Chen, StephenLiaskos, Sotirios2015-12-162015-12-162015-08-202015-12-16http://hdl.handle.net/10315/30739Customers are getting increasingly involved in the design of the products and services they choose by specifying their desired characteristics. As a result, configuration systems have become essential technologies to support the development of mass-customization business models. These technologies facilitate the configuration of complex products and services that otherwise could generate many incorrect configurations and overwhelm users with confusion. This thesis studies the problem of optimizing the user interaction in a configuration process – as in minimizing the number of questions asked to a user in order to obtain a fully-specified product or service configuration. The work carried out builds upon a previously existing framework to optimize the process of configuring a software system, and focuses on improving its efficiency and generalizing its application to a wider range of configuration domains. Two solution methods along with two alternative ways of specifying the configuration models are proposed and studied on different configuration scenarios. The experimental study evidences that the introduced solutions overcome the limitations of the existing framework, resulting in more suitable algorithms to work with models involving a large number of configuration variables.enAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Artificial intelligenceComputer scienceInformation technologyElectronic Thesis or Dissertation2015-12-16Knowledge-based configurationMarkov decision processesReinforcement learningGenetic algorithms