevier B.V. All rights reserved.
classification uncertain knowledge rule exchange collaborative learning probabilistic modeling collective intelligence interestingness