Probabilistic Temporal Reasoning with Endogenous Change
  This paper presents a probabilistic model for reasoning about the state of a
system as it changes over time, both due to exogenous and endogenous
influences. Our target domain is a class of medical prediction problems that
are neither so urgent as to preclude careful diagnosis nor progress so slowly
as to allow arbitrary testing and treatment options. In these domains there is
typically enough time to gather information about the patient's state and
consider alternative diagnoses and treatments, but the temporal interaction
between the timing of tests, treatments, and the course of the disease must
also be considered. Our approach is to elicit a qualitative structural model of
the patient from a human expert---the model identifies important attributes,
the way in which exogenous changes affect attribute values, and the way in
which the patient's condition changes endogenously. We then elicit
probabilistic information to capture the expert's uncertainty about the effects
of tests and treatments and the nature and timing of endogenous state changes.
This paper describes the model in the context of a problem in treating vehicle
accident trauma, and suggests a method for solving the model based on the
technique of sequential imputation. A complementary goal of this work is to
understand and synthesize a disparate collection of research efforts all using
the name ?probabilistic temporal reasoning.? This paper analyzes related work
and points out essential differences between our proposed model and other
approaches in the literature.
