Proper bibliography support.

Francisco Coelho
1 parent aa257377
Showing 3 changed files with 79 additions and 15 deletions Show diff stats
task_01/proposal.pdf
task_01/proposal.tex
task_01/zugzwang.bib
 \documentclass[a4paper]{article}
  
+
+\usepackage[
+    bibstyle=authoryear,
+    citestyle=alphabetic
+]{biblatex} %Imports biblatex package
+\addbibresource{zugzwang.bib} %Import the bibliography file
+
 \usepackage{commath}
 \usepackage{hyperref}
 \hypersetup{
     colorlinks=true,
+    citecolor=blue,
     linkcolor=blue,
 }
  
 \title{Zugzwang\\\textit{Logic and Artificial Intelligence}}
-\author{Francisco Coelho\\ \texttt{fc@uevora.pt}}
+\author{
+    \begin{tabular}{cc}
+        Francisco Coelho & Bruno Dinis\\
+        \texttt{fc@uevora.pt} & \texttt{bruno.dinis@uevora.pt}
+    \end{tabular}    
+}
  
 \begin{document}
 \maketitle
-
+\cite{*}
 \begin{abstract}
     A major limitation of logical representations is the implicit assumption that the Background Knowledge (BK) is perfect. This assumption is problematic if data is noisy, which is often the case. Here we aim to explore how ASP specifications with probabilistic facts can lead to characterizations of probability functions on the specification's domain. 
 \end{abstract}
  
 \section{Introduction and Motivation }
  
-Answer Set Programming (ASP) is a logic programming paradigm based on the Stable Model semantics of Normal Logic Programs (NP) that can be implemented using the latest advances in SAT solving technology. ASP is a truly declarative language that supports language constructs such as disjunction in the head of a clause, choice rules, and hard and weak constraints.
+Answer Set Programming (ASP) \cite{gebser2012answer} is a logic programming paradigm based on the Stable Model semantics of Normal Logic Programs (NP) that can be implemented using the latest advances in SAT solving technology. ASP is a truly declarative language that supports language constructs such as disjunction in the head of a clause, choice rules, and hard and weak constraints.
  
-The Distribution Semantics (DS) is a key approach to extend logical representations with probabilistic reasoning. Probabilistic Facts (PF) are the most basic stochastic DS primitive and they take the form of logical facts, $a$, labelled with a probability, such as $p::a$; Each probabilistic fact represents a boolean random variable that is true with probability $p$ and false with probability $1 - p$. A (consistent) combination of the PFs defines a \textit{total choice} $\theta = \set{p::a, \ldots}$ such that
+The Distribution Semantics (DS) \cite{riguzzi2022foundations} is a key approach to extend logical representations with probabilistic reasoning. Probabilistic Facts (PF) \cite{riguzzi2022foundations} are the most basic stochastic DS primitive and they take the form of logical facts, $a$, labelled with a probability, such as $p::a$; Each probabilistic fact represents a boolean random variable that is true with probability $p$ and false with probability $1 - p$. A (consistent) combination of the PFs defines a \textit{total choice} $\theta = \set{p::a, \ldots}$ such that
  
 \begin{equation}
     P(\theta) = \prod_{a\in\theta} p \prod_{a \not\in \theta} (1- p).
@@ -34,7 +47,7 @@ Our goal is to extend this probability, from total choices, to cover the specifi
     \item Also, given a dataset and a divergence measure, now the specification can be scored (by the divergence w.r.t. the \emph{empiric} distribution of the dataset), and sorted amongst other specifications. This is a key ingredient in algorithms searching, for example, an \textit{optimal specification} of the dataset. 
 \end{enumerate}
  
-This goal faces a critical problem concerning situations where \textit{multiple} standard models result from a given total choice, illustrated by the following example. The specification 
+This goal faces a critical problem concerning situations where \textit{multiple} standard models result from a given total choice\cite{cozman2020joy}, illustrated by the following example. The specification 
 $$
 \begin{aligned}
     0.3::a&,\cr
@@ -71,22 +84,25 @@ A team of two researchers and a undergraduate, master, or Ph.D. student, working
     \item Application and evaluation of this approach, using tools such as \hyperlink{https://ciao-lang.org/playground/scasp.html}{s(casp)}, or the \hyperlink{https://potassco.org/}{Potassco suit} to a range of problems from the simple \textit{Burglar, Earthquake, Alarm} to measuring a specification accuracy on a given dataset, or finding an optimal specification for a given dataset given some background knowledge. 
 \end{itemize}
  
-While the theoretical word for this project has yet to be completed, there are some relevant tasks that, with different levels of ambition, can be solved right now: \marginpar{Estas tarefas precisam de maior\ldots\ folego.}
+While the theoretical work for this project has yet to be completed, there are some relevant tasks that, with different levels of ambition, can be started right now: 
 \begin{enumerate}
-    \item \textit{Extract Probability Annotations}. For example, convert the annotated specification \verb!0.3::a. b ; c :- a.! to \verb! a ; -a. b ; c :- a!. This is a simple, syntactical task that can be implemented either with \texttt{prolog} or using \texttt{python} and the API provided by the Potassco suite.  
-    \item \textit{Extended Probability to Stable Models}. Application of the method outlined before, where the probability of total choices is extended to standard models using parameters, which are next estimated with a dataset.
+    \item \textit{Extract Probability Annotations}. For example, convert the annotated specification \verb!0.3::a. b ; c :- a.! to \verb! a ; -a. b ; c :- a!. This is a simple, syntactical task that can be implemented either with \texttt{prolog} or using \texttt{python} and the API provided by the Potassco suite.
+    \item \textit{Extend Probability to Stable Models}. Application of the method outlined before, where the probability of total choices is extended to standard models using parameters, which are next estimated with a dataset.  
     \item \textit{Relate Samples, Stable Models and Total Choices}. Determine which stable models, or total choices, contain and which are contained in a given sample. 
+    \item \textit{Propagate Probability to Samples}. Use of the relation above to assign a probability to an arbitrary event, using an aggregation operation, such as $\max$ or $\prod$, from the relevant stable models.
+    \item \textit{Process Evaluation on Well-known Cases}. Assessment of the implemented prototype using well-known problems such as the ``A\-larm-Bur\-glar-Earth\-qua\-ke''. 
 \end{enumerate} 
  
 \section*{References}
  
+\printbibliography
  
-\begin{enumerate}
-    \item Victor Verreet, Vincent Derkinderen, Pedro Zuidberg Dos Martires, Luc De Raedt, Inference and Learning with Model Uncertainty in Probabilistic Logic Programs (2022)
-    \item Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton, Inductive logic programming at 30 (2021)
-    \item Fabio Gagliardi Cozman, Denis Deratani Mauá, The joy of Probabilistic Answer Set Programming: Semantics - complexity, expressivity, inference (2020)
-    \item Fabrizio Riguzzi, Foundations of Probabilistic Logic Programming Languages, Semantics, Inference and Learning. Rivers Publishers (2018) 
-    \item Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub, Answer Set Solving in Practice, Morgan \& Claypool Publishers (2013)
-\end{enumerate}
+% \begin{enumerate}
+%     \item Victor Verreet, Vincent Derkinderen, Pedro Zuidberg Dos Martires, Luc De Raedt, Inference and Learning with Model Uncertainty in Probabilistic Logic Programs (2022)
+%     \item Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton, Inductive logic programming at 30 (2021)
+%     \item Fabio Gagliardi Cozman, Denis Deratani Mauá, The joy of Probabilistic Answer Set Programming: Semantics - complexity, expressivity, inference (2020)
+%     \item Fabrizio Riguzzi, Foundations of Probabilistic Logic Programming Languages, Semantics, Inference and Learning. Rivers Publishers (2018) 
+%     \item Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub, Answer Set Solving in Practice, Morgan \& Claypool Publishers (2013)
+% \end{enumerate}
  
 \end{document}
 \ No newline at end of file
@@ -0,0 +1,48 @@
+@inproceedings{verreet2022inference,
+  title={Inference and learning with model uncertainty in probabilistic logic programs},
+  author={Verreet, Victor and Derkinderen, Vincent and Dos Martires, Pedro Zuidberg and De Raedt, Luc},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={36},
+  number={9},
+  pages={10060--10069},
+  year={2022}
+}
+
+@article{cropper2022inductive,
+  title={Inductive logic programming at 30},
+  author={Cropper, Andrew and Duman{\v{c}}i{\'c}, Sebastijan and Evans, Richard and Muggleton, Stephen H},
+  journal={Machine Learning},
+  volume={111},
+  number={1},
+  pages={147--172},
+  year={2022},
+  publisher={Springer}
+}
+
+@article{cozman2020joy,
+  title={The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference},
+  author={Cozman, Fabio Gagliardi and Mau{\'a}, Denis Deratani},
+  journal={International Journal of Approximate Reasoning},
+  volume={125},
+  pages={218--239},
+  year={2020},
+  publisher={Elsevier}
+}
+
+@book{riguzzi2022foundations,
+  title={Foundations of probabilistic logic programming: Languages, semantics, inference and learning},
+  author={Riguzzi, Fabrizio},
+  year={2022},
+  publisher={CRC Press}
+}
+
+@article{gebser2012answer,
+  title={Answer set solving in practice},
+  author={Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and Schaub, Torsten},
+  journal={Synthesis lectures on artificial intelligence and machine learning},
+  volume={6},
+  number={3},
+  pages={1--238},
+  year={2012},
+  publisher={Morgan \& Claypool Publishers}
+}
1	1	\documentclass[a4paper]{article}
2	2
	3	+
	4	+\usepackage[
	5	+ bibstyle=authoryear,
	6	+ citestyle=alphabetic
	7	+]{biblatex} %Imports biblatex package
	8	+\addbibresource{zugzwang.bib} %Import the bibliography file
	9	+
3	10	\usepackage{commath}
4	11	\usepackage{hyperref}
5	12	\hypersetup{
6	13	colorlinks=true,
	14	+ citecolor=blue,
7	15	linkcolor=blue,
8	16	}
9	17
10	18	\title{Zugzwang\\\textit{Logic and Artificial Intelligence}}
11		-\author{Francisco Coelho\\ \texttt{fc@uevora.pt}}
	19	+\author{
	20	+ \begin{tabular}{cc}
	21	+ Francisco Coelho & Bruno Dinis\\
	22	+ \texttt{fc@uevora.pt} & \texttt{bruno.dinis@uevora.pt}
	23	+ \end{tabular}
	24	+}
12	25
13	26	\begin{document}
14	27	\maketitle
15		-
	28	+\cite{*}
16	29	\begin{abstract}
17	30	A major limitation of logical representations is the implicit assumption that the Background Knowledge (BK) is perfect. This assumption is problematic if data is noisy, which is often the case. Here we aim to explore how ASP specifications with probabilistic facts can lead to characterizations of probability functions on the specification's domain.
18	31	\end{abstract}
19	32
20	33	\section{Introduction and Motivation }
21	34
22		-Answer Set Programming (ASP) is a logic programming paradigm based on the Stable Model semantics of Normal Logic Programs (NP) that can be implemented using the latest advances in SAT solving technology. ASP is a truly declarative language that supports language constructs such as disjunction in the head of a clause, choice rules, and hard and weak constraints.
	35	+Answer Set Programming (ASP) \cite{gebser2012answer} is a logic programming paradigm based on the Stable Model semantics of Normal Logic Programs (NP) that can be implemented using the latest advances in SAT solving technology. ASP is a truly declarative language that supports language constructs such as disjunction in the head of a clause, choice rules, and hard and weak constraints.
23	36
24		-The Distribution Semantics (DS) is a key approach to extend logical representations with probabilistic reasoning. Probabilistic Facts (PF) are the most basic stochastic DS primitive and they take the form of logical facts, $a$, labelled with a probability, such as $p::a$; Each probabilistic fact represents a boolean random variable that is true with probability $p$ and false with probability $1 - p$. A (consistent) combination of the PFs defines a \textit{total choice} $\theta = \set{p::a, \ldots}$ such that
	37	+The Distribution Semantics (DS) \cite{riguzzi2022foundations} is a key approach to extend logical representations with probabilistic reasoning. Probabilistic Facts (PF) \cite{riguzzi2022foundations} are the most basic stochastic DS primitive and they take the form of logical facts, $a$, labelled with a probability, such as $p::a$; Each probabilistic fact represents a boolean random variable that is true with probability $p$ and false with probability $1 - p$. A (consistent) combination of the PFs defines a \textit{total choice} $\theta = \set{p::a, \ldots}$ such that
25	38
26	39	\begin{equation}
27	40	P(\theta) = \prod_{a\in\theta} p \prod_{a \not\in \theta} (1- p).
...	...	@@ -34,7 +47,7 @@ Our goal is to extend this probability, from total choices, to cover the specifi
34	47	\item Also, given a dataset and a divergence measure, now the specification can be scored (by the divergence w.r.t. the \emph{empiric} distribution of the dataset), and sorted amongst other specifications. This is a key ingredient in algorithms searching, for example, an \textit{optimal specification} of the dataset.
35	48	\end{enumerate}
36	49
37		-This goal faces a critical problem concerning situations where \textit{multiple} standard models result from a given total choice, illustrated by the following example. The specification
	50	+This goal faces a critical problem concerning situations where \textit{multiple} standard models result from a given total choice\cite{cozman2020joy}, illustrated by the following example. The specification
38	51	$$
39	52	\begin{aligned}
40	53	0.3::a&,\cr
...	...	@@ -71,22 +84,25 @@ A team of two researchers and a undergraduate, master, or Ph.D. student, working
71	84	\item Application and evaluation of this approach, using tools such as \hyperlink{https://ciao-lang.org/playground/scasp.html}{s(casp)}, or the \hyperlink{https://potassco.org/}{Potassco suit} to a range of problems from the simple \textit{Burglar, Earthquake, Alarm} to measuring a specification accuracy on a given dataset, or finding an optimal specification for a given dataset given some background knowledge.
72	85	\end{itemize}
73	86
74		-While the theoretical word for this project has yet to be completed, there are some relevant tasks that, with different levels of ambition, can be solved right now: \marginpar{Estas tarefas precisam de maior\ldots\ folego.}
	87	+While the theoretical work for this project has yet to be completed, there are some relevant tasks that, with different levels of ambition, can be started right now:
75	88	\begin{enumerate}
76		- \item \textit{Extract Probability Annotations}. For example, convert the annotated specification \verb!0.3::a. b ; c :- a.! to \verb! a ; -a. b ; c :- a!. This is a simple, syntactical task that can be implemented either with \texttt{prolog} or using \texttt{python} and the API provided by the Potassco suite.
77		- \item \textit{Extended Probability to Stable Models}. Application of the method outlined before, where the probability of total choices is extended to standard models using parameters, which are next estimated with a dataset.
	89	+ \item \textit{Extract Probability Annotations}. For example, convert the annotated specification \verb!0.3::a. b ; c :- a.! to \verb! a ; -a. b ; c :- a!. This is a simple, syntactical task that can be implemented either with \texttt{prolog} or using \texttt{python} and the API provided by the Potassco suite.
	90	+ \item \textit{Extend Probability to Stable Models}. Application of the method outlined before, where the probability of total choices is extended to standard models using parameters, which are next estimated with a dataset.
78	91	\item \textit{Relate Samples, Stable Models and Total Choices}. Determine which stable models, or total choices, contain and which are contained in a given sample.
	92	+ \item \textit{Propagate Probability to Samples}. Use of the relation above to assign a probability to an arbitrary event, using an aggregation operation, such as $\max$ or $\prod$, from the relevant stable models.
	93	+ \item \textit{Process Evaluation on Well-known Cases}. Assessment of the implemented prototype using well-known problems such as the ``A\-larm-Bur\-glar-Earth\-qua\-ke''.
79	94	\end{enumerate}
80	95
81	96	\section*{References}
82	97
	98	+\printbibliography
83	99
84		-\begin{enumerate}
85		- \item Victor Verreet, Vincent Derkinderen, Pedro Zuidberg Dos Martires, Luc De Raedt, Inference and Learning with Model Uncertainty in Probabilistic Logic Programs (2022)
86		- \item Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton, Inductive logic programming at 30 (2021)
87		- \item Fabio Gagliardi Cozman, Denis Deratani Mauá, The joy of Probabilistic Answer Set Programming: Semantics - complexity, expressivity, inference (2020)
88		- \item Fabrizio Riguzzi, Foundations of Probabilistic Logic Programming Languages, Semantics, Inference and Learning. Rivers Publishers (2018)
89		- \item Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub, Answer Set Solving in Practice, Morgan \& Claypool Publishers (2013)
90		-\end{enumerate}
	100	+% \begin{enumerate}
	101	+% \item Victor Verreet, Vincent Derkinderen, Pedro Zuidberg Dos Martires, Luc De Raedt, Inference and Learning with Model Uncertainty in Probabilistic Logic Programs (2022)
	102	+% \item Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton, Inductive logic programming at 30 (2021)
	103	+% \item Fabio Gagliardi Cozman, Denis Deratani Mauá, The joy of Probabilistic Answer Set Programming: Semantics - complexity, expressivity, inference (2020)
	104	+% \item Fabrizio Riguzzi, Foundations of Probabilistic Logic Programming Languages, Semantics, Inference and Learning. Rivers Publishers (2018)
	105	+% \item Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub, Answer Set Solving in Practice, Morgan \& Claypool Publishers (2013)
	106	+% \end{enumerate}
91	107
92	108	\end{document}
93	109	\ No newline at end of file
...	...
...	...	@@ -0,0 +1,48 @@
	1	+@inproceedings{verreet2022inference,
	2	+ title={Inference and learning with model uncertainty in probabilistic logic programs},
	3	+ author={Verreet, Victor and Derkinderen, Vincent and Dos Martires, Pedro Zuidberg and De Raedt, Luc},
	4	+ booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
	5	+ volume={36},
	6	+ number={9},
	7	+ pages={10060--10069},
	8	+ year={2022}
	9	+}
	10	+
	11	+@article{cropper2022inductive,
	12	+ title={Inductive logic programming at 30},
	13	+ author={Cropper, Andrew and Duman{\v{c}}i{\'c}, Sebastijan and Evans, Richard and Muggleton, Stephen H},
	14	+ journal={Machine Learning},
	15	+ volume={111},
	16	+ number={1},
	17	+ pages={147--172},
	18	+ year={2022},
	19	+ publisher={Springer}
	20	+}
	21	+
	22	+@article{cozman2020joy,
	23	+ title={The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference},
	24	+ author={Cozman, Fabio Gagliardi and Mau{\'a}, Denis Deratani},
	25	+ journal={International Journal of Approximate Reasoning},
	26	+ volume={125},
	27	+ pages={218--239},
	28	+ year={2020},
	29	+ publisher={Elsevier}
	30	+}
	31	+
	32	+@book{riguzzi2022foundations,
	33	+ title={Foundations of probabilistic logic programming: Languages, semantics, inference and learning},
	34	+ author={Riguzzi, Fabrizio},
	35	+ year={2022},
	36	+ publisher={CRC Press}
	37	+}
	38	+
	39	+@article{gebser2012answer,
	40	+ title={Answer set solving in practice},
	41	+ author={Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and Schaub, Torsten},
	42	+ journal={Synthesis lectures on artificial intelligence and machine learning},
	43	+ volume={6},
	44	+ number={3},
	45	+ pages={1--238},
	46	+ year={2012},
	47	+ publisher={Morgan \& Claypool Publishers}
	48	+}
...	...