Abstract
The present work describes some aspects related to the
utility/swamping problem in ADAPtER, a multimodal reasoning system
combining Case-Based Reasoning and Model-Based Reasoning
for diagnostic problem solving. A detailed set of experiments allowed
us to analyse the average behavior of the system with respect
to a given domain, in terms of performance of the whole system and
its components. Such experiments pointed out that the increasing of
the size of the case memory reduces the need for solving problem
from scratch, but is the main responsible for the arising of the utility
problem in ADAPtER. As a consequence, particular attention is
paid to the problem of dynamically maintaining under control the
growth of the case memory. We propose two learning strategies implementing
a dynamic approach to case memory management. Such
strategies allow the system to dynamically add or replace cases from
memory, in order to keep under control both case memory size and
content. Experimental testing of the above strategies suggests that
their adoption can greatly mitigate the over-sizing of the case memory.
Lingua originale | Inglese |
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Pagine | 73-78 |
Numero di pagine | 6 |
Stato di pubblicazione | Pubblicato - 1 gen 1998 |
Evento | 13th European Conference on Artificial Intelligence (ECAI 98) - Brighton, UK Durata: 1 gen 1998 → … |
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???event.eventtypes.event.conference??? | 13th European Conference on Artificial Intelligence (ECAI 98) |
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Città | Brighton, UK |
Periodo | 1/01/98 → … |
Keywords
- Case base maintenance
- Case-Based Reasoning