Masaru.Tomita@A.CS.CMU.EDU (11/25/86)
RUM: A Layered Architecture for Reasoning with Uncertainty
Piero P. Bonissone
General Electric Corporate Research and Development
P.O. Box 8, K1-5C32A, Schenectady, New York 12301
3:30pm, WeH5409
New reasoning techniques for dealing with uncertainty in Expert Systems
have been embedded in RUM, a Reasoning with Uncertainty Module. RUM is an
integrated software tool based on a frame system (KEE) that is implemented
in an object oriented language. RUM's capabilities are subdivided into
three layers: Representation, Inference, and Control.
The Representation layer is based on frame-like data structures that
capture the uncertainty information used in the inference layer and the
uncertainty meta-information used in the control layer. Linguistic
probabilities are used to describe lower and upper bounds of the certainty
measure attached to a Well Formed Formula (wff). The source and the
conditions under which the information was obtained represent the
non-numerical meta-information.
The Inference layer provides the uncertainty calculi to perform the
intersection, detachment, union, and pooling of the information. Five
uncertainty calculi, based on their underlying Triangular norms (T-norms),
are used in this layer.
The Control layer uses the meta-information to select the appropriate
calculus for each context and to resolve eventual ignorance or conflict in
the information. This layer enables the programmer to declaratively
express the local (context dependent) meta-knowledge that will substitute
the global assumptions traditionally used in uncertain reasoning.
RUM has been tested and validated in a sequence of experiments in naval
situation assessment (SA). These experiments consists in determining
report/track correlation, platform location, and platform typing. The
testbed environment for developing these experiments has been provided by
LOTTA, a symbolic simulator implemented in Zetalisp Flavors, the object
oriented language of the Lisp Machine. This simulator maintains
time-varying situations in a multi-player antagonistic game where players
must make decisions in light of uncertain and incomplete data. RUM has
been used to assist one of the LOTTA players to perform the SA task.
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