@inproceedings{LamRigPer01-EVOLEARN01-IC,
author = {Evelina Lamma and Fabrizio Riguzzi and Lu\'\i{}s Moniz Pereira},
title = {Belief Revision by {L}amarckian Evolution},
booktitle = {Applications of Evolutionary Computing : EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM, Como, Italy, April 18-20, 2001, Proceedings},
editor = {E.J.W. Boers and J. Gottlieb and P.L. Lanzi and R.E. Smith and S. Cagnoni and E. Hart and G.R. Raidl and H. Tijink},
publisher = {Springer-Verlag},
note = {The original publication is available at \url{http://www.springerlink.com}},
series = {Lecture Notes on Computer Science},
abstract = {We propose a multi-agent genetic algorithm to accomplish
belief revision. The algorithm implements a new evolutionary
strategy resulting from a combination of Darwinian and
Lamarckian approaches. Besides encompassing the Darwinian
operators of selection, mutation and crossover, it comprises a
Lamarckian operator that mutates the genes in a chromosome that
code for the believed assumptions. These self mutations are
performed as a consequence of the chromosome phenotype's
experience obtained while solving a belief revision problem.
They are directed by a belief revision procedure which relies on
tracing the logical derivations leading to inconsistency of
belief, so as to remove the latter's support on the gene coded
assumptions, by mutating the genes.},
volume = {2037},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamPerRig-EVOLEARN01.pdf},
http = {http://www.springerlink.com/content/8a3v3f22ygnp4l63/},
year = {2001},
month = apr,
pages = {404–413},
address = {Heidelberg, \Germany},
keywords = {Genetic_Algorithms,Theory_Revision},
issn = {0302-9743},
copyright = {Springer},
doi = {10.1007/3-540-45365-2_42},
scopus = {2s2.084958052631},
wos = {WOS:000174203500042},
isbn = {3540419209}
}
@article{AlfDelLam01-ALP-INVJ,
author = {Jos\'{e} J\'{u}lio Alferes and Pierangelo Dell'Acqua and Evelina Lamma
and Jo{\~a}o Alexandre Leite and Lu\'{i}s Moniz Pereira and Fabrizio Riguzzi},
title = {A Logic Based Approach to Multi-Agent Systems},
journal = {The Association for Logic Programming Newsletter},
year = {2001},
volume = {14},
number = {3},
month = aug,
publisher = {The Association for Logic Programming},
address = {\London, \UK},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/AlfDelLam-ALP01.pdf}
}
@inproceedings{LamManMel01-IDAMAP01-IW,
author = {Evelina Lamma and Marco Manservigi and Paola Mello and Annamaria Nanetti
and Fabrizio Riguzzi and Sergio Storari },
title = {The Automatic Discovery of Alarm Rules for the Validation of Microbiological Data},
booktitle = {6th Internationl Workshop on Intelligent Data Analysis In
Medicine And Pharmacology (IDAMAP2001)},
year = {2001},
month = sep,
pages = {1--7},
address = {\London, \UK},
editor = {Bellazzi, R. and Zupan, B. and Liu, X.},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamManMel-IDAMAP01.pdf}
}
@article{LamMaeMel01-ENTCS-IJ,
author = {Evelina Lamma and Leonardo Maestrami and Paola Mello and Fabrizio Riguzzi
and Sergio Storari},
title = {Rule-based Programming for Building Expert Systems: a Comparison in the Microbiological Data Validation and Surveillance Domain},
journal = {Electronic Notes in Theoretical Computer Science},
volume = {59},
issue = {4},
publisher = {Elsevier Science Publishers},
editor = {Mark van den Brand and Rakesh Verma},
year = {2001},
address = {Amsterdam, \TheNetherlands},
doi = {10.1016/S1571-0661(04)00299-3},
url = {http://www.sciencedirect.com/science/article/pii/S1571066104002993},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamMaeMel-ENTCS01.pdf},
month = sep,
abstract = {In this work, we compare three rule-based programming tools used for building an
expert system for microbiological laboratory data validation and bacteria infections
monitoring. The first prototype of the system was implemented in KAPPA-PC. We
report on the implementation and performance by comparing KAPPA-PC with two
other more recent tools, namely JESS and ILOG JRULES. In order to test each
tool we realized three simple test applications capable to perform some tasks that
are peculiar of our expert system.},
keywords = {Expert Systems, Knowledge-based Systems, Microbiology}
}
@inproceedings{LamMelNan01-ISMDA01-IC,
author = {Evelina Lamma and Paola Mello and Annamaria Nanetti and Gianluca Poli and
Fabrizio Riguzzi and Sergio Storari},
title = {An Expert System for Microbiological Data Validation and Surveillance},
booktitle = {Medical Data Analysis: Second International Symposium, {ISMDA} 2001 Madrid, Spain, October 8-9, 2001 Proceedings},
volume = {2199},
abstract = {In this work, we describe a system for microbiological laboratory data
validation and bacteria infections monitoring. In the following sections we
report about the first results we have obtained with a prototype that adopts a
knowledge-base approach for identifying critical situations and correspondingly
issuing alarms. The knowledge base has been obtained from international
standard guidelines for microbiological laboratory practice and from expert
suggestions.},
keywords = {Expert Systems, Microbiology},
series = {{Lecture Notes on Computer Science}},
publisher = {Springer Verlag},
note = {The original publication is available at \url{http://www.springerlink.com}},
year = {2001},
address = {Heidelberg, \Germany},
pages = {153--160},
month = oct,
editor = {Crespo, J. and Maojo, V.and Martin, F.},
isbn = {3-540-42734-1},
issn = {0302-9743},
doi = {10.1007/3-540-45497-7},
url = {http://www.springerlink.com/content/dmlnfr2k6havakvj/},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamMelNan-ISMDA01.pdf},
copyright = {Springer},
scopus = {2-s2.0-0005697191}
}
@inproceedings{LamRigPer01a-CLIMA01-IW,
author = {Evelina Lamma and Fabrizio Riguzzi and Lu\'\i{}s Moniz Pereira},
title = {Belief Revision by Multi-Agent Genetic Search},
booktitle = {{ICLP}01 2nd International Workshop on Computational Logic
for Multi-Agent Systems ({CLIMA}01)},
abstract = {The revision of beliefs is an important general purpose functionality that an agent must exhibit.
The agent usually needs to perform this task in cooperation with other agents, because access to
knowledge and the knowledge itself are distributed in nature.
In this work, we propose a new approach for performing belief revision in a society of logic-based
agents, by means of a (distributed) genetic algorithm, where the revisable assumptions of each
agent are coded into chromosomes as bit-strings. Each agent by itself locally performs a genetic
search in the space of possible revisions of its knowledge, and exchanges genetic information by
crossing its revisable chromosomes with those of other agents.
We have performed experiments comparing the evolution in beliefs of a single agent informed of the
whole of knowledge, to that of a society of agents, each agent accessing only part of the
knowledge. In spite that the distribution of knowledge increases the difficulty of the problem,
experimental results show that the solutions found in the multi-agent case are comparable in terms
of accuracy to those obtained in the single agent case.
The genetic algorithm we propose, besides encompassing the Darwinian operators of selection,
mutation and crossover, also comprises a Lamarckian operator that mutates the genes in a chromosome
as a consequence of the chromosome phenotype's individual experience obtained while solving a
belief revision problem. These chromosomic mutations are directed by a logic-based belief revision
procedure that relies on tracing the logical derivations leading to inconsistency of belief, so as
to remove these derivations' support on the gene coded assumptions, effectively by mutating the
latter. Because of the use a Lamarckian operator, and following the literature, the genes in these
chromosomes that are modified by the Lamarckian operator are best dubbed ``memes'', since they
code the memory of the experiences of an individual along its lifetime, besides being transmitted
to its progeny.
We believe our method to be important for situations where classical belief revision methods hardly
apply: those where environments are non-uniform and time changing. These can be explored by
distributed agents that evolve genetically to accomplish cooperative belief revision, if they use
our approach.},
year = {2001},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamPerRig-CLIMA01a.pdf},
month = dec,
keywords = {Genetic_Algorithms,Theory_Revision},
address = {Paphos, \Cyprus}
}
@inproceedings{LamRigPer01b-CLIMA01-IW,
author = {Evelina Lamma and Fabrizio Riguzzi and Lu\'\i{}s Moniz Pereira},
title = {A System for Multi-Agent Belief Revision by Genetic Search},
booktitle = {{ICLP}01 2nd International Workshop on Computational Logic
for Multi-Agent Systems ({CLIMA}01)},
year = {2001},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/LamPerRig-CLIMA01b.pdf},
month = dec,
keywords = {Genetic Algorithms,Theory Revision, Logic Prograimming, Belief Revision, Multi-Agent Systems},
address = {Paphos, \Cyprus},
abstract = {We consider a definition of the belief revision prob-
lem that consists in removing a contradiction from
an extended logic program by modifying the
truth value of a selected set of literals called revis-
ables. The program contains as well clauses with
false in the head, representing integrity constraints. Any model of the program must ensure
that the body of integrity constraints be false for
the program to be non-contradictory. Contradiction
may also arise in an extended logic program when
both a literal L and its opposite :L are obtainable
in the model of the program. Such a problem has
been widely studied in the literature, and various so-
lutions have been proposed that are based on
abductive logic proof procedures.
The system performs belief revision in a society
of logic-based agents, by means of a (distributed)
genetic algorithm. The problem can be modeled by
means of a genetic algorithm, by assigning to each
revisable of a logic program a gene in a chromosome.
In the case of a two-valued revision, the gene will
have the value 1 if the corresponding revisable is true
and the value 0 if the revisable is false. The fitness
function that is used in this case is represented in
part by the percentage of integrity constraints that
are satisfied by a chromosome.}
}
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