Imagistic Reasoning Square, Cambrl@e. MA 02139 KENNETH YIP MIT Ar Ât@cla[ Intelligence Laboratory, 54.5 Technology FENG ZHAO Department Columbus, of Computer OH 43210 and Information Sczence, The OhLo State Un[uerslty, 2015 Ned Auenue, ELlSHA SACKS 1398 Computer Science BuddLng, purdue UnlL,ersLty, West Lafayette, IN 47907-1398 Imagistic reasoning is a new paradigm for understanding sensory data and controlling environments based on the construction, interpretation, and manipulation of image-like, analogue representations of physical systems. The reasoning is primarily perceptual and only secondarily symbolic. In the past decade, we have built several imagistic reasoners that perform at an expert level on scientific problems that defy current analytical methods, including helping us solve open problems. We hypothesize that much of scientific reasoning is imagistic and that this reasoning is best automated by imagistic algorithms. The classical artificial intelligence architecture Âa central deductive reasoner operating on symbolic predicates delivered by low-level perceptual preprocessors Âis unsuitable for these tasks. Imagistic reasoners are faster and more efficient because they trade many inferences for sensing and action. Their behavior is easier to understand and debug because they deal directly with geometric structures and their interactions. Imagistic reasoning organizes comaround information-rich, putations analogue representations of physical systems.
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Imagistic reasoning
Yip, Kenneth; Zhao, Feng; Sacks, Elisha
Follow ACM Computing Surveys (CSUR)
, Volume 27 (3)
Association for Computing Machinery – Sep 1, 1995
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