In this book, tools and techniques developed in argumentation theory and artificial intelligence are applied to problems of analyzing and evaluating argumentation used in law. Argumentation is a set of context-sensitive practical methods used to help a user identify, analyze, and evaluate arguments, especially common ones of the kind often found in everyday discourse. In the past it was the prevalent assumption that the deductive model of valid inference was the cornerstone of rational thinking. There has now been a paradigm shift to highly knowledge-dependent models of reasoning under conditions of uncertainty where a conclusion is drawn on a basis of tentative acceptance on a balance of considerations. Argumentation based on this new notion of argument, also called informal logic, is now being widely used as a new model of practical reasoning in computing, especially in agent communication in multiagent systems. Recent work in artificial intelligence and law has recently turned more and more to argumentation as a rich, interdisciplinary area of research that can furnish methods, especially in those areas of law related to evidence and reasoning (Bench-Capon, 1995; Gordon, 1995; Prakken, 2001a; Verheij, 2005; Walton, 2005). Generally, techniques and results of argumentation “have found a wide range of applications in both theoretical and practical branches of artificial intelligence and computer science” (Rahwan, Moraitis, and Reed, 2005, p. I). At the same time, artificial intelligence in law has coincided with the new evidence scholarship in law (Tillers, 2002).