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task_01/proposal.pdf
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task_01/proposal.tex
| @@ -2,8 +2,8 @@ | @@ -2,8 +2,8 @@ | ||
| 2 | 2 | ||
| 3 | 3 | ||
| 4 | \usepackage[ | 4 | \usepackage[ |
| 5 | - bibstyle=authoryear, | ||
| 6 | - citestyle=alphabetic | 5 | + bibstyle=numeric, |
| 6 | + citestyle=numeric | ||
| 7 | ]{biblatex} %Imports biblatex package | 7 | ]{biblatex} %Imports biblatex package |
| 8 | \addbibresource{zugzwang.bib} %Import the bibliography file | 8 | \addbibresource{zugzwang.bib} %Import the bibliography file |
| 9 | 9 | ||
| @@ -24,8 +24,11 @@ | @@ -24,8 +24,11 @@ | ||
| 24 | } | 24 | } |
| 25 | 25 | ||
| 26 | \begin{document} | 26 | \begin{document} |
| 27 | + | ||
| 27 | \maketitle | 28 | \maketitle |
| 28 | -\cite{*} | 29 | + |
| 30 | +\nocite{*} | ||
| 31 | + | ||
| 29 | \begin{abstract} | 32 | \begin{abstract} |
| 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. | 33 | 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. |
| 31 | \end{abstract} | 34 | \end{abstract} |
text/paper_01/pre-paper.pdf
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| @@ -0,0 +1,48 @@ | @@ -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 | +} |