TY - GEN
T1 - Fuzzy conformance checking of observed behaviour with expectations
AU - Bragaglia, Stefano
AU - Chesani, Federico
AU - Mello, Paola
AU - Montali, Marco
AU - Sottara, Davide
PY - 2011
Y1 - 2011
N2 - In some different research fields a research issue has been to establish if the external, observed behaviour of an entity is conformant to some rules/specifications/expectations. Research areas like Multi Agent Systems, Business Process, and Legal/Normative systems, have proposed different characterizations of the same problem, named as the conformance problem. Most of the available systems, however, provide only simple yes/no answers to the conformance issue. In this paper we introduce the idea of a gradual conformance, expressed in fuzzy terms. To this end, we present a system based on a fuzzy extension of Drools, and exploit it to perform conformance tests. In particular, we consider two aspects: the first related to fuzzy ontological aspects, and the second about fuzzy time-related aspects. Moreover, we discuss how to conjugate the fuzzy contributions from these aspects to get a single, fuzzy score representing a conformance degree.
AB - In some different research fields a research issue has been to establish if the external, observed behaviour of an entity is conformant to some rules/specifications/expectations. Research areas like Multi Agent Systems, Business Process, and Legal/Normative systems, have proposed different characterizations of the same problem, named as the conformance problem. Most of the available systems, however, provide only simple yes/no answers to the conformance issue. In this paper we introduce the idea of a gradual conformance, expressed in fuzzy terms. To this end, we present a system based on a fuzzy extension of Drools, and exploit it to perform conformance tests. In particular, we consider two aspects: the first related to fuzzy ontological aspects, and the second about fuzzy time-related aspects. Moreover, we discuss how to conjugate the fuzzy contributions from these aspects to get a single, fuzzy score representing a conformance degree.
KW - expectations
KW - fuzzy conformance
KW - production rule systems
KW - time reasoning
UR - http://www.scopus.com/inward/record.url?scp=80053007683&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053007683&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23954-0_10
DO - 10.1007/978-3-642-23954-0_10
M3 - Conference contribution
AN - SCOPUS:80053007683
SN - 9783642239533
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 80
EP - 91
BT - AI*IA 2011
T2 - 12th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2011
Y2 - 15 September 2011 through 17 September 2011
ER -