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Conservative adaptation consists in a minimal change on a source case to be consistent with the target case, given the domain knowledge. It has been formalised in a previous work thanks to the AGM theory of belief revision applied to propositional logic. However, this formalism is rarely used in case-based reasoning systems. In this paper, conservative adaptation is extended to a more general representation framework, that includes also attribute-value formalisms. In this framework, a case is a class of case instances, which are elements of a metric space. Conservative adaptation is formalised in this framework and is extended to α-conservative adaptation, that relaxes the conservativeness. These approaches to adaptation in a metric space transform adaptation problems to well-formulated optimization problems. A running example in the cooking domain is used to illustrate the notions that are introduced.

Abstract. Our society needs and expects more high-value services. Such "knowledge-intensive" services can only be delivered if the necessary organizational and technical requirements are fulfilled. In addition, the cost-benefit analysis from the service provider point of view needs to be positive. Continuous improvement and goal-directed (partial) automation of such services is therefore of crucial importance. As a contribution to this we describe our current research vision for (partially) automated support of knowledge work(ers) based on intelligent information systems focusing on the use of experience. For the implementation of such a vision we base on the integration of approaches from artificial intelligence and software engineering. A "deep" integration of case-based reasoning and experience factory is a first successful step in this direction [33,28]. We envision the further integration of software product-lines and multi-agent systems as the next one.