author = {Fabrizio Riguzzi},
  title = {A Survey of Software Metrics},
  institution = {DEIS, Universit\`{a} di Bologna},
  year = 1996,
  number = {DEIS-LIA-96-010, LIA Series n.17},
  month = jul,
  pdf = {}
  author = {Antonis C. Kakas AND Fabrizio Riguzzi},
  title = {Abductive Concept Learning},
  institution = {{C}omputer {S}cience {D}epartment, University of {C}yprus},
  year = 1996,
  number = {TR-96-15},
  keywords = {Abduction, Negation, Integrity_Constraints},
  month = dec,
  pdf = {}
  author = {Fausto Gramantieri and Evelina Lamma and Paola Mello and Fabrizio Riguzzi},
  title = {A System for Measuring Function Points from Specifications},
  institution = {DEIS, Universit\`{a} di Bologna},
  year = 1997,
  number = {DEIS-LIA-97-006,  LIA Series n.23},
  month = apr,
  pdf = {}
  author = {Fabrizio Riguzzi},
  title = {Specification of the Application SuperSport with {ER}-{DFD}},
  institution = {Dipartimento di Ingegneria, Universit\`{a} di Ferrara},
  year = 2003,
  number = {CS-2003-01},
  month = jul,
  url = {}
  author = {Fabrizio Riguzzi},
  title = {{ALLPAD}: Approximate Learning of Logic Programs with Annotated Disjunctions},
  institution = {Dipartimento di Ingegneria, Universit\`{a} di Ferrara},
  year = 2006,
  number = {CS-2006-01},
  month = oct,
  pdf = {},
  abstract = {Logic Programs with Annotated Disjunctions (LPADs) provide
a simple and elegant framework for representing probabilistic knowledge
in logic programming. In this paper I consider the problem of learning
ground LPADs starting from a set of interpretations annotated with
their probability. I present the system ALLPAD for solving this problem.
ALLPAD modifies the previous system LLPAD in order to tackle real
world learning problems more effectively. This is achieved by looking for
an approximate solution rather than a perfect one. ALLPAD has been
tested on the problem of classifying proteins according to their tertiary
structures and the results compare favorably with most other approaches.},
  keywords = {Probabilistic Inductive Logic Programming, Statistical Relational Learning}
  author = { Riguzzi, Fabrizio},
  title = {The {SLGAD} Procedure for Inference on Logic Programs with Annotated Disjunctions},
  year = 2008,
  number = {CS-2008-01},
  institution = {Dipartimento di Ingegneria, Universit\`{a} di Ferrara},
  pdf = {},
  abstract = {Logic Programs with Annotated Disjunctions (LPADs) allow
to express probabilistic information in logic programming. The semantics
of an LPAD is given in terms of well founded models of the normal logic
programs obtained by selecting one disjunct from each ground LPAD
clause. The paper presents SLGAD resolution that computes the (con-
ditional) probability of a ground query from an LPAD and is based on
SLG resolution for normal logic programs. The performances of SLGAD
are evaluated on classical benchmarks for normal logic programs under
the well founded semantics, namely the stalemate game and the ancestor
relation. The results show that SLGAD has good scaling properties and
is able to deal with cyclic programs.},
  keywords = {Probabilistic Logic Programming, Well Founded Semantics, Logic
Programs with Annotated Disjunctions, SLG resolution}
  author = {Fabrizio Riguzzi and Nicola {Di Mauro}},
  title = {Application of the Information Bottleneck to {LPAD} Learning},
  year = {2010},
  institution = {Dipartimento di Ingegneria, Universit\`a di Ferrara, Italy},
  number = {CS-2010-01},
  url = {}
  author = {Elena Bellodi and Fabrizio Riguzzi},
  title = { {EM} over Binary Decision Diagrams for Probabilistic Logic Programs},
  year = {2011},
  institution = {Dipartimento di Ingegneria, Universit\`a di Ferrara, Italy},
  number = {CS-2011-01},
  url = {}
  author = {Fabrizio Riguzzi},
  title = {Introduzione all'intelligenza artificiale},
  journal = {CoRR},
  volume = {abs/1511.04352},
  year = {2015},
  url = {},
  note = {Published as Fabrizio Riguzzi, Introduzione all'Intelligenza Artificiale, Terre di Confine, 2(1), January 2006, License CC-BY},
  abstract = {
  The paper presents an introduction to Artificial Intelligence (AI) in an accessible and informal but precise form. The paper focuses on the algorithmic aspects of the discipline, presenting the main techniques used in AI systems groped in symbolic and subsymbolic. The last part of the paper is devoted to the discussion ongoing among experts in the field and the public at large about on the advantages and disadvantages of AI and in particular on the possible dangers. The personal opinion of the author on this subject concludes the paper.
L'articolo presenta un'introduzione all'Intelligenza Artificiale (IA) in forma divulgativa e informale ma precisa. L'articolo affronta prevalentemente gli aspetti informatici della disciplina, presentando le principali tecniche usate nei sistemi di IA divise in simboliche e subsimboliche. L'ultima parte dell'articolo presenta il dibattito in corso tra gli esperi e il pubblico su vantaggi e svantaggi dell'IA e in particolare sui possibili pericoli. L'articolo termina con l'opinione dell'autore al riguardo.},
  keywords = {Intelligenza Artificiale, Artificial Intelligence},
  copyright = {CC-BY}
  title = {Quantum Weighted Model Counting},
  author = {Fabrizio Riguzzi},
  year = {2019},
  journal = {arXiv},
  volume = {abs/1910.13530},
  url = {},
  primaryclass = {quant-ph}

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