Task-specific utility in a general Bayes net vision system

TEA is a task-oriented computer vision system that uses Bayes nets and a maximum expected-utility decision rule to choose a sequence of task-dependent and opportunistic visual operations on the basis of their cost and (present and future) benefit. The authors discuss technical problems regarding utilities, present TEA-1's utility function (which approximates a two-step lookahead), and compare it to various simpler utility functions in experiments with real and simulated scenes.<
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