Commit 808facfef786d435f3e346bf8240ebb1eabf3a52

Authored by Francisco Coelho
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README.pdf 0 → 100644
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biblio/00-reading/2008 | ANNOTATED | Cognitive Technologies | Logical and relational learning with 10 tables | Luc De Raedt | Springer.pdf 0 → 100644
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biblio/00-reading/2022 | plingo 2206.11515.pdf 0 → 100644
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biblio/00-reading/21245-Article Text-25258-1-2-20220628.pdf 0 → 100644
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biblio/00-reading/3534678.3542609.pdf 0 → 100644
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biblio/00-reading/AIC-2022-camera-ready-full-paper-19.pdf 0 → 100644
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biblio/00-reading/ANNOTATED | The joy of Probabilistic Answer Set Programming: Semantics - complexity, expressivity, inference | Fabio Gagliardi Cozman, Denis Deratani Mauá | 2020.pdf 0 → 100644
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biblio/00-reading/algorithms-15-00201-v3.pdf 0 → 100644
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biblio/Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples by Masís, Serg (z-lib.org).pdf 0 → 100644
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biblio/Meta-Level Abduction.pdf 0 → 100644
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biblio/asp/ASP-CORE-2.01c.pdf 0 → 100644
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biblio/asp/Answer Set Programming in a Nutshell | Thomas Eiter | 2008.pdf 0 → 100644
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biblio/asp/Answer set solving in practice.pdf 0 → 100644
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biblio/asp/Complexity results for probabilistic answer set programming | Maua D, Cozman F | 2020 0 → 100644
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biblio/asp/Conflict-driven answer set solving: From theory to practice.pdf 0 → 100644
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biblio/asp/Experiencing Answer Set Programming at Work - Today and Tomorrow | Torsten Schaub.pdf 0 → 100644
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biblio/asp/FastLAS_Scalable_Inductive_Logic_Programming_ETC | 2020.pdf 0 → 100644
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biblio/asp/Inductive learning of answer set programs.pdf 0 → 100644
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biblio/asp/Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Torsten Schaub - Answer Set Solving in Practice-Morgan & Claypool (2013).pdf 0 → 100644
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biblio/asp/ON THE FOUNDATIONS OF GROUNDING IN ASP | 2021.pdf 0 → 100644
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biblio/asp/The ILASP System for Inductive Learning of Answer Set Programs | 2020.pdf 0 → 100644
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biblio/asp/potassco_guide.pdf 0 → 100644
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biblio/dagrep-v009-i005-complete.pdf 0 → 100644
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biblio/dagrep_v010_i002_p001_20061.pdf 0 → 100644
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biblio/dagrep_v011_i004_p020_21192.pdf 0 → 100644
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biblio/dagrep_v011_i008_p001_21361.pdf 0 → 100644
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biblio/ilp/2008_Book_ProbabilisticInductiveLogicPro.pdf 0 → 100644
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biblio/ilp/Cropper-Morel2021_Article_LearningProgramsByLearningFrom.pdf 0 → 100644
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biblio/ilp/Cropper-Muggleton2019_Article_LearningEfficientLogicPrograms.pdf 0 → 100644
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biblio/ilp/Inductive Logic Programming: Theory and methods | 1994.pdf 0 → 100644
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biblio/ilp/Inductive logic programming at 30 | 2021.pdf 0 → 100644
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biblio/ilp/Mark Low | Conflict-driven Inductive Logic Programming | 2021.pdf 0 → 100644
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biblio/ilp/Probabilistic Inductive Logic Programming (2008).pdf 0 → 100644
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biblio/ilp/Shan-Hwei Nienhuys-Cheng, Roland de Wolf | Foundations of Inductive Logic Programming | 1997.pdf 0 → 100644
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biblio/inbox/%5B2021-07-14%5D%20Jason%20Dellaluce%20-%20Master%20Thesis.pdf 0 → 100644
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biblio/inbox/00 | 2022 | 21245-Article Text-25258-1-2-20220628.pdf 0 → 100644
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biblio/inbox/2013 | Probabilistic Answer Set Programming.pdf 0 → 100644
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biblio/inbox/2014 | Probabilistic Inductive Logic Programming Based on Answer Set Programming.pdf 0 → 100644
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biblio/inbox/2015 | A Probabilistic Extension of the Stable Model Semantics 0 → 100644
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biblio/inbox/2018 | Weight Learning in a Probabilistic Extension of Answer Set Programs.pdf 0 → 100644
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biblio/inbox/2020 | Thirty years of credal networks: Specification, algorithms and complexity.pdf 0 → 100644
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biblio/inbox/2022 | 2206.11515.pdf 0 → 100644
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biblio/inbox/2022 | Fast Error Propagation Probability Estimates by Answer Set Programming and Approximate Model Counting.pdf 0 → 100644
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biblio/inbox/2022 | Graph Neural Networks - Foundations, Frontiers and Applications.pdf 0 → 100644
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biblio/inbox/2022 | plingo - A system for probabilistic reasoning in clingo.pdf 0 → 100644
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biblio/inbox/2206.00426.pdf 0 → 100644
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biblio/inbox/AIC-2022-camera-ready-full-paper-19.pdf 0 → 100644
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biblio/inbox/Semantics for Hybrid Probabilistic Logic Programs.pdf 0 → 100644
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biblio/inbox/ThesisArnaud.pdf 0 → 100644
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biblio/inbox/algorithms-15-00201-v3.pdf 0 → 100644
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biblio/inbox/paper1CAUSAL.pdf 0 → 100644
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biblio/inbox/short1PLP.pdf 0 → 100644
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biblio/kanren/2009 | Thesis | Relational Programming in MiniKanren | William Byrd.pdf 0 → 100644
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biblio/kanren/2013 | muKanren - A Minimal Functional Core | Hemann.pdf 0 → 100644
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biblio/kanren/2018 | The Reasoned Schemer, second edition | Daniel P. Friedman, William E. Byrd, Oleg Kiselyov, Jason Hemann.pdf 0 → 100644
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biblio/rml/(Cognitive Technologies) Luc De Raedt - Logical and relational learning with 10 tables-Springer (2008).pdf 0 → 100644
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biblio/rml/2022 | Abduction with probabilistic logic programming under the distribution semantics | Riguzzi F.pdf 0 → 100644
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biblio/rml/Deep Learning A Critical Appraisal | 2018.pdf 0 → 100644
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biblio/rml/Fabrizio Riguzzi - Foundations of Probabilistic Logic Programming. Languages, Semantics, Inference and Learning-River (2018).pdf 0 → 100644
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biblio/rml/Inductive logic programming at 30 | 2021.pdf 0 → 100644
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biblio/rml/Learning_exploratory_rules_from_noisy_data.pdf 0 → 100644
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biblio/rml/Optimizing Probabilities in Probabilistic Logic Programs | 2021.pdf 0 → 100644
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biblio/rml/Sato, T | Statistical Learning Method for Logic Programs with Distribution | 1995.pdf 0 → 100644
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biblio/rml/Statistical Relational Learning, De Raedt et al, 2010.pdf 0 → 100644
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biblio/sota/A Neuro-Symbolic ASP Pipeline for Visual Question Answering.pdf 0 → 100644
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biblio/sota/Approaches and Applications of Inductive Programming | 2019.pdf 0 → 100644
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biblio/sota/Efficient Knowledge Compilation Beyond Weighted Model Counting.pdf 0 → 100644
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biblio/sota/Interpretable Machine Learning – A BriefHistory, State-of-the-Art and Challenges | 2020.pdf 0 → 100644
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biblio/sota/Learning MAX-SAT Models from Examples using Genetic Algorithms and Knowledge Compilation.pdf 0 → 100644
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biblio/sota/Learning to Synthesize Programs as Interpretable and Generalizable Policies | 2021.pdf 0 → 100644
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biblio/sota/Limitations of Interpretable Machine Learning Methods | 2019.pdf 0 → 100644
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biblio/sota/Limitations of Interpretable Machine Learning Methods.epub 0 → 100644
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biblio/sota/Prolog and Answer Set Programming_Languages in Logic Programming | 2020pdf 0 → 100644
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biblio/sota/Sum-Product Loop Programming - From Probabilistic Circuits to Loop Programming.pdf 0 → 100644
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biblio/sota/Turning 30: New Ideas in Inductive Logic Programming | 2020.pdf 0 → 100644
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biblio/tops/Chiaki Sakama | Induction from answer sets in nonmonotonic logic programs | ACM-TOCL | 2005.pdf 0 → 100644
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biblio/tops/S. Tomović, Z. Ognjanović, D. Doder | A First-order Logic for Reasoning about Knowledge and Probability | ACM-TOCL | 2020.pdf 0 → 100644
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biblio/tops/Selected Papers of the International Joint Conference on Automated Reasoning (IJCAR 2016).pdf 0 → 100644
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biblio/tops/The joy of Probabilistic Answer Set Programming: Semantics - complexity, expressivity, inference | Fabio Gagliardi Cozman, Denis Deratani Mauá | 2020.pdf 0 → 100644
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code/asp/alarm.lp 0 → 100644
... ... @@ -0,0 +1,5 @@
  1 +burglary;-burglary. % 0.1::
  2 +earthquake;-earthquake. % 0.01::
  3 +
  4 +alarm:- burglary.
  5 +alarm;-alarm :- earthquake. % 0.8::
0 6 \ No newline at end of file
... ...
code/asp/assp_pag43.lp 0 → 100644
... ... @@ -0,0 +1,2 @@
  1 +q.
  2 +p :- q, not r.
0 3 \ No newline at end of file
... ...
code/asp/assp_pag46.lp 0 → 100644
... ... @@ -0,0 +1 @@
  1 +p :- -p.
0 2 \ No newline at end of file
... ...
code/asp/bachelor.lp 0 → 100644
... ... @@ -0,0 +1,4 @@
  1 +person(joey).
  2 +%married(X); -married(X) :- person(X).
  3 +male(X); female(X) :- person(X).
  4 +bachelor(X) :- male(X), not married(X).
0 5 \ No newline at end of file
... ...
code/asp/bachelor.pl 0 → 100644
... ... @@ -0,0 +1,6 @@
  1 +person(joey).
  2 +% male(X); female(X) :- person(X).
  3 +male(X) :- person(X).
  4 +female(X) :- person(X).
  5 +
  6 +bachelor(X) :- male(X), \+ married(X).
0 7 \ No newline at end of file
... ...
code/asp/choices_01.lp 0 → 100644
... ... @@ -0,0 +1,11 @@
  1 +{ a ; b }.
  2 +
  3 +% Answer: 1
  4 +%
  5 +% Answer: 2
  6 +% b
  7 +% Answer: 3
  8 +% a
  9 +% Answer: 4
  10 +% a b
  11 +% SATISFIABLE
0 12 \ No newline at end of file
... ...
code/asp/choices_02.lp 0 → 100644
... ... @@ -0,0 +1,9 @@
  1 +a ; b.
  2 +
  3 +% Answer: 1
  4 +% b
  5 +% Answer: 2
  6 +% a
  7 +% SATISFIABLE
  8 +%
  9 +% In Answer 2 we (also) have not b.
0 10 \ No newline at end of file
... ...
code/asp/choices_03.lp 0 → 100644
... ... @@ -0,0 +1,10 @@
  1 +{ a ; b }.
  2 +:- a, not b.
  3 +
  4 +% Answer: 1
  5 +%
  6 +% Answer: 2
  7 +% b
  8 +% Answer: 3
  9 +% a b
  10 +% SATISFIABLE
0 11 \ No newline at end of file
... ...
code/asp/choices_04.lp 0 → 100644
... ... @@ -0,0 +1,5 @@
  1 +a ; b.
  2 +:- a, not b.
  3 +% Answer: 1
  4 +% b
  5 +% SATISFIABLE
0 6 \ No newline at end of file
... ...
code/asp/choices_05.lp 0 → 100644
... ... @@ -0,0 +1,8 @@
  1 +{ a ; -a }.
  2 +:- a, not -a.
  3 +
  4 +% Answer: 1
  5 +%
  6 +% Answer: 2
  7 +% -a
  8 +% SATISFIABLE
... ...
code/asp/choices_06.lp 0 → 100644
... ... @@ -0,0 +1,5 @@
  1 +a ; -a.
  2 +:- a, not -a.
  3 +% Answer: 1
  4 +% -a
  5 +% SATISFIABLE
0 6 \ No newline at end of file
... ...
code/asp/color.lp 0 → 100644
... ... @@ -0,0 +1,15 @@
  1 +node(1..6).
  2 +
  3 +edge(1,2). edge(2,4). edge(3,1).
  4 +edge(4,1). edge(5,3). edge(6,2).
  5 +edge(1,3). edge(2,5). edge(3,4).
  6 +edge(4,2). edge(5,4). edge(6,3).
  7 +edge(1,4). edge(2,6). edge(3,5).
  8 +edge(5,6). edge(6,5).
  9 +
  10 +col(r). col(b). col(g).
  11 +
  12 +1 { color(X,C) : col(C)} 1 :- node(X).
  13 +:- edge(X,Y), color(X,C), color(Y,C).
  14 +
  15 +#show color/2.
0 16 \ No newline at end of file
... ...
code/asp/credal_01.lp 0 → 100644
... ... @@ -0,0 +1,4 @@
  1 +a ; -a.
  2 +
  3 +s :- not w, not a.
  4 +w :- not s.
0 5 \ No newline at end of file
... ...
code/asp/cyclic.lp 0 → 100644
... ... @@ -0,0 +1,9 @@
  1 +% p :- not p. % UNSAT
  2 +% not p :- p. % SAT: {}
  3 +% p :- -p. % SAT: {}
  4 +% -p :- p. % SAT: {}
  5 +% not p :- -p. % SAT: {}
  6 +% -p :- not p. % SAT: -p
  7 +a :- b.
  8 +b :- a.
  9 +a :- not a.
0 10 \ No newline at end of file
... ...
code/asp/cyclic.pl 0 → 100644
... ... @@ -0,0 +1,14 @@
  1 +a :- b.
  2 +a :- \+ a.
  3 +b :- a.
  4 +% > swipl cyclic.pl
  5 +% ERROR: /home/fc/sci/projetos/plp/code/asp/cyclic.pl:2:
  6 +% ERROR: Stack limit (1.0Gb) exceeded
  7 +% ERROR: Stack sizes: local: 1.0Gb, global: 27Kb, trail: 1Kb
  8 +% ERROR: Stack depth: 7,455,777, last-call: 0%, Choice points: 7,455,758
  9 +% ERROR: Probable infinite recursion (cycle):
  10 +% ERROR: [7,455,777] user:p
  11 +% ERROR: [7,455,776] user:p
  12 +% Warning: /home/fc/sci/projetos/plp/code/asp/cyclic.pl:2:
  13 +% Warning: Goal (directive) failed: user:p
  14 +%
0 15 \ No newline at end of file
... ...
code/asp/double_negation.lp 0 → 100644
... ... @@ -0,0 +1,42 @@
  1 +%not not a.
  2 +%% UNSATISFIABLE
  3 +%%% ie no models.
  4 +
  5 +% a.
  6 +%% Answer: 1
  7 +%% a
  8 +%% SATISFIABLE
  9 +%%% ie there is (only) one (stable) model: {a}
  10 +
  11 +% -a.
  12 +%% Answer: 1
  13 +%% -a
  14 +%% SATISFIABLE
  15 +
  16 +% --a.
  17 +%% *** ERROR: (clingo): parsing failed
  18 +%%% WTF?
  19 +
  20 +% not a.
  21 +%% Answer: 1
  22 +%%
  23 +%% SATISFIABLE
  24 +%%% ie there is (only) one (stable) model: {}
  25 +%%%
  26 +%%% this program states that there is no information. In particular, there is no information about a.
  27 +%%% Therefore there are no provable atoms. Hence the empty set is a stable model.
  28 +
  29 +% not -a.
  30 +%% Answer: 1
  31 +%%
  32 +%% SATISFIABLE
  33 +
  34 +% b.
  35 +% a;c.
  36 +% not a :- b.
  37 +%% Answer: 1
  38 +%% b -a
  39 +%% SATISFIABLE
  40 +
  41 +a.
  42 +b :- not a.
0 43 \ No newline at end of file
... ...
code/asp/ex03.lp 0 → 100644
... ... @@ -0,0 +1,3 @@
  1 +p(a) :- not p(b).
  2 +p(b) :- not p(a).
  3 +:- p(b).
0 4 \ No newline at end of file
... ...
code/asp/gcE01.lp 0 → 100644
... ... @@ -0,0 +1,9 @@
  1 +col(r ; g ; b).
  2 +
  3 +% var C is local in this rule.
  4 +% More specificlly, it is bound to the (lhs) cardinality contraint.
  5 +% Also, it varies over all instantiations of col(C).
  6 +1 { color(X, C) : col(C) } 1 :- node(X).
  7 +:- edge(X, Y), color(X, C), color(Y, C).
  8 +
  9 +#show color/2.
0 10 \ No newline at end of file
... ...
code/asp/gcI01.lp 0 → 100644
... ... @@ -0,0 +1,8 @@
  1 +node(1 .. 6).
  2 +
  3 +edge(1 ,2). edge(2 ,4). edge(3 ,1).
  4 +edge(4 ,1). edge(5 ,3). edge(6 ,2).
  5 +edge(1 ,3). edge(2 ,5). edge(3 ,4).
  6 +edge(4 ,2). edge(5 ,4).
  7 +edge(6 ,3). edge(1 ,4). edge(2 ,6).
  8 +edge(3 ,5). edge(5 ,6). edge(6 ,5).
0 9 \ No newline at end of file
... ...
code/asp/no_model_cyclic.lp 0 → 100644
... ... @@ -0,0 +1 @@
  1 +a :- not a.
0 2 \ No newline at end of file
... ...