Refworks에서 bibtex 파일을 내보낸 후 내 참조 목록이 왜 나타나는지 파악하는 데 어려움을 겪고 있습니다. 내 tex 코드는 내 문서의 여러 섹션에 대한 관련 텍스트가 포함된 파일 세트를 함께 묶는 하나의 마스터 파일로 구성됩니다. 결합된 tex 코드(모든 입력문을 해당 코드로 대체한 후)와 bib 파일은 아래를 참고하세요.
텍스트 파일:
\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 파일:
@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 답변1
실제로 항목 중 하나를 인용해야 합니다. 예를 들어, 다음 줄의 주석 처리를 해제하세요.
%\cite{Lellis2014} use to cite
그러면 참조 목록에 해당 항목이 표시됩니다.
인용하지 않고 bibfile의 모든 항목을 포함하려면 다음 줄을 입력하십시오.
\nocite{*}
문서 본문 어딘가에 있습니다.
물론 를 실행해야 하고 bibtex, 참고문헌 파일을 호출해야 합니다 references.bib.