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Intensive computations required for sensing and processing perceptual information can impose significant burdens on personal computer systems. We explore several policies for selective perception in SEER, a multimodal system for recognizing office activity that relies on a layered Hidden Markov Model representation. We review our efforts to employ expected-value-of-information (EVI) computations to limit sensing and analysis in a context-sensitive manner. We discuss an implementation of adoi:10.1145/958432.958442 dblp:conf/icmi/OliverH03 fatcat:xcvs3ktrfbdnhfhne5hhzd73qu