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The need to autonomically optimize end-user service experience in near real time has been identified in the literature in recent years. Management systems that monitor end-user service session context exist but approaches that estimate end-user service experience from session context do not analyze the compliance of that experience with user expectations. Approaches that optimize end-user service delivery are not applicable to arbitrary services; they either optimize specific service types or use general mechanisms that do not consider service experience. The lack of a holistic model for end-user service management is a barrier to autonomic end-user service optimization. This paper presents Aesop, an approach addressing autonomic optimization of end-user service delivery using semantic-based techniques. Its knowledge base uses the End-User Service Analysis and Optimization ontology, which models the end-user service management domain and partitions knowledge that varies over time for efficient access. The Aesop Engine executes an autonomic loop in near real time, which runs semantic algorithms to monitor sessions, analyze their compliance with expectations, and plan and execute optimizations on service delivery networks. The algorithms are efficient because they operate on small partitioned subsets of the Knowledge Base held as separate self-contained models at run time. An Aesop implementation was evaluated on a home area network test bed where compliance of service sessions with expectations when optimization was active was compared with compliance of an identical set of sessions when optimization was inactive. Significant improvements were observed on compliance levels of high priority sessions in all experimental scenarios, with compliance levels more than doubled in some cases.