Preferences are fundamental to decision processes, because decision analysts must account for the preferences of the stakeholders who participate in these processes and are impacted by the decision outcomes. To support the elicitation of stakeholder preferences, many models, procedures and methodologies have been proposed. These approaches to preference elicitation and learning will become more and more important with the proliferation of semi-automated computerized interfaces and the adoption of decision support systems which build on increasingly large datasets. One of the major central tasks the decision analyst is to elicit the judgements and value systems of the decision makers (DMs), including their views on the problem, and to integrate the resulting information into a preference model from which recommendations can be derived. This preference elicitation activity can be tricky: the preferences expressed by the DMs can be imprecise, conflicting, unstable, time-dependent, yet they should be structured and synthesized into numerical values (or intervals of numerical values) concerning the parameters that characterize preferences in the decision model. For the domain Preference Elicitation and Learning, models, procedures and methodologies have been developed by researchers working in the field of Multiple Criteria Decision Aid but also in that of Artificial Intelligence. Their research has focused on the modeling, representation, elicitation, learning, aggregation, and management of preferences. This work has lead to the development of algorithms that can be embedded in methodological approaches to support different phases of the decision process, including the integration of the DM preferences, the elaboration of decision recommendations , and the understanding and acceptance of the decision recommendations by stakeholders.