An Explicit Representation of Reasoning Failures

Michael T. Cox
Computer Science Department. Carnegie Mellon University
Pittsburgh, PA 15213-3891
mcox@cs.cmu.edu


Abstract. This paper focuses upon the content and the level of granularity at which representations 
for the mental world should be placed in case-based explainers that employ
introspective reasoning. That is, for a case-based reasoning system to represent thinking
about the self, about the states and processes of reasoning, at what level of detail should
one attempt to declaratively capture the contents of thought? Some claim that a mere set of
two mental primitives are sufficient to represent the utterances of humans concerning
verbs of thought such as I forgot his birthday. Alternatively, many in the CBR community 
have built systems that record elaborate traces of reasoning, keep track of knowledge
dependencies or inference, or encode much metaknowledge concerning the structure of
internal rules and defaults. The position here is that a system should be able instead to capture 
enough details to represent causally a common set of reasoning failure symptoms. I
propose a simple model of expectation-driven reasoning, derive a taxonomy of reasoning
failures from the model, and present a declarative representation of the failure symptoms
that have been implemented in a CBR simulation. Such representations enable a system to
explain reasoning failures by mapping from symptoms of the failures to causal factors
involved.
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