KSL-00-03
## Reinterpretation of Causal Order Graphs Towards Effective Explanation Generation Using Compositional Modeling

**Reference: **
Kumar, T. K. S. Reinterpretation of Causal Order Graphs Towards Effective Explanation Generation Using Compositional Modeling. The Fourteenth International Workshop on Qualitative Reasoning, Mexico, 2000.

**Abstract:** Compositional modeling provides a number of advantages over
conventional simulation software in explanation generation mainly
because of its causal interpretation of data. However, little work
was done with regard to a supporting algorithm that can generate
cogent explanations from the simulation values and causal graphs of
model parameters. Earlier attempts did not solve the problem of
irrelevant details introduced by using compositional modeling; as a
result of which misleading references resulted in attempting
explanation of device behavior. This was mainly because they were
based merely on equation tracing and did not try to infer anything
about the working phenomena from the causal order graph. We present
a domain independent algorithm that interprets causal order graphs
in terms of working template phenomena rather than in terms of
quantities defined in the equation model. A byproduct of this is in
capturing the user's psychology in terms of phenomena rather than in
terms of mathematical equations defined by some other person. The
explanation is in the form of natural language rather than graphs
of numerical variables. We also describe a number of extensions of
the algorithm to handle issues such as scalability and ranking by significance.

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