This chapter explains the role of proof burdens and standards in argumentation, illustrates them using legal procedures, and surveys the history of research on computational models of these concepts. It also presents an original computational model which aims to integrate the features of these prior systems. The 'mainstream' conception of argumentation in the field of artificial intelligence is monological [6] and relational [14] . Argumentation is viewed as taking place against the background

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... f an inconsistent knowledge base, where the knowledge base is a set of propositions represented in some formal logic. Argumentation in this conception is a method for deducing warranted propositions from an inconsistent knowledge base. Which statements are warranted depends on attack relations among the arguments [10] which can be constructed from the knowledge base. The notions of proof standards and burden of proof become relevant only when argumentation is viewed as a dialogical process for making justified decisions. The input to the process is an initial claim or issue. The goal of the process is to clarify and decide the issues, and produce a justification of the decision which can withstand a critical evaluation by a particular audience. The role of the audience could be played by the respondent or a neutral-third party, depending on the type of dialogue. The output of this process consists of: 1) a set of claims, 2) the decision to accept or reject each claim, 3) a theory of the generalizations of the domain and the facts of the particular case, and 4) a proof justifying the decision of each issues, showing how the decision is supported by the theory. Notice that a theory or knowledge-base is part of the output of argumentation dialogues, not, as in the relational conception, its input. This is because, as has been