Plan Recognition in Stories and in Life
نویسندگان
چکیده
Plan recognition does not work the same way in stories and in "real life, (people tend to jump to conclusions more in sto ries). We present a theory of this, for the particular case of how objects in stories (or in life) influence plan recognition decisions. We provide a Bayesian network formaliza tion of a simple first-order theory of plans, and show how a particular network param eter seems to govern the difference between "life-like" and "story-like" response. We then show why this parameter would be in fluenced (in the desired way) by a model of speaker (or author) topic selection which assumes that facts in stories are typically "relevant".
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