# Latex Why are my references empty

I am having a difficult time figuring out why my reference list after exporting a bibtex file from refworks. My tex code consists of one master file that ties together a set of files that contain the relevant text for the different sections of my document. Please see below for the combined tex code (after replacing all input statements with the relevant code) and the bib file.

Tex file:

\documentclass[11pt,twoside]{article}
\usepackage{apacite}

\begin{document}

% title page. Edit as needed for future publications.
\title{Research Proposal}
\author{Teererai Marange \\
Department of Computer Science \\
University of Auckland\\
\texttt{t.marange@auckland.ac.nz}}
\date{January 26, 2017}
\maketitle
% end title page.

% abstract goes here
\newpage
\begin{abstract}
Abstract goes here
\end{abstract}
\newpage

\tableofcontents
\newpage

%begin introduction
\section {Introduction}
Lorem Ipsum Doremifaso

%\cite{Lellis2014} use to cite

%references our biliography. This must go whereever we use our bibtex references

\bibliography{references}
\bibliographystyle{apacite}

\end{document}


bibtex file:

  @misc{RefWorks:doc:572abc36e4b087a1e3af61fd,
author =     {Estimating the Efficiency of Backtrack Programs},
year =   {1975},
title =      {MATHEMATICS OF COMPUTATION, VOLUME 29, NUMBER 129 JANUARY 1975, PAGES 121-136},
volume =     {29},
abstract =   {Abstract.     One  of  the  chief  difficulties   associated   with  the  so-called  backtracking    tech- nique  for  combinatorial     problems   has  been  our  inability   to  predict   the  efficiency   of  a given  algorithm,   or to  compare   the  efficiencies   of different   approaches,   without   actu- ally writing   and  running   the  programs.    This  paper  presents   a simple  method   which  pro- duces  reasonable   estimates   for  most  applications,    requiring   only  a modest   amount   of hand  calculation.     The  method   should   prove  to  be of  considerable    utility   in  connection with  D. H. Lehmer's   branch-and-bound     approach   to  combinatorial    optimization.}
}
@article{Lellis2014,
author={Levi Lelis and Roni Stern and Ariel Felner and Sandra Zilles and Robert Holte},
year={2014},
month={November},
title={Predicting optimal solution cost with conditional probabilities},
journal={Annals of Mathematics and Artificial Intelligence},
volume={72},
number={3},
pages={267-295},
abstract={Heuristic search algorithms are designed to return an optimal path from a start state to a goal state. They find the optimal solution cost as a side effect. However, there are applications in which all one wants to know is an estimate of the optimal solution cost. The actual path from start to goal is not initially needed. For instance, one might be interested in quickly assessing the monetary cost of a project for bidding purposes. In such cases only the cost of executing the project is required. The actual construction plan could be formulated later, after bidding. In this paper we propose an algorithm, named Solution Cost Predictor (SCP), that accurately and efficiently predicts the optimal solution cost of a problem instance without finding the actual solution. While SCP can be viewed as a heuristic function, it differs from a heuristic conceptually in that: 1) SCP is not required to be fast enough to guide search algorithms; 2) SCP is not required to be admissible; 3) our measure of effectiveness is the prediction accuracy, which is in contrast to the solution quality and number of nodes expanded used to measure the effectiveness of heuristic functions. We show empirically that SCP makes accurate predictions on several heuristic search benchmarks.},
isbn={1012-2443},
language={English},
doi={10.1007/s10472-014-9432-8}
}
@inproceedings{RefWorks:doc:5850e62fe4b02dcd50f40f9c,
author={Carlos Linares Lopez and Andreas Junghanns},
year={2002},
title={Perimeter search performance},
booktitle={International Conference on Computers and Games},
publisher={Springer},
pages={345-359}
}
@book{RefWorks:doc:57298613e4b0a05ab1595b05,
author={Mike Barley and Levi H.S. Lellis and Sandraz Zilles and Robert C. Holte},
year={2016},
title={Heuristic Subset Selection in Classical Planning},
language={English},
url={http://replace-me/ebraryid=10477244}
}
@article{RefWorks:doc:5781f9f9e4b0fbd8da0bf0da,
author={Mike Barley and Pat Riddle and Santiago Franco},
year={2014},
title={Overcoming the utility problem in heuristic generation: Why time matters.&nbsp;},
journal={Proceedings of the Twenty-Proceedings Fourth International Conference on Automated Planning and Scheduling},
abstract={Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed. Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. While no previous familiarity with heuristic search is necessary the reader should have a basic knowledge of algorithms, data structures, and calculus. Real-world case studies and chapter ending exercises help to create a full and realized picture of how search fits into the world of artificial intelligence and the one around us. This title provides real-world success stories and case studies for heuristic search algorithms. It includes many AI developments not yet covered in textbooks such as pattern databases, symbolic search, and parallel processing units.},
language={English},
url={http://replace-me/ebraryid=10477244}
}
@article{RefWorks:doc:572abc30e4b087a1e3af61fa,
author={Pang C. Chen},
year={1992},
month={Apr 1,},
title={Heuristic Sampling: A Method for Predicting the Performance of Tree Searching Programs},
journal={SIAM Journal on Computing},
volume={21},
number={2},
pages={295},
abstract={Determining the feasibility of a particular search program is important in practical situations, especially when the computation involved can easily require days, or even years. To help make such predictions, a simple procedure based on a stratified sampling approach is presented. This new method, which is called heuristic sampling, is a generalization of Knuth's original algorithm for estimating the efficiency of backtrack programs. With the aid of simple heuristics, this method can produce significantly more accurate cost estimates for commonly used tree search algorithms such as depth-first, breadth-first, best-first, and iterative-deepening.},
isbn={0097-5397},
language={English},
url={http://search.proquest.com/docview/919708785},
doi={10.1137/0221022}
}
@article{RefWorks:doc:577dfc64e4b078abda92e8f1,
author={Armand Prieditis and Robert Davis},
year={1995},
title={Quantitatively relating abstractness to the accuracy of admissible heuristics},
journal={Artificial Intelligence},
volume={74},
number={1},
pages={165-175},
isbn={0004-3702},
language={English},
url={http://www.sciencedirect.com/science/article/pii/000437029400084E},
doi={10.1016/0004-3702(94)00084-E}
}
@misc{RefWorks:doc:572bb99be4b0f8534b8b5856,
author =     {Valtorta Marco},
year =   {1984},
title =      {A Result on the Computational Complexity of Heuristic Estimates for the A* Algorithm},
journal =    {Information Sciences 34},
abstract =   {The performance of a new heuristic search algorithm is analyzed. The algorithm uses a
formal representation (semantic representation) that contains enough information to compute
the heuristic evaluation function h (n). as defined in the context of A *. without requiring a
human expert to provide it. The heuristic is computed by 􀂮olving less constrained subproblems
(auxiliary problems) of the given problem. The new algorithm is shown to be less efficient than
the Dijkstra algorithm. according to the complexity measure" number of node expansions."
This proves that it is not efficient to compute heuristics for A* by solving auxiliary problems
with backtracking.},
isbn =   {0020-0255}
}


## migrated from stackoverflow.comJan 27 '17 at 19:09

This question came from our site for professional and enthusiast programmers.

• In addition to what sieste mentions in his/hers answer, have you actually run bibtex in between runs of pdflatex? – Torbjørn T. Jan 27 '17 at 19:34

You have to actually cite one of the entries. For example, uncomment the line

%\cite{Lellis2014} use to cite


and you will get that entry in your list of references.

To include all entries from your bibfile without citing them, put the line

\nocite{*}


Of course you have to run bibtex, and the bibliography file has to be called references.bib.