This tutorial will offer a detailed introduction to the Abstract Meaning Representation (AMR) formalism and its use for sentence semantics in NLP. Our goals are twofold. First, we will describe the nature and design principles behind the representation, and demonstrate that it can be practical for annotation. Participants will be coached in the basics of annotation so that, when working with AMR data in the future, they will appreciate the benefits and limitations of the process by which it was created. Second, we will survey the state of the art for computation with AMRs. This will focus on the task of parsing English text into AMR graphs, which requires algorithms for alignment, for structured prediction, and for statistical learning. We will also discuss graph grammar formalisms that have been recently developed, and future applications such as AMR-based machine translation and summarization.