Towards Probabilistic Argumentation
ARGUMENTATION IN ARTIFICIAL INTELLIGENCE(2009)
摘要
All arguments share certain key similarities: they have a goal and some support for the goal, although the form of the goal
and support may vary dramatically. Human argumentation is also typically enthymematic, i.e., people produce and expect arguments that omit easily inferable information. In this chapter, we draw on the insights
obtained from a decade of research to formulate requirements common to computational systems that interpret human arguments
and generate their own arguments. To ground our discussion, we describe how some of these requirements are addressed by two
probabilistic argumentation systems developed by the User Modeling and Natural Language (UMNL) Group at Monash University:
the argument generation system nag (Nice Argument Generator) [18, 19, 20, 38, 39, 40], and the argument interpretation system bias (Bayesian Interactive Argumentation System) [7, 8, 34, 35, 36, 37].
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关键词
user model,natural language
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