ADAPtER: An Integrated Diagnostic
System Combining Case-Based and
Abductive Reasoning

Luigi Portinale, Pietro Torasso

Dipartimento di Informatica - Universita di Torino (Italy)


Abstract. The aim of this paper is to describe the ADAPtER system, a
diagnostic architecture combining case-based reasoning with abductive reasoning 
and exploiting the adaptation of the solution of old episodes, in order
to focus the reasoning process. Domain knowledge is represented via a logical
model and basic mechanisms, based on abductive reasoning with consistency
constraints, have been defined for solving complex diagnostic problems involving 
multiple faults. The model-based component has been supplemented
with a case memory and adaptation mechanisms have been developed, in
order to make the diagnostic system able to exploit past experience in solving 
new cases. A heuristic function is proposed, able to rank the solutions
associated to retrieved cases with respect to the adaptation effort needed
to transform such solutions into possible solutions for the current case. We
will discuss some preliminary experiments showing the validity of the above
heuristic and the convenience of solving a new case by adapting a retrieved
solution rather than solving the new problem from scratch.
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