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Marc Goodman

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2 papers

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AAAI Conference 1994 Conference Paper

Results on Controlling Action with Projective Visualization

Marc Goodman

A projective visualizer learns to simulate events in the external world through observation of the world. These simulations are used to evaluate potential actions on the basis of their probable outcomes. Results are given that indicate, 1). the error rate for projective visualization is sub-linear as the system projects farther into the future, 2). the error rate is inversely proportional to the number of cases, 3). a simple domain model can be used to reduce the effect of compounding error, and 4). projection can be used to increase the performance of an agent, even when this projection is imperfect.

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AAAI Conference 1993 Conference Paper

Projective Visualization: Acting from Experience

Marc Goodman

This paper describes Projective Visualization, which uses previous observation of a process or activity to project the results of an agent’ s actions into the future. Actions which seem likely to succeed are selected and applied. Actions which seem likely to fail are rejected, and other actions can be generated and evaluated. This paper presents a description of the architecture for Projective Visualization, preliminary results on learning to act from observations of a reactive system, and a comparison of two types of Case Projection (how situations are projected into the future).

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