RefWorks から bibtex ファイルをエクスポートした後、参考文献リストがなぜそうなるのか理解するのに苦労しています。私の tex コードは、文書のさまざまなセクションに関連するテキストを含む一連のファイルを結合する 1 つのマスター ファイルで構成されています。結合された tex コード (すべての入力ステートメントを関連するコードに置き換えた後) と bib ファイルについては、以下を参照してください。
Tex ファイル:
\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{[email protected]}}
\date{January 26, 2017}
\maketitle
% end title page.
% abstract goes here
\newpage
\begin{abstract}
Abstract goes here
\end{abstract}
\newpage
% table of contents
\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 ファイル:
@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. },
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 答え1
実際にエントリの1つを引用する必要があります。たとえば、次の行のコメントを解除します。
%\cite{Lellis2014} use to cite
すると、そのエントリが参考文献リストに追加されます。
bibfileのすべてのエントリを引用せずに含めたい場合は、次の行を追加します。
\nocite{*}
ドキュメント本文のどこかに。
もちろん、 を実行しbibtex、参考文献ファイルを と呼ぶ必要がありますreferences.bib。