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Aggregated fuzzy answer set programming

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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Fuzzy Answer Set programming (FASP) is an extension of answer set programming (ASP), based on fuzzy logic. It allows to encode continuous optimization problems in the same concise manner as ASP allows to model combinatorial problems. As a result of its inherent continuity, rules in FASP may be satisfied or violated to certain degrees. Rather than insisting that all rules are fully satisfied, we may only require that they are satisfied partially, to the best extent possible. However, most approaches that feature partial rule satisfaction limit themselves to attaching predefined weights to rules, which is not sufficiently flexible for most real-life applications. In this paper, we develop an alternative, based on aggregator functions that specify which (combination of) rules are most important to satisfy. We extend upon previous work by allowing aggregator expressions to define partially ordered preferences, and by the use of a fixpoint semantics.
Keywords
INFORMATION, OPERATORS, UNCERTAINTY, Fuzzy Logic, Answer Set Programming

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Citation

Please use this url to cite or link to this publication:

Chicago
Janssen, Jeroen, Steven Schockaert, Dirk Vermeir, and Martine De Cock. 2011. “Aggregated Fuzzy Answer Set Programming.” Annals of Mathematics and Artificial Intelligence 63 (2): 103–147.
APA
Janssen, Jeroen, Schockaert, S., Vermeir, D., & De Cock, M. (2011). Aggregated fuzzy answer set programming. ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE, 63(2), 103–147.
Vancouver
1.
Janssen J, Schockaert S, Vermeir D, De Cock M. Aggregated fuzzy answer set programming. ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE. 2011;63(2):103–47.
MLA
Janssen, Jeroen, Steven Schockaert, Dirk Vermeir, et al. “Aggregated Fuzzy Answer Set Programming.” ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 63.2 (2011): 103–147. Print.
@article{2917819,
  abstract     = {Fuzzy Answer Set programming (FASP) is an extension of answer set programming (ASP), based on fuzzy logic. It allows to encode continuous optimization problems in the same concise manner as ASP allows to model combinatorial problems. As a result of its inherent continuity, rules in FASP may be satisfied or violated to certain degrees. Rather than insisting that all rules are fully satisfied, we may only require that they are satisfied partially, to the best extent possible. However, most approaches that feature partial rule satisfaction limit themselves to attaching predefined weights to rules, which is not sufficiently flexible for most real-life applications. In this paper, we develop an alternative, based on aggregator functions that specify which (combination of) rules are most important to satisfy. We extend upon previous work by allowing aggregator expressions to define partially ordered preferences, and by the use of a fixpoint semantics.},
  author       = {Janssen, Jeroen and Schockaert, Steven and Vermeir, Dirk and De Cock, Martine},
  issn         = {1012-2443},
  journal      = {ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE},
  keyword      = {INFORMATION,OPERATORS,UNCERTAINTY,Fuzzy Logic,Answer Set Programming},
  language     = {eng},
  number       = {2},
  pages        = {103--147},
  title        = {Aggregated fuzzy answer set programming},
  url          = {http://dx.doi.org/10.1007/s10472-011-9256-8},
  volume       = {63},
  year         = {2011},
}

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