TY - JOUR
T1 - A configurable Rete-OO engine for reasoning with different types of imperfect information
AU - Sottara, Davide
AU - Mello, Paola
AU - Proctor, Mark
N1 - Funding Information:
The authors would like to thank the DROOLS team for their support, and the anonymous reviewers for their precious comments on how to improve the quality of this work, partially supported by the MIUR PRIN07 project scheme.
PY - 2010
Y1 - 2010
N2 - The RETE algorithm is a very efficient option for the development of a rule-based system, but it supports only boolean, first order logic. Many real-world contexts, instead, require some degree of vagueness or uncertainty to be handled in a robust and efficient manner, imposing a trade-off between the number of rules and the cases that can be handled with sufficient accuracy. Thus, in the first part of the paper, an extension of RETE networks is proposed, capable of handling a more general inferential process, which actually includes several types of schemes for reasoning with imperfect information. In particular, the architecture depends on a number of configuration parameters which could be set by the user, individually or as a whole for the entire rule base. The second part, then, shows how an appropriate combination of parameters can be used to emulate some of the most common, specialized engines: 3-valued logic, classical certainty factors, fuzzy, many-valued logic and Bayesian networks.
AB - The RETE algorithm is a very efficient option for the development of a rule-based system, but it supports only boolean, first order logic. Many real-world contexts, instead, require some degree of vagueness or uncertainty to be handled in a robust and efficient manner, imposing a trade-off between the number of rules and the cases that can be handled with sufficient accuracy. Thus, in the first part of the paper, an extension of RETE networks is proposed, capable of handling a more general inferential process, which actually includes several types of schemes for reasoning with imperfect information. In particular, the architecture depends on a number of configuration parameters which could be set by the user, individually or as a whole for the entire rule base. The second part, then, shows how an appropriate combination of parameters can be used to emulate some of the most common, specialized engines: 3-valued logic, classical certainty factors, fuzzy, many-valued logic and Bayesian networks.
KW - "fuzzy" and probabilistic reasoning
KW - Inference engines
KW - nonmonotonic reasoning and belief revision
KW - rule-based processing
KW - uncertainty
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U2 - 10.1109/TKDE.2010.125
DO - 10.1109/TKDE.2010.125
M3 - Article
AN - SCOPUS:77956988278
SN - 1041-4347
VL - 22
SP - 1535
EP - 1548
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 11
M1 - 5551128
ER -