restructure folders

Francisco Coelho
1 parent 3f5ec5fc
Showing 148 changed files with 738 additions and 21378 deletions Show diff stats
.vscode/launch.json
README.md
README.pdf
SCASP-best-practices.pdf
biblio
biblio.gdlink
biblio/00-reading/2008 | ANNOTATED | Cognitive Technologies | Logical and relational learning with 10 tables | Luc De Raedt | Springer.pdf
biblio/00-reading/2022 | plingo 2206.11515.pdf
biblio/00-reading/3534678.3542609.pdf
biblio/00-reading/AIC-2022-camera-ready-full-paper-19.pdf
biblio/00-reading/ANNOTATED | The joy of Probabilistic Answer Set Programming: Semantics - complexity, expressivity, inference | Fabio Gagliardi Cozman, Denis Deratani Mauá | 2020.pdf
biblio/00-reading/Inference and Learning with Model Uncertainty in Probabilistic Logic Programs.pdf
biblio/00-reading/algorithms-15-00201-v3.pdf
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
biblio/Meta-Level Abduction.pdf
biblio/asp/ASP-CORE-2.01c.pdf
biblio/asp/ASP-CORE-2.03b.pdf
biblio/asp/Answer Set Programming in a Nutshell | Thomas Eiter | 2008.pdf
biblio/asp/Answer set programming and plan generation.pdf
biblio/asp/Answer set solving in practice.pdf
@@ -0,0 +1,16 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": true
+        }
+    ]
+}
 \ No newline at end of file
@@ -1,67 +0,0 @@
-# Probabilistic ILP
-
-**Check** Conformal prediction.
-
-> Fonte: [Turning 30: New Ideas in Inductive Logic Programming](https://arxiv.org/abs/2002.11002)
-
-## Introduction
-
-- How pILP relates to:
-  - ILP?
-  - ASP?
-  - RML?
-- What 
-  - tools?
-  - methods?
-  - theory?
-    - Distributed semantics
-  - applications?
-
-### Overview of Bibliography and State of the Art
-
-Recursion; Predicate Invention; Higher order, ASP Hypotheses; Optimality; Prolog, ASP, NNs 
-
-## Context
-
-### Kanren
-
-### Inductive Logic Programming
-
-### Answer Set Programming
-
-### Relational Machine Learning
-
-### SAT Solvers
-
-## Tools
-
-- [(mini)kanren](http://minikanren.org/)
-  - in Julia: [MuKanren](https://github.com/latticetower/MuKanren.jl), [YA microkanren in Julia](https://www.philipzucker.com/yet-another-microkanren-in-julia/)!.
-- [metagol | archive](https://github.com/metagol/metagol) _superseeded by **popper**._
-- ILP: [popper](https://github.com/logic-and-learning-lab/Popper)
-- ASP: [ILASP](https://github.com/ilaspltd/ILASP-releases)
-- [Inspire | Kazmi et al. 2017]()
-- ASP: [Potassco: clingo, clasp, ...](https://potassco.org/)
-- [cplint (on SWISH)](http://cplint.ml.unife.it/)
-  - exact probabilistic inference (PITA)
-    - Fabrizio Riguzzi and Terrance Swift. Well-definedness and efficient inference for probabilistic logic programming under the distribution semantics. Theory and Practice of Logic Programming, 13(Special Issue 02 - 25th Annual GULP Conference):279-302, © Cambridge University Press, March 2013.
-  - Monte Carlo inference (MCINTYRE) 
-    - Fabrizio Riguzzi. MCINTYRE: A Monte Carlo system for probabilistic logic programming. Fundamenta Informaticae, 124(4):521-541, © IOS Press, 2013.
-  - Metropolis/Hastings sampling
-    - Arun Nampally and C. R. Ramakrishnan. Adaptive MCMC-Based Inference in Probabilistic Logic Programs. arXiv preprint arXiv:1403.6036, 2014.
-  - parameter learning (EMBLEM)
-    - Elena Bellodi and Fabrizio Riguzzi. Expectation Maximization over binary decision diagrams for probabilistic logic programs. Intelligent Data Analysis, 17(2):343-363, © IOS Press, 2013.
-  - SLIPCOVER algorithm for structure learning
-    - Elena Bellodi and Fabrizio Riguzzi. Structure learning of probabilistic logic programs by searching the clause space. Theory and Practice of Logic Programming, 15(2):169-212, © Cambridge University Press, 2015.
-  - LEMUR algorithm for structure learning
-    - Nicola Di Mauro, Elena Bellodi, and Fabrizio Riguzzi. Bandit-based Monte-Carlo structure learning of probabilistic logic programs. Machine Learning, 100(1):127-156, © Springer International Publishing, July 2015.
-
-## Methods
-
-## Theory
-
-### Distributed Semantics
-
-## Applications
-
-### ELearning
@@ -0,0 +1 @@
+/home/fc/Insync/mangon@gmail.com/Google Drive - Shared with me/biblio
 \ No newline at end of file
@@ -0,0 +1 @@
+{"url": "https://drive.google.com/file/d/19ZBN9zT4YyLOEeMNvpA9Baqw9KKWd-dx/view?usp=drivesdk", "file_id": "19ZBN9zT4YyLOEeMNvpA9Baqw9KKWd-dx", "account_email": "mangon@gmail.com"}
 \ No newline at end of file
 from itertools import combinations, product
  
 def fmt(expr):
+    """Doc string"""
     return ",".join(f"{x:>2}" for x in expr)
  
 def c(expr):
+    """Doc string"""
     def litcomp(x):
         if x == "⊤":
             return "⊥"
@@ -16,6 +18,7 @@ def c(expr):
     return [litcomp(x) for x in expr]
  
 def domain(symbols, unary="¬"):
+    """Doc string"""
     atoms = list(symbols)
     literals = [
         [f"{u}{a}" for u in unary] +
@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-6, 6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = np.sin(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f2d55566b50>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "a59afa236e16843183c59a167f072b6fa0409044b3c4938e82ac98aad91bf217"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
@@ -1,27 +0,0 @@
-## Zugzwang Meetings
-
-### 2023-01-10 | 15:00
-
-- Paper
-- Project
-- Latent Facts
-
-### 2022-12-12
-
-- Is the project proposal ok? How long/detailed should it be?
-- Initial exploratory code `event_lattice.py` and `EventLattice.ipynb` done.
-- Start writing paper: Introduction, state of the art, motivation
-  - Identify key problems
-  - Target Conferences
-    - KR;
-    - [ICLP](https://waset.org/language-planning-conference-in-april-2023-in-lisbon); 
-    - [ECAI](https://ecai2023.eu/)
-- Next task for prototype: 
-  - Get stable models from potassco/s(casp)
-  - other?
-
-
-### 2022-12-05
-
-- Created shared folder (gdrive:zugzwang) <https://drive.google.com/drive/folders/1xs-cjxWJzn2JxqeNgh9LX5xWN50BW-Be?usp=share_link>
-- Refine project tasks, for Bachelor, M.Sc., Ph.D. students and for researchers. 
 \ No newline at end of file
@@ -0,0 +1 @@
+/home/fc/Insync/mangon@gmail.com/Google Drive - Shared with me/projecto-NovaLINCS
 \ No newline at end of file
@@ -0,0 +1 @@
+{"url": "https://drive.google.com/file/d/1liS-0PGFXZVVPtfiRZix2JXjXzshbNKw/view?usp=drivesdk", "file_id": "1liS-0PGFXZVVPtfiRZix2JXjXzshbNKw", "account_email": "mangon@gmail.com"}
 \ No newline at end of file
@@ -1,53 +0,0 @@
-# Zugzwang | Logic and Artificial Intelligence
-
-## Team
-
-- Francisco Coelho
-- Salvador Abreu (PI)
-- Bruno Dinis (External Collaborator)
-
-## Research Questions
-
-How to extend probability annotations on an ASP program to a distribution over the possible observations? In general, the association of probabilities to some facts is not enough to uniquely define a probability of stable models. This lack of information must be carefully expressed and handled, to avoid biased results.
-
-Once made explicit, how to use such distribution, together with an empiric distribution from a dataset, to do general probabilistic tasks such as the estimation of a marginal or of the joint probability? Having a probability on a set of observations, including the stable models, might not cover all the sample space. Again, information and structure entailed from the ASP program must guide unbiased extensions to the sample space.
-
-How to use distribution measures to score ASP programs? We view ASP programs as formalized knowledge about an observable system. Some of those programs will be "better" than others, as determined by a quantitative, objective, measure, rooted on well-known functions such as the Kullback–Leibler divergence.
-
-This last question leads to the application of evolutionary algorithms to inductive logic programming and to a bridge with common machine learning methods, where a model is scored by a dataset. Other important questions lie behind the scope of this project. For example, how to deal with latent, unobserved, variables? 
-
-## Novelty/Impact
-
-ASP has some key advantages over Prolog, most of which result from ASP being a truly declarative language and modern APS systems, such as CLASP, apply efficient optimisation techniques. Other systems, like ILASP, learn ASP programs with normal rules, choice rules, and hard and weak constraints. Moreover, ASP can use recent important advances concerning SAT solvers to ILP tasks. However, the assumption that the knowledge base (BK) is perfect, leaving no room for uncertainty, poses here a major limitation. 
-
-One approach to overcome this restriction on logic programs is Statistical Relationship Learning (StarAI), that extends the BK with probabilities in order to setup a distribution representation. The formal setting for this approach rests on Sato's Distribution Semantics and frames systems such as Problog and PRISM. But these systems are oriented towards Prolog-like programs and semantic and leave out ASP program learning.
-
-One important ongoing research question is the precise semantic of an ASP program annotated with probabilities. Sato's semantic specify an unique probability distribution over Prolog-like program's atoms, but this uniqueness fails for ASP programs. Efforts to address this problem are either based on Credal sets or on selecting one probability over others, such as the P-log, the LP^MLN or the PrASP languages.
-
-The novelty of this research is to define the semantic of an ASP program + probability annotated facts + observations from the following process: (a) parametrise the uncertainty on stable models and annotations, (b) setup a partition of the sample space around on the stable models and (c) use observations to estimate the value of the uncertainty parameters of step (a).
-If successful, it would have an impact on how logic programs express and are used to deal with real-world problems, where both uncertainty and formal KB are required ingredients. Also, when used in ILP problems the resulting models are logic programs, much simpler to understand than numeric models. 
-
-## Expected Results / Demonstrators
-
-Our aim is to develop a set of software tools to apply and evaluate the theoretical results on well-known, and relevant, problems. The initial target framework is the Potassco suite, that provides a Python API to state-of-the-art grounding (CLASP) and solving (CLINGO) tools, as well as ASP-Core-2 Language support.
-
-The researchers will do the theoretical study and supervise one to three undergraduate, master or Ph.D. students on the implementation tasks. Theoretical work includes formalization and assessment of methods; The students will implement the tools required to experiment, explore and test those methods. The degree of complexity of the implementation tasks must be adapted to the individual competences and interests of the students.
-
-The theoretical work is to be reported in one or two papers with intermediate results and a final comprehensive paper for a conference.
-
-## Relationship of activity w/ NOVA LINCS strategic program and Research Group work plan
-
-This project involves two members from the NOVA LINCS "Knowledge-Based Systems" research group, from the Universidade de Évora pole and a member of the CIMA center from the Universidade de Évora.
-
-Part of the proposed research and software is currently being developed.
-
-## Resources required and justification
-
-Students are expected to benefit from a BIC or a BI scholarship. Depending on the candidates the respective durations can range from 3 to 12 months and the respective amounts from 486.12€ to 1144.64€ per month.
-
-Considering one undergraduate student with a 3 month scholarship:
-
-- 972.24 € = 3 months x 486.12 €/month;
-- TOTAL: 972.24 €
-
-
@@ -1,54 +0,0 @@
-# Concurso Bolsa | TEMPLATE
-
-- _centro de investigação ou departamento_
-
-- _identificação da tipologia de bolsa oferecida e número de vagas_
-
-- _dia_ de _mês_ de 2023
-
-Encontra-se aberto concurso para a atribuição de _número_ Bolsas _tipo_ no âmbito do projeto _título do projeto_, _referência_, financiado por fundos nacionais através da _…_ exemplo: FCT/MCTES e cofinanciado pelo Fundo Europeu de Desenvolvimento Regional _FEDER_ através do COMPETE – Programa Operacional Fatores de Competitividade _POFC_ _quando aplicável_, nas seguintes condições:
-
-- **Área Científica:** …
-
-- **Requisitos de admissão:** _indicar a habilitação académica necessária, bem como a experiência exigida em investigação e outros requisitos de admissão, incluindo fatores preferenciais_
-Escolher uma opção: 
-
-  - Conforme o Regulamento de Bolsas de Investigação da FCT nº950/2019 de 16 de dezembro de 2019, artigo 3º e 6º, os candidatos a BI _Bolsas de Investigação_ devem cumprir como condição para a atribuição da bolsa, a inserção efetiva em ciclos de estudos conducentes à atribuição de graus académicos ou em cursos não conferentes de grau académico. Os cursos não conferentes de grau académico correspondem aos cursos previstos na alínea e_ do nº 3 do artigo 4º do Decreto-Lei nº74/2006 de 24 de março e deverão ser desenvolvidos numa instituição de ensino superior em associação a pelo menos uma unidade de I&D, incluindo-se o plano do curso numa ou em várias áreas de investigação da unidade.
-  - Conforme o Regulamento de Bolsas de Investigação da FCT nº950/2019 de 16 de dezembro de 2019, artigo 5º, — As BII _Bolsas de Iniciação à Investigação_ não podem ser atribuídas a quem já tenha beneficiado de bolsas de investigação direta ou indiretamente financiadas pela FCT, atribuídas nos termos do Estatuto do Bolseiro de Investigação.
-  - Relativamente às BIPD _Bolsas de Investigação Pós-doutoral_, ao nível dos requisitos a verificar para atribuição das mesmas _artigo 7º do Regulamento de Bolsas de Investigação da FCT nº950/2019_ destaca-se a necessidade do grau de doutor ter sido obtido nos 3 anos anteriores à data de submissão da candidatura à bolsa e, ao nível da sua execução, o facto de apenas poder ser renovada até ao prazo máximo de 3 anos.
-
-- **Plano de trabalhos:** _apresentar um resumo dos trabalhos a desenvolver e dos objetivos a atingir_
-
-- **Legislação e regulamentação aplicável:** A concessão da Bolsa de Investigação será realizada mediante a celebração de um contrato entre a Universidade de Évora e o bolseiro conforme minuta <https://www.fct.pt/apoios/Minuta_Contrato_Bolsa.docx> , nos termos do Estatuto do Bolseiro de Investigação _Lei nº40/2004 de 18 de agosto e decreto-lei nº 123/2019 de 28 de agosto_ e de acordo com a legislação e Regulamento de Bolsas de Investigação da Fundação para a Ciência e a Tecnologia, I.P em vigor, regulamento nº950/2019 de 16 de dezembro de 2019: <https://www.fct.pt/apoios/bolsas/regulamento.phtml.pt> e demais normas aplicáveis.
-
-- **Local de trabalho:** O trabalho será desenvolvido no(a) _denominação da unidade de investigação_ da Universidade de Évora, sob a orientação científica do Professor(a)/Doutor(a) ………
-
-- **Duração da(s) bolsa(s):** A bolsa terá a duração de ..... meses, com início previsto em ..... _mês_ de .......... _ano_. O contrato de bolsa poderá ser renovado até _ex._ ao máximo de ….. meses ou até ao final da dotação orçamental do projeto de financiamento _…_.
-
-- **Valor do subsídio de manutenção mensal:** O montante da bolsa corresponde a €………., conforme tabela de valores das bolsas atribuídas diretamente pela FCT, I.P. no País _http://fct.pt/apoios/bolsas/valores_, sendo os pagamentos efetuados mensalmente, através de cheque ou transferência bancária.
-
-- **Métodos de seleção:** Os métodos de seleção a utilizar serão os seguintes: _avaliação curricular, entrevista, provas de conhecimento, ou outros_, com a respetiva valoração de _indicar os valores atribuídos a cada critério ou item avaliado e sua ponderação percentual_.
-
-- **Composição do Júri de Seleção:** _identificação do Presidente do Júri e dos vogais efetivos e suplentes_
-
-- **Forma de publicitação/notificação dos resultados:** Os resultados finais da avaliação serão publicitados, através de lista ordenada _alfabeticamente, por nota final obtida ou outra_ afixada em local visível e público do(a) _indicar local da instituição_, sendo o candidato(a) aprovado(a) notificado através de _email, ofício ou outro_.
-Nos termos de direito de audiência prévia dos interessados o projeto de Classificação Final será anunciado por qualquer meio escrito a todos os interessados.
-Após comunicação da lista provisória dos resultados da avaliação, os candidatos dispõem de um período de 10 dias úteis para, querendo, se pronunciarem em sede de audiência prévia de interessados. 
-
-- **Prazo de candidatura e forma de apresentação das candidaturas:** O concurso encontra-se aberto no período de ......................... a ......................... de 2022 e os resultados da seleção serão publicados até………………..de………………….de 2022. _O prazo de apresentação de candidaturas não deve ser inferior a 10 dias úteis_.
-
-- **As candidaturas devem ser formalizadas**, obrigatoriamente, através do envio de carta de candidatura acompanhada dos seguintes documentos: _ex: Curriculum Vitae, certificado de habilitações, cartas de referência ou recomendação e outros documentos comprovativos considerados relevantes_.
-  - Para efeitos de candidatura os comprovativos podem ser substituídos por declaração de honra do candidato, mas a não demonstração, em fase de contratualização, da posse do grau exigido à data limite da candidatura ou a não apresentação dos comprovativos de matrícula ou inscrição em ciclo de estudos ou curso não conferente de grau, para as bolsas com essa componente, implicam a anulação da avaliação do candidato. 
-  - Os graus académicos obtidos em países estrangeiros necessitam de registo por uma Instituição Portuguesa de acordo com o Decreto-lei nº. 66/2018, de 16 de agosto e a Portaria nº. 33/2019, de 25 de janeiro. A apresentação do certificado é obrigatória para a assinatura do contrato. 
-  Mais informação poderá ser obtida em:  <https://www.dges.gov.pt/pt/pagina/reconhecimento?plid=374>
-
-
-- **As candidaturas deverão ser remetidas por e-mail para:**
-    - Prof. Doutor(a)………………….
-    - _Centro/Dep._……………………………….. da Universidade de Évora
-    - e-mail: 
-
-
-
-_logos do programa de financiamento quando aplicável_
-
...	...	@@ -0,0 +1,16 @@
	1	+{
	2	+ // Use IntelliSense to learn about possible attributes.
	3	+ // Hover to view descriptions of existing attributes.
	4	+ // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
	5	+ "version": "0.2.0",
	6	+ "configurations": [
	7	+ {
	8	+ "name": "Python: Current File",
	9	+ "type": "python",
	10	+ "request": "launch",
	11	+ "program": "${file}",
	12	+ "console": "integratedTerminal",
	13	+ "justMyCode": true
	14	+ }
	15	+ ]
	16	+}
0	17	\ No newline at end of file
...	...
...	...	@@ -1,67 +0,0 @@
1		-# Probabilistic ILP
2		-
3		-Check Conformal prediction.
4		-
5		-> Fonte: [Turning 30: New Ideas in Inductive Logic Programming](https://arxiv.org/abs/2002.11002)
6		-
7		-## Introduction
8		-
9		-- How pILP relates to:
10		- - ILP?
11		- - ASP?
12		- - RML?
13		-- What
14		- - tools?
15		- - methods?
16		- - theory?
17		- - Distributed semantics
18		- - applications?
19		-
20		-### Overview of Bibliography and State of the Art
21		-
22		-Recursion; Predicate Invention; Higher order, ASP Hypotheses; Optimality; Prolog, ASP, NNs
23		-
24		-## Context
25		-
26		-### Kanren
27		-
28		-### Inductive Logic Programming
29		-
30		-### Answer Set Programming
31		-
32		-### Relational Machine Learning
33		-
34		-### SAT Solvers
35		-
36		-## Tools
37		-
38		-- [(mini)kanren](http://minikanren.org/)
39		- - in Julia: [MuKanren](https://github.com/latticetower/MuKanren.jl), [YA microkanren in Julia](https://www.philipzucker.com/yet-another-microkanren-in-julia/)!.
40		-- [metagol \| archive](https://github.com/metagol/metagol) _superseeded by popper._
41		-- ILP: [popper](https://github.com/logic-and-learning-lab/Popper)
42		-- ASP: [ILASP](https://github.com/ilaspltd/ILASP-releases)
43		-- [Inspire \| Kazmi et al. 2017]()
44		-- ASP: [Potassco: clingo, clasp, ...](https://potassco.org/)
45		-- [cplint (on SWISH)](http://cplint.ml.unife.it/)
46		- - exact probabilistic inference (PITA)
47		- - Fabrizio Riguzzi and Terrance Swift. Well-definedness and efficient inference for probabilistic logic programming under the distribution semantics. Theory and Practice of Logic Programming, 13(Special Issue 02 - 25th Annual GULP Conference):279-302, © Cambridge University Press, March 2013.
48		- - Monte Carlo inference (MCINTYRE)
49		- - Fabrizio Riguzzi. MCINTYRE: A Monte Carlo system for probabilistic logic programming. Fundamenta Informaticae, 124(4):521-541, © IOS Press, 2013.
50		- - Metropolis/Hastings sampling
51		- - Arun Nampally and C. R. Ramakrishnan. Adaptive MCMC-Based Inference in Probabilistic Logic Programs. arXiv preprint arXiv:1403.6036, 2014.
52		- - parameter learning (EMBLEM)
53		- - Elena Bellodi and Fabrizio Riguzzi. Expectation Maximization over binary decision diagrams for probabilistic logic programs. Intelligent Data Analysis, 17(2):343-363, © IOS Press, 2013.
54		- - SLIPCOVER algorithm for structure learning
55		- - Elena Bellodi and Fabrizio Riguzzi. Structure learning of probabilistic logic programs by searching the clause space. Theory and Practice of Logic Programming, 15(2):169-212, © Cambridge University Press, 2015.
56		- - LEMUR algorithm for structure learning
57		- - Nicola Di Mauro, Elena Bellodi, and Fabrizio Riguzzi. Bandit-based Monte-Carlo structure learning of probabilistic logic programs. Machine Learning, 100(1):127-156, © Springer International Publishing, July 2015.
58		-
59		-## Methods
60		-
61		-## Theory
62		-
63		-### Distributed Semantics
64		-
65		-## Applications
66		-
67		-### ELearning
...	...	@@ -0,0 +1 @@
	1	+/home/fc/Insync/mangon@gmail.com/Google Drive - Shared with me/biblio
0	2	\ No newline at end of file
...	...
...	...	@@ -0,0 +1 @@
	1	+{"url": "https://drive.google.com/file/d/19ZBN9zT4YyLOEeMNvpA9Baqw9KKWd-dx/view?usp=drivesdk", "file_id": "19ZBN9zT4YyLOEeMNvpA9Baqw9KKWd-dx", "account_email": "mangon@gmail.com"}
0	2	\ No newline at end of file
...	...
1	1	from itertools import combinations, product
2	2
3	3	def fmt(expr):
	4	+ """Doc string"""
4	5	return ",".join(f"{x:>2}" for x in expr)
5	6
6	7	def c(expr):
	8	+ """Doc string"""
7	9	def litcomp(x):
8	10	if x == "⊤":
9	11	return "⊥"
...	...	@@ -16,6 +18,7 @@ def c(expr):
16	18	return [litcomp(x) for x in expr]
17	19
18	20	def domain(symbols, unary="¬"):
	21	+ """Doc string"""
19	22	atoms = list(symbols)
20	23	literals = [
21	24	[f"{u}{a}" for u in unary] +
...	...
...	...	@@ -1,27 +0,0 @@
1		-## Zugzwang Meetings
2		-
3		-### 2023-01-10 \| 15:00
4		-
5		-- Paper
6		-- Project
7		-- Latent Facts
8		-
9		-### 2022-12-12
10		-
11		-- Is the project proposal ok? How long/detailed should it be?
12		-- Initial exploratory code `event_lattice.py` and `EventLattice.ipynb` done.
13		-- Start writing paper: Introduction, state of the art, motivation
14		- - Identify key problems
15		- - Target Conferences
16		- - KR;
17		- - [ICLP](https://waset.org/language-planning-conference-in-april-2023-in-lisbon);
18		- - [ECAI](https://ecai2023.eu/)
19		-- Next task for prototype:
20		- - Get stable models from potassco/s(casp)
21		- - other?
22		-
23		-
24		-### 2022-12-05
25		-
26		-- Created shared folder (gdrive:zugzwang) <https://drive.google.com/drive/folders/1xs-cjxWJzn2JxqeNgh9LX5xWN50BW-Be?usp=share_link>
27		-- Refine project tasks, for Bachelor, M.Sc., Ph.D. students and for researchers.
28	0	\ No newline at end of file
...	...	@@ -1,53 +0,0 @@
1		-# Zugzwang \| Logic and Artificial Intelligence
2		-
3		-## Team
4		-
5		-- Francisco Coelho
6		-- Salvador Abreu (PI)
7		-- Bruno Dinis (External Collaborator)
8		-
9		-## Research Questions
10		-
11		-How to extend probability annotations on an ASP program to a distribution over the possible observations? In general, the association of probabilities to some facts is not enough to uniquely define a probability of stable models. This lack of information must be carefully expressed and handled, to avoid biased results.
12		-
13		-Once made explicit, how to use such distribution, together with an empiric distribution from a dataset, to do general probabilistic tasks such as the estimation of a marginal or of the joint probability? Having a probability on a set of observations, including the stable models, might not cover all the sample space. Again, information and structure entailed from the ASP program must guide unbiased extensions to the sample space.
14		-
15		-How to use distribution measures to score ASP programs? We view ASP programs as formalized knowledge about an observable system. Some of those programs will be "better" than others, as determined by a quantitative, objective, measure, rooted on well-known functions such as the Kullback–Leibler divergence.
16		-
17		-This last question leads to the application of evolutionary algorithms to inductive logic programming and to a bridge with common machine learning methods, where a model is scored by a dataset. Other important questions lie behind the scope of this project. For example, how to deal with latent, unobserved, variables?
18		-
19		-## Novelty/Impact
20		-
21		-ASP has some key advantages over Prolog, most of which result from ASP being a truly declarative language and modern APS systems, such as CLASP, apply efficient optimisation techniques. Other systems, like ILASP, learn ASP programs with normal rules, choice rules, and hard and weak constraints. Moreover, ASP can use recent important advances concerning SAT solvers to ILP tasks. However, the assumption that the knowledge base (BK) is perfect, leaving no room for uncertainty, poses here a major limitation.
22		-
23		-One approach to overcome this restriction on logic programs is Statistical Relationship Learning (StarAI), that extends the BK with probabilities in order to setup a distribution representation. The formal setting for this approach rests on Sato's Distribution Semantics and frames systems such as Problog and PRISM. But these systems are oriented towards Prolog-like programs and semantic and leave out ASP program learning.
24		-
25		-One important ongoing research question is the precise semantic of an ASP program annotated with probabilities. Sato's semantic specify an unique probability distribution over Prolog-like program's atoms, but this uniqueness fails for ASP programs. Efforts to address this problem are either based on Credal sets or on selecting one probability over others, such as the P-log, the LP^MLN or the PrASP languages.
26		-
27		-The novelty of this research is to define the semantic of an ASP program + probability annotated facts + observations from the following process: (a) parametrise the uncertainty on stable models and annotations, (b) setup a partition of the sample space around on the stable models and (c) use observations to estimate the value of the uncertainty parameters of step (a).
28		-If successful, it would have an impact on how logic programs express and are used to deal with real-world problems, where both uncertainty and formal KB are required ingredients. Also, when used in ILP problems the resulting models are logic programs, much simpler to understand than numeric models.
29		-
30		-## Expected Results / Demonstrators
31		-
32		-Our aim is to develop a set of software tools to apply and evaluate the theoretical results on well-known, and relevant, problems. The initial target framework is the Potassco suite, that provides a Python API to state-of-the-art grounding (CLASP) and solving (CLINGO) tools, as well as ASP-Core-2 Language support.
33		-
34		-The researchers will do the theoretical study and supervise one to three undergraduate, master or Ph.D. students on the implementation tasks. Theoretical work includes formalization and assessment of methods; The students will implement the tools required to experiment, explore and test those methods. The degree of complexity of the implementation tasks must be adapted to the individual competences and interests of the students.
35		-
36		-The theoretical work is to be reported in one or two papers with intermediate results and a final comprehensive paper for a conference.
37		-
38		-## Relationship of activity w/ NOVA LINCS strategic program and Research Group work plan
39		-
40		-This project involves two members from the NOVA LINCS "Knowledge-Based Systems" research group, from the Universidade de Évora pole and a member of the CIMA center from the Universidade de Évora.
41		-
42		-Part of the proposed research and software is currently being developed.
43		-
44		-## Resources required and justification
45		-
46		-Students are expected to benefit from a BIC or a BI scholarship. Depending on the candidates the respective durations can range from 3 to 12 months and the respective amounts from 486.12€ to 1144.64€ per month.
47		-
48		-Considering one undergraduate student with a 3 month scholarship:
49		-
50		-- 972.24 € = 3 months x 486.12 €/month;
51		-- TOTAL: 972.24 €
52		-
53		-
...	...	@@ -1,54 +0,0 @@
1		-# Concurso Bolsa \| TEMPLATE
2		-
3		-- _centro de investigação ou departamento_
4		-
5		-- _identificação da tipologia de bolsa oferecida e número de vagas_
6		-
7		-- _dia_ de _mês_ de 2023
8		-
9		-Encontra-se aberto concurso para a atribuição de _número_ Bolsas _tipo_ no âmbito do projeto _título do projeto_, _referência_, financiado por fundos nacionais através da _…_ exemplo: FCT/MCTES e cofinanciado pelo Fundo Europeu de Desenvolvimento Regional _FEDER_ através do COMPETE – Programa Operacional Fatores de Competitividade _POFC_ _quando aplicável_, nas seguintes condições:
10		-
11		-- Área Científica: …
12		-
13		-- Requisitos de admissão: _indicar a habilitação académica necessária, bem como a experiência exigida em investigação e outros requisitos de admissão, incluindo fatores preferenciais_
14		-Escolher uma opção:
15		-
16		- - Conforme o Regulamento de Bolsas de Investigação da FCT nº950/2019 de 16 de dezembro de 2019, artigo 3º e 6º, os candidatos a BI _Bolsas de Investigação_ devem cumprir como condição para a atribuição da bolsa, a inserção efetiva em ciclos de estudos conducentes à atribuição de graus académicos ou em cursos não conferentes de grau académico. Os cursos não conferentes de grau académico correspondem aos cursos previstos na alínea e_ do nº 3 do artigo 4º do Decreto-Lei nº74/2006 de 24 de março e deverão ser desenvolvidos numa instituição de ensino superior em associação a pelo menos uma unidade de I&D, incluindo-se o plano do curso numa ou em várias áreas de investigação da unidade.
17		- - Conforme o Regulamento de Bolsas de Investigação da FCT nº950/2019 de 16 de dezembro de 2019, artigo 5º, — As BII _Bolsas de Iniciação à Investigação_ não podem ser atribuídas a quem já tenha beneficiado de bolsas de investigação direta ou indiretamente financiadas pela FCT, atribuídas nos termos do Estatuto do Bolseiro de Investigação.
18		- - Relativamente às BIPD _Bolsas de Investigação Pós-doutoral_, ao nível dos requisitos a verificar para atribuição das mesmas _artigo 7º do Regulamento de Bolsas de Investigação da FCT nº950/2019_ destaca-se a necessidade do grau de doutor ter sido obtido nos 3 anos anteriores à data de submissão da candidatura à bolsa e, ao nível da sua execução, o facto de apenas poder ser renovada até ao prazo máximo de 3 anos.
19		-
20		-- Plano de trabalhos: _apresentar um resumo dos trabalhos a desenvolver e dos objetivos a atingir_
21		-
22		-- Legislação e regulamentação aplicável: A concessão da Bolsa de Investigação será realizada mediante a celebração de um contrato entre a Universidade de Évora e o bolseiro conforme minuta <https://www.fct.pt/apoios/Minuta_Contrato_Bolsa.docx> , nos termos do Estatuto do Bolseiro de Investigação _Lei nº40/2004 de 18 de agosto e decreto-lei nº 123/2019 de 28 de agosto_ e de acordo com a legislação e Regulamento de Bolsas de Investigação da Fundação para a Ciência e a Tecnologia, I.P em vigor, regulamento nº950/2019 de 16 de dezembro de 2019: <https://www.fct.pt/apoios/bolsas/regulamento.phtml.pt> e demais normas aplicáveis.
23		-
24		-- Local de trabalho: O trabalho será desenvolvido no(a) _denominação da unidade de investigação_ da Universidade de Évora, sob a orientação científica do Professor(a)/Doutor(a) ………
25		-
26		-- Duração da(s) bolsa(s): A bolsa terá a duração de ..... meses, com início previsto em ..... _mês_ de .......... _ano_. O contrato de bolsa poderá ser renovado até _ex._ ao máximo de ….. meses ou até ao final da dotação orçamental do projeto de financiamento _…_.
27		-
28		-- Valor do subsídio de manutenção mensal: O montante da bolsa corresponde a €………., conforme tabela de valores das bolsas atribuídas diretamente pela FCT, I.P. no País _http://fct.pt/apoios/bolsas/valores_, sendo os pagamentos efetuados mensalmente, através de cheque ou transferência bancária.
29		-
30		-- Métodos de seleção: Os métodos de seleção a utilizar serão os seguintes: _avaliação curricular, entrevista, provas de conhecimento, ou outros_, com a respetiva valoração de _indicar os valores atribuídos a cada critério ou item avaliado e sua ponderação percentual_.
31		-
32		-- Composição do Júri de Seleção: _identificação do Presidente do Júri e dos vogais efetivos e suplentes_
33		-
34		-- Forma de publicitação/notificação dos resultados: Os resultados finais da avaliação serão publicitados, através de lista ordenada _alfabeticamente, por nota final obtida ou outra_ afixada em local visível e público do(a) _indicar local da instituição_, sendo o candidato(a) aprovado(a) notificado através de _email, ofício ou outro_.
35		-Nos termos de direito de audiência prévia dos interessados o projeto de Classificação Final será anunciado por qualquer meio escrito a todos os interessados.
36		-Após comunicação da lista provisória dos resultados da avaliação, os candidatos dispõem de um período de 10 dias úteis para, querendo, se pronunciarem em sede de audiência prévia de interessados.
37		-
38		-- Prazo de candidatura e forma de apresentação das candidaturas: O concurso encontra-se aberto no período de ......................... a ......................... de 2022 e os resultados da seleção serão publicados até………………..de………………….de 2022. _O prazo de apresentação de candidaturas não deve ser inferior a 10 dias úteis_.
39		-
40		-- As candidaturas devem ser formalizadas, obrigatoriamente, através do envio de carta de candidatura acompanhada dos seguintes documentos: _ex: Curriculum Vitae, certificado de habilitações, cartas de referência ou recomendação e outros documentos comprovativos considerados relevantes_.
41		- - Para efeitos de candidatura os comprovativos podem ser substituídos por declaração de honra do candidato, mas a não demonstração, em fase de contratualização, da posse do grau exigido à data limite da candidatura ou a não apresentação dos comprovativos de matrícula ou inscrição em ciclo de estudos ou curso não conferente de grau, para as bolsas com essa componente, implicam a anulação da avaliação do candidato.
42		- - Os graus académicos obtidos em países estrangeiros necessitam de registo por uma Instituição Portuguesa de acordo com o Decreto-lei nº. 66/2018, de 16 de agosto e a Portaria nº. 33/2019, de 25 de janeiro. A apresentação do certificado é obrigatória para a assinatura do contrato.
43		- Mais informação poderá ser obtida em: <https://www.dges.gov.pt/pt/pagina/reconhecimento?plid=374>
44		-
45		-
46		-- As candidaturas deverão ser remetidas por e-mail para:
47		- - Prof. Doutor(a)………………….
48		- - _Centro/Dep._……………………………….. da Universidade de Évora
49		- - e-mail:
50		-
51		-
52		-
53		-_logos do programa de financiamento quando aplicável_
54		-