Foundations for an intelligent business logic engine using genetic programming and RuleML-based services

Abstract

Service Oriented Architectures (SOAs) involve interacting business applications loosely interconnected by published services. In business environments, the content of these published services is in a constant state of change. A natural choice for the automatic synthesis and response to constantly changing service logic is an inherently adaptive, or evolutionary, system. This paper proposes and provides the design foundation for an automatic RuleML-based business rule recommendation engine using Genetic Programming (GP). The system proposed would actively adapt to rules exposed as web services from internal or external providers in order to automatically produce rule-based recommendations for the competitive advantage of the enterprise. This paper assumes the use of RuleML as the language used to communicate the services between providers, and describes the process whereby the rules can be translated and encoded for analysis in a GP system. Following encoding, the implementation details whereby the algorithm would automatically evaluate and generate new RuleML-based recommendations are described.