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biblio/00-reading/2008 | ANNOTATED | Cognitive Technologies | Logical and relational learning with 10 tables | Luc De Raedt | Springer.pdf
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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
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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
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biblio/asp/Answer Set Programming in a Nutshell | Thomas Eiter | 2008.pdf
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biblio/asp/Complexity results for probabilistic answer set programming | Maua D, Cozman F | 2020
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biblio/asp/Conflict-driven answer set solving: From theory to practice.pdf
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biblio/asp/Experiencing Answer Set Programming at Work - Today and Tomorrow | Torsten Schaub.pdf
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biblio/asp/FastLAS_Scalable_Inductive_Logic_Programming_ETC | 2020.pdf
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biblio/asp/Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Torsten Schaub - Answer Set Solving in Practice-Morgan & Claypool (2013).pdf
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biblio/asp/The ILASP System for Inductive Learning of Answer Set Programs | 2020.pdf
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biblio/ilp/Cropper-Morel2021_Article_LearningProgramsByLearningFrom.pdf
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biblio/ilp/Cropper-Muggleton2019_Article_LearningEfficientLogicPrograms.pdf
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biblio/ilp/Mark Low | Conflict-driven Inductive Logic Programming | 2021.pdf
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biblio/ilp/Shan-Hwei Nienhuys-Cheng, Roland de Wolf | Foundations of Inductive Logic Programming | 1997.pdf
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biblio/inbox/%5B2021-07-14%5D%20Jason%20Dellaluce%20-%20Master%20Thesis.pdf
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biblio/inbox/2014 | Probabilistic Inductive Logic Programming Based on Answer Set Programming.pdf
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biblio/inbox/2015 | A Probabilistic Extension of the Stable Model Semantics
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biblio/inbox/2018 | Weight Learning in a Probabilistic Extension of Answer Set Programs.pdf
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biblio/inbox/2020 | Thirty years of credal networks: Specification, algorithms and complexity.pdf
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biblio/inbox/2022 | Fast Error Propagation Probability Estimates by Answer Set Programming and Approximate Model Counting.pdf
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biblio/inbox/2022 | Graph Neural Networks - Foundations, Frontiers and Applications.pdf
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biblio/inbox/2022 | plingo - A system for probabilistic reasoning in clingo.pdf
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biblio/kanren/2009 | Thesis | Relational Programming in MiniKanren | William Byrd.pdf
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biblio/kanren/2013 | muKanren - A Minimal Functional Core | Hemann.pdf
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biblio/kanren/2018 | The Reasoned Schemer, second edition | Daniel P. Friedman, William E. Byrd, Oleg Kiselyov, Jason Hemann.pdf
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biblio/rml/(Cognitive Technologies) Luc De Raedt - Logical and relational learning with 10 tables-Springer (2008).pdf
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biblio/rml/2022 | Abduction with probabilistic logic programming under the distribution semantics | Riguzzi F.pdf
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biblio/rml/Fabrizio Riguzzi - Foundations of Probabilistic Logic Programming. Languages, Semantics, Inference and Learning-River (2018).pdf
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biblio/rml/Optimizing Probabilities in Probabilistic Logic Programs | 2021.pdf
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biblio/rml/Sato, T | Statistical Learning Method for Logic Programs with Distribution | 1995.pdf
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biblio/sota/A Neuro-Symbolic ASP Pipeline for Visual Question Answering.pdf
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biblio/sota/Approaches and Applications of Inductive Programming | 2019.pdf
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biblio/sota/Efficient Knowledge Compilation Beyond Weighted Model Counting.pdf
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biblio/sota/Interpretable Machine Learning – A BriefHistory, State-of-the-Art and Challenges | 2020.pdf
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biblio/sota/Learning MAX-SAT Models from Examples using Genetic Algorithms and Knowledge Compilation.pdf
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biblio/sota/Learning to Synthesize Programs as Interpretable and Generalizable Policies | 2021.pdf
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biblio/sota/Limitations of Interpretable Machine Learning Methods | 2019.pdf
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biblio/sota/Limitations of Interpretable Machine Learning Methods.epub
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biblio/sota/Prolog and Answer Set Programming_Languages in Logic Programming | 2020pdf
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biblio/sota/Sum-Product Loop Programming - From Probabilistic Circuits to Loop Programming.pdf
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biblio/sota/Turning 30: New Ideas in Inductive Logic Programming | 2020.pdf
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biblio/tops/Chiaki Sakama | Induction from answer sets in nonmonotonic logic programs | ACM-TOCL | 2005.pdf
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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
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biblio/tops/Selected Papers of the International Joint Conference on Automated Reasoning (IJCAR 2016).pdf
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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
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... | ... | @@ -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 |
... | ... |
... | ... | @@ -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 |
... | ... |
... | ... | @@ -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 |
... | ... |
... | ... | @@ -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 |
... | ... |
... | ... | @@ -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 |
... | ... |