Commit d562e0e9 by Stalin Munoz

updating files

parent 727b15ac
......@@ -38,10 +38,10 @@
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......
......@@ -236,17 +236,27 @@ Pues está sobreestimando el costo para el nodo $D$, con ello conduciendo al agen
\end{center}
}
\note<1>{
Una heurística es consistente o monotónica si para cada sucesor de todo vertice del grafo de estados acciones, la heurística del vértice es menor o igual que la heurística de
Una heurística es consistente o monotónica si para cada sucesor de todo vertice del grafo de estados acciones, la heurística del vértice es menor o igual que la heurística de la suma del costo de la transición del vertice al sucesor con la heurística del sucesor.
En este ejemplo la heurística mostrada cumple con esta propiedad.
}
\only<2>{
\begin{center}
\includegraphics[scale=0.5]{admisible-02.pdf}
\includegraphics[scale=0.4]{consistencia-02.pdf}
\end{center}
}
\note<2>{
En este ejemplo tenemos una heurística no admisible.
Aquí ilustramos un ejemplo donde la heurística no es monotónica.
Pues está sobreestimando el costo para el nodo $D$, con ello conduciendo al agente por un camino más largo.
Si avanzamos en la dirección del objetivo por el camino $A$, $C$ y $B$.
Observamos que para el nodo $A$ la función $h$ no es menor que la suma del costo a $C$, que es 1, con la heurística del nodo $C$ que es 2.
Esto significa que el valor de $f(A) = g(A)+ 4$ es mayor que el valor $f(C) = g(A) + 1 + 2$. Esto al avanzar en la profundidad observamos que el valor $f$ no se incrementa, sino que disminuye.
Esto no es bueno para $A^*$, pues una heurística no monotónica puede llevarnos a soluciones subóptimas.
Admisibilidad de una heurística esta implicada si la heurística es consistente.
}
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......
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......@@ -9,7 +9,7 @@
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......
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\documentclass[xcolor=dvipsnames]{beamer}
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\title{Algoritmo de búsqueda Bidireccional}
\author{Stalin Muñoz Gutiérrez}
\institute{
Centro de Ciencias de la Complejidad\\
Universidad Nacional Aut\'onoma de Mexico (UNAM)}
\date{}
\begin{document}
\parindent 2em
\frame
{
\titlepage
\note<1>{
Hoy veremos el algoritmo de búsqueda bidireccional.
Este es una variación interesante del algoritmo BFS.
Para poder aplicar el algoritmo vamos a requerir de condiciones adicionales.
Una condición es conocer el estado meta. Si no conocemos el estado meta o hay varios estados meta, no podemos aplicar el algoritmo directamente.
La otra condición es que para cualquier estado podamos conocer el conjunto de acciones y estados de los que es sucesor.
}
}
\begin{frame}[<+->]
\frametitle{Algoritmo de búsqueda bidireccional}
\begin{block}{Algoritmo basado en BFS}
Dos búsquedas por turnos.
\note<1>{
El algoritmo de búsqueda bidireccional, es en realidad dos búsquedas por turnos.
Las búsquedas podrían hacerse de manera paralela, pero requeriría modificar ligeramente el algoritmo que vamos a presentar.
}
\begin{itemize}
\item Una búsqueda progresiva o hacia adelante comenzando en el nodo inicial.
\note<2>{
La primera de las búsquedas es una búsqueda progresiva tipo BFS que comienza con el nodo inicial.
}
\item Otra búsqueda regresiva o hacia atrás comenzando con el nodo meta.
\note<3>{
La segunda búsqueda es una búsqueda regresiva o hacía atrás tipo BFS empezando del nodo meta.
}
\item El algoritmo termina cuando las búsquedas se encuentran a la mitad del camino.
\note<4>{
Cada dirección de exploración tiene una frontera de búsqueda.
El objetivo de cada exploración es descubrir un estado que es común a las dos fronteras.
Cuando dicha intersección se encuentra se puede conformar la ruta total a partir de las rutas parciales al estado común.
}
\end{itemize}
\end{block}
\end{frame}
\begin{frame}
\frametitle{Búsqueda bidireccional}
\only<1>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-10.pdf}
\end{center}
}
\note<1>{
El estado inicial se agrega a la frontera de la búsqueda progresiva.
El estado meta se agrega a la frontera de la búsqueda regresiva.
}
\only<2>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-09.pdf}
\end{center}
}
\note<2>{
La búsqueda progresiva expande el nodo inicial.
La nueva frontera son todos los estados a profundidad 1.
La búsqueda progresiva busca que su frontera se intersecte con la frontera de la búsqueda regresiva.
Esto no ha pasado aún.
}
\only<3>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-08.pdf}
\end{center}
}
\note<3>{
Ahora es el turno de la búsqueda regresiva.
Si hacemos las búsquedas por turnos garantizamos que al intersectarse las fronteras, tendremos la solución óptima.
La búsqueda regresiva expande el estado meta.
}
\only<4>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-07.pdf}
\end{center}
}
\note<4>{
Turno de la búsqueda progresiva.
Generamos la frontera actual.
Todos los sucesores de los estados de la frontera anterior.
}
\only<5>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-06.pdf}
\end{center}
}
\note<5>{
Ahora es el turno de la búsqueda regresiva.
Agregamos todos los estados a profundidad 2.
Aún no se intersectan las fronteras.
}
\only<6>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-05.pdf}
\end{center}
}
\note<6>{
Hacia adelante con profundidad 3.
}
\only<7>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-04.pdf}
\end{center}
}
\note<7>{
Hacia atrás con profundidad 3.
}
\only<8>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-03.pdf}
\end{center}
}
\note<8>{
Se alcanza profundidad 4 en la búsqueda progresiva.
No hay intersección todavía.
}
\only<9>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-02.pdf}
\end{center}
}
\note<9>{
La búsqueda regresiva también alcanza profundidad 4.
Sin intersección.
}
\only<10>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-01.pdf}
\end{center}
}
\note<10>{
Turno de la búsqueda hacia adelante.
No mostramos todos los nodos a profundidad 5.
Únicamente el nodo de intersección entre las dos fronteras.
}
\only<11>{
\begin{center}
\includegraphics[scale=0.13]{bidireccional-diagrama-00.pdf}
\end{center}
}
\note<11>{
Considerando que la intersección se encuentra tras expandir todos los estados a profundidad 5 en la frontera de la búsqueda progresiva,
y sumando el total de estados expandidos con el total de ambas fronteras,
tenemos que hemos expandido poco menos de 500 estados.
Dado que en este ejemplo la solución esta a profundidad 9,
un algoritmo BFS convencional habría expandido cerca de 30 mil estados para encontrar la solución.
Esto es un ahorro significativo en memoria.
}
\end{frame}
\begin{frame}
\frametitle{Algoritmo Bidireccional}
\begin{center}
\includegraphics[scale=0.10]{pizarron.jpg}
\end{center}
\note{
Explicamos el algoritmo Bidireccional en el pizarrón.
}
\end{frame}
\begin{frame}
\frametitle{Análisis Asintótico del algoritmo Bidireccional}
\begin{center}
\includegraphics[scale=0.35]{analisis-bidireccional.pdf}
\end{center}
\end{frame}
\note{
El desempeño del algoritmo bidireccional es muy bueno.
A pesar de utilizar memoria exponencial, al dividirse la búsqueda en dos exploraciones por turnos, el crecimiento de memoria es $O(b^{d/2})$.
Este ahorro es sustancial.
Respecto del tiempo, crece a la par con la memoria.
El algoritmo nos da la solución óptima,
y es completo.
Para problemas grandes, aún este ahorro de memoria, puede no ser suficiente.
La desventaja más notable es que el algoritmo no es tan general como los otros.
Se requiere de conocer el estado meta y también de poder ir hacia atrás de los estados para recuperar los estados predecesores.
}
\end{document}
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......@@ -6,7 +6,7 @@
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......@@ -9,7 +9,7 @@
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......@@ -396,9 +396,12 @@ Hacemos la población actual igual a la nueva población.
\begin{frame}
\frametitle{Comentario final.}
Cómputo evolutivo,
\begin{center}
\includegraphics[scale=0.5]{logoesp.jpeg}
\end{center}
Curso: Cómputo evolutivo,
dentro de la especialización Introducción a la inteligencia artificial de la UNAM.
Especialización: Introducción a la inteligencia artificial de la UNAM.
\note{
Los algoritmos genéticos son metaheurísticos muy poderosos.
......
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......@@ -36,10 +36,10 @@
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......@@ -6,7 +6,7 @@
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% descomentar para generar handouts con notas
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......
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\documentclass[xcolor=dvipsnames]{beamer}
......@@ -6,7 +6,7 @@
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% descomentar para generar handouts con notas
\setbeameroption{show notes on second screen=right}
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......
......@@ -38,10 +38,10 @@
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......@@ -6,7 +6,7 @@
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......@@ -498,6 +498,8 @@ $J$ será el siguiente en salir.
Evaluamos cuando debemos agregar a $D$ a la agenda, pues ya se había expandido antes y esta en la tabla con un costo de 1206.
Su costo acumulado es de 1357. Significa que esta ruta es peor que la anterior y sería infructuoso expandirlo nuevamente.
El siguiente nodo a sacar es $L$.
}
\only<19>{
......@@ -505,42 +507,73 @@ Su costo acumulado es de 1357. Significa que esta ruta es peor que la anterior y
\includegraphics[scale=0.30]{BBAstar-ejemplo-19.pdf}
\end{center}
}
\note<19>{}
\note<19>{
$T$ su sucesor, también supera la cota de costo.
Lo ignoramos.
}
\only<20>{
\begin{center}
\includegraphics[scale=0.30]{BBAstar-ejemplo-20.pdf}
\end{center}
}
\note<20>{}
\note<20>{
Es el turno de $V$.
No agrega nada.
}
\only<21>{
\begin{center}
\includegraphics[scale=0.30]{BBAstar-ejemplo-21.pdf}
\end{center}
}
\note<21>{}
\note<21>{
El siguiente al frente es $R$.
}
\only<22>{
\begin{center}
\includegraphics[scale=0.30]{BBAstar-ejemplo-22.pdf}
\end{center}
}
\note<22>{}
\note<22>{
Su sucesor, el estado $U$, tiene un costo acumulado que supera la cota.
Lo ignoramos también.
}
\only<23>{
\begin{center}
\includegraphics[scale=0.30]{BBAstar-ejemplo-23.pdf}
\end{center}
}
\note<23>{}
\note<23>{
Queda otra instancia del nodo $R$ en la agenda.
La sacamos.
Su costo acumulado de 1391 esta dentro de la cota.
Sin embargo ya se había expandido una $R$ a un costo de 1149.
Como esta segunda ruta a $R$, tiene un costo superior, no vamos a expandir el nodo.
}
\only<24>{
\begin{center}
\includegraphics[scale=0.30]{BBAstar-ejemplo-24.pdf}
\end{center}
}
\note<24>{}
\note<24>{
La agenda se ha vaciado.
Regresamos la mejor ruta encontrada.
En este caso la $\text{ruta}_2$.
}
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......
......@@ -61,10 +61,10 @@
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% descomentar para generar handouts con notas
\setbeameroption{show notes on second screen=right}
%\setbeameroption{show notes on second screen=right}
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......
......@@ -9,7 +9,7 @@
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......@@ -490,7 +490,7 @@ Los algoritmos metaheurísticos tienen un espíritu pragmático y generalmente comb
Frecuentemente la fuente de inspiración para diseñar dichas estrategias es la naturaleza. Como es el caso del algortimo SA que hemos visto hoy.
Una técnica muy frecuentemente utilizada en la práctica es la hibridación o combinación de estrategias. Por ejemplo con los algoritmos denominados \emph{meméticos} que combinan la evolución de una población de soluciones con aprendizaje individual.
Una estrategia frecuentemente utilizada en la práctica es la hibridación o combinación de algoritmos. Por ejemplo, los algoritmos denominados \emph{meméticos} combinan la evolución de una población de soluciones con aprendizaje individual.
}
\begin{frame}
......
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ruta de 15 pasos
ruta de 13 pasos
ruta de 11 pasos
ruta de 3 pasos
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longitud de ruta: 3
...--......----........
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longitud de ruta: 5
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@book{russell2009,
title={Artificial intelligence: A modern approach (3rd or 2nd Edition)},
author={Russell, Stuart and Norvig, Peter},
year={2009},
publisher={Prentice Hall},
note ={El libro de referencia del curso. Cubre prácticamente todos los temas a tratar}
}
@book{pearl1984,
title={Heuristics: intelligent search strategies for computer problem solving},
author={Pearl, Judea},
year={1984},
publisher={Addison-Wesley Pub. Co., Inc., Reading, MA},
note ={Cubre el tema de resolución de problemas. Búsquedas informadas}
}
@book{heineman2016algorithms,
title={Algorithms in a nutshell: a practical guide},
author={Heineman, George T and Pollice, Gary and Selkow, Stanley},
year={2016},
publisher={" O'Reilly Media, Inc."}
}
@book{ertel2018introduction,
title={Introduction to artificial intelligence},
author={Ertel, Wolfgang},
year={2018},
publisher={Springer}
}
@book{michalewicz2013solve,
title={How to solve it: modern heuristics},
author={Michalewicz, Zbigniew and Fogel, David B},
year={2013},
publisher={Springer Science \& Business Media}
}
@book{goldberg1989,
title={Genetic algorithms in search, optimization, and machine learning},
author={Goldberg, David Edward and others},
volume={412},
year={1989},
publisher={Addison-wesley Reading Menlo Park},
note = {Cubre el tema de algoritmos genéticos}
}
@book{nilsson2010,
title={The quest for artificial intelligence},
author={Nilsson, Nils J},
year={2010},
publisher={Cambridge University Press},
note = {Discute extensivamente el área de Inteligencia Artificial mostrando su evolución histórica y aplicaciones. \url{http://ai.stanford.edu/~nilsson/QAI/qai.pdf}}
}
@inproceedings{berliner1985study,
title={A study of search methods: the effect of constraint satisfaction and adventurousness},
author={Berliner, Hans and Goetsch, Gordon},
booktitle={Proceedings of the 9th international joint conference on Artificial intelligence-Volume 2},
pages={1079--1082},
year={1985},
organization={Morgan Kaufmann Publishers Inc.}
}
@article{burns2010best,
title={Best-first heuristic search for multicore machines},
author={Burns, Ethan and Lemons, Sofia and Ruml, Wheeler and Zhou, Rong},
journal={Journal of Artificial Intelligence Research},
volume={39},
pages={689--743},
year={2010}
}
@article{zhou2006breadth,
title={Breadth-first heuristic search},
author={Zhou, Rong and Hansen, Eric A},
journal={Artificial Intelligence},
volume={170},
number={4},
pages={385--408},
year={2006}
}
@inproceedings{holte2010common,
title={Common misconceptions concerning heuristic search},
author={Holte, Robert C},
booktitle={Third Annual Symposium on Combinatorial Search},
year={2010}
}
@inproceedings{zhang2000depth,
title={Depth-first branch-and-bound versus local search: A case study},
author={Zhang, Weixiong},
booktitle={AAAI/IAAI},
pages={930--935},
year={2000}
}
@inproceedings{zhang1993depth,
title={Depth-first vs. best-first search: New results},
author={Zhang, Weixiong and Korf, Richard E},
booktitle={AAAI},
pages={769--775},
year={1993}
}
@article{korf1990depth,
title={Depth-limited search for real-time problem solving},
author={Korf, Richard E},
journal={Real-Time Systems},
volume={2},
number={1-2},
pages={7--24},
year={1990},
publisher={Springer}
}
@inproceedings{korf2000divide,
title={Divide-and-conquer frontier search applied to optimal sequence alignment},
author={Korf, Richard E and Zhang, Weixiong},
booktitle={AAAI/IAAI},
pages={910--916},
year={2000}
}
@article{reinefeld1994enhanced,
title={Enhanced iterative-deepening search},
author={Reinefeld, Alexander and Marsland, T. Anthony},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume={16},
number={7},
pages={701--710},
year={1994},
publisher={IEEE}
}
@inproceedings{korf1997finding,
title={Finding optimal solutions to Rubik's cube using pattern databases},
author={Korf, Richard E},
booktitle={AAAI/IAAI},
pages={700--705},
year={1997}
}
@inproceedings{korf1996finding,
title={Finding optimal solutions to the twenty-four puzzle},
author={Korf, Richard E and Taylor, Larry A},
booktitle={Proceedings of the national conference on artificial intelligence},
pages={1202--1207},
year={1996}
}
@article{korf2005frontier,
title={Frontier search},
author={Korf, Richard E and Zhang, Weixiong and Thayer, Ignacio and Hohwald, Heath},
journal={Journal of the ACM (JACM)},
volume={52},
number={5},
pages={715--748},
year={2005},
publisher={ACM}
}
@article{gendron1994parallel,
title={Parallel branch-and-branch algorithms: Survey and synthesis},
author={Gendron, Bernard and Crainic, Teodor Gabriel},
journal={Operations research},
volume={42},
number={6},
pages={1042--1066},
year={1994},
publisher={INFORMS}
}
@inproceedings{kaindl1995use,
title={How to use limited memory in heuristic search},
author={Kaindl, Hermann and Kainz, Gerhard and Leeb, Angelika and Smetana, Harald},
booktitle={IJCAI},
pages={236--242},
year={1995}
}
@inproceedings{ochoa2012hyflex,
title={Hyflex: A benchmark framework for cross-domain heuristic search},
author={Ochoa, Gabriela and Hyde, Matthew and Curtois, Tim and Vazquez-Rodriguez, Jose A and Walker, James and Gendreau, Michel and Kendall, Graham and McCollum, Barry and Parkes, Andrew J and Petrovic, Sanja and others},
booktitle={European Conference on Evolutionary Computation in Combinatorial Optimization},
pages={136--147},
year={2012},
organization={Springer}
}
@inproceedings{korf1996improved,
title={Improved limited discrepancy search},
author={Korf, Richard E},
booktitle={AAAI/IAAI, Vol. 1},
pages={286--291},
year={1996}
}
@article{harikumar1996iterative,
title={Iterative deepening multiobjective},
author={Harikumar, S and Kumar, Shashi},
journal={Information Processing Letters},
volume={58},
pages={11--15},
year={1996},
publisher={Elsevier Science}
}
@article{korf1985depth,
title={Depth-first iterative-deepening: An optimal admissible tree search},
author={Korf, Richard E},
journal={Artificial intelligence},
volume={27},
number={1},
pages={97--109},
year={1985},
publisher={Elsevier}
}
@article{korf1993linear,
title={Linear-space best-first search},
author={Korf, Richard E},
journal={Artificial Intelligence},
volume={62},
number={1},
pages={41--78},
year={1993},
publisher={Elsevier}
}
@article{prieditis1993machine,
title={Machine discovery of effective admissible heuristics},
author={Prieditis, Armand E},
journal={Machine learning},
volume={12},
number={1-3},
pages={117--141},
year={1993},
publisher={Springer}
}
@article{holte2006maximizing,
title={Maximizing over multiple pattern databases speeds up heuristic search},
author={Holte, Robert C and Felner, Ariel and Newton, Jack and Meshulam, Ram and Furcy, David},
journal={Artificial Intelligence},
volume={170},
number={16-17},
pages={1123--1136},
year={2006},
publisher={Elsevier}
}
@inproceedings{zhou2002memory,
title={Memory-Bounded A* Graph Search.},
author={Zhou, Rong and Hansen, Eric A},
booktitle={FLAIRS conference},
pages={203--209},
year={2002}
}
@inproceedings{kaindl1994memory,
title={Memory-bounded bidirectional search},
author={Kaindl, Hermann and Khorsand, Aliasghar},
booktitle={AAAI},
pages={1359--1364},
year={1994}
}
@article{culberson1998pattern,
title={Pattern databases},
author={Culberson, Joseph C and Schaeffer, Jonathan},
journal={Computational Intelligence},
volume={14},
number={3},
pages={318--334},
year={1998},
publisher={Wiley Online Library}
}
@article{dillenburg1994perimeter,
title={Perimeter search},
author={Dillenburg, John F and Nelson, Peter C},
journal={Artificial Intelligence},
volume={65},
number={1},
pages={165--178},
year={1994},
publisher={Elsevier}
}
@article{korf2001time,
title={Time complexity of iterative-deepening-A*},
author={Korf, Richard E and Reid, Michael and Edelkamp, Stefan},
journal={Artificial Intelligence},
volume={129},
number={1-2},
pages={199--218},
year={2001},
publisher={Elsevier}
}
@book{rubik1987rubik,
title={Rubik's cubic compendium},
author={Rubik, Ern{\"o}},
volume={3},
year={1987},
publisher={Oxford University Press, USA}
}
@article{mcaleer2018solving,
title={Solving the Rubik's Cube Without Human Knowledge},
author={McAleer, Stephen and Agostinelli, Forest and Shmakov, Alexander and Baldi, Pierre},
journal={arXiv preprint arXiv:1805.07470},
year={2018}
}
@article{arfaee2011learning,
title={Learning heuristic functions for large state spaces.},
author={Arfaee, Shahab Jabbari and Zilles, Sandra and Holte, Robert C},
journal={Artif. Intell.},
volume={175},
number={16-17},
pages={2075--2098},
year={2011},
publisher={Citeseer}
}
@article{kirkpatrick1983optimization,
title={Optimization by simulated annealing},
author={Kirkpatrick, Scott and Gelatt, C Daniel and Vecchi, Mario P},
journal={science},
volume={220},
number={4598},
pages={671--680},
year={1983},
publisher={American Association for the Advancement of Science}
}
@book{russell2009,
title={Artificial intelligence: A modern approach (3rd or 2nd Edition)},
author={Russell, Stuart and Norvig, Peter},
year={2009},
publisher={Prentice Hall},
note ={El libro de referencia del curso. Cubre prácticamente todos los temas a tratar}
}
@book{pearl1984,
title={Heuristics: intelligent search strategies for computer problem solving},
author={Pearl, Judea},
year={1984},
publisher={Addison-Wesley Pub. Co., Inc., Reading, MA},
note ={Cubre el tema de resolución de problemas. Búsquedas informadas}
}
@book{heineman2016algorithms,
title={Algorithms in a nutshell: a practical guide},
author={Heineman, George T and Pollice, Gary and Selkow, Stanley},
year={2016},
publisher={" O'Reilly Media, Inc."}
}
@book{ertel2018introduction,
title={Introduction to artificial intelligence},
author={Ertel, Wolfgang},
year={2018},
publisher={Springer}
}
@book{michalewicz2013solve,
title={How to solve it: modern heuristics},
author={Michalewicz, Zbigniew and Fogel, David B},
year={2013},
publisher={Springer Science \& Business Media}
}
@book{goldberg1989,
title={Genetic algorithms in search, optimization, and machine learning},
author={Goldberg, David Edward and others},
volume={412},
year={1989},
publisher={Addison-wesley Reading Menlo Park},
note = {Cubre el tema de algoritmos genéticos}
}
@book{nilsson2010,
title={The quest for artificial intelligence},
author={Nilsson, Nils J},
year={2010},
publisher={Cambridge University Press},
note = {Discute extensivamente el área de Inteligencia Artificial mostrando su evolución histórica y aplicaciones. \url{http://ai.stanford.edu/~nilsson/QAI/qai.pdf}}
}
@inproceedings{berliner1985study,
title={A study of search methods: the effect of constraint satisfaction and adventurousness},
author={Berliner, Hans and Goetsch, Gordon},
booktitle={Proceedings of the 9th international joint conference on Artificial intelligence-Volume 2},
pages={1079--1082},
year={1985},
organization={Morgan Kaufmann Publishers Inc.}
}
@article{burns2010best,
title={Best-first heuristic search for multicore machines},
author={Burns, Ethan and Lemons, Sofia and Ruml, Wheeler and Zhou, Rong},
journal={Journal of Artificial Intelligence Research},
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year={2006}
}
@inproceedings{holte2010common,
title={Common misconceptions concerning heuristic search},
author={Holte, Robert C},
booktitle={Third Annual Symposium on Combinatorial Search},
year={2010}
}
@inproceedings{zhang2000depth,
title={Depth-first branch-and-bound versus local search: A case study},
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pages={910--916},
year={2000}
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title={Enhanced iterative-deepening search},
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@inproceedings{korf1996finding,
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title={Hyflex: A benchmark framework for cross-domain heuristic search},
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organization={Springer}
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publisher={Elsevier Science}
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title={Depth-first iterative-deepening: An optimal admissible tree search},
author={Korf, Richard E},
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}
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\bibcite{arfaee2011learning}{1}
\bibcite{berliner1985study}{2}
\bibcite{burns2010best}{3}
\bibcite{culberson1998pattern}{4}
\bibcite{dillenburg1994perimeter}{5}
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\bibcite{holte2006maximizing}{12}
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\bibcite{kaindl1994memory}{14}
\bibcite{kirkpatrick1983optimization}{15}
\bibcite{korf1985depth}{16}
\bibcite{korf1990depth}{17}
\bibcite{korf1993linear}{18}
\bibcite{korf1996improved}{19}
\bibcite{korf1997finding}{20}
\bibcite{korf2001time}{21}
\bibcite{korf1996finding}{22}
\bibcite{korf2000divide}{23}
\bibcite{korf2005frontier}{24}
\bibcite{mcaleer2018solving}{25}
\bibcite{michalewicz2013solve}{26}
\bibcite{nilsson2010}{27}
\bibcite{ochoa2012hyflex}{28}
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\bibcite{prieditis1993machine}{30}
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\bibcite{russell2009}{33}
\bibcite{zhang2000depth}{34}
\bibcite{zhang1993depth}{35}
\bibcite{zhou2002memory}{36}
\bibcite{zhou2006breadth}{37}
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\newblock Improved limited discrepancy search.
\newblock In {\em AAAI/IAAI, Vol. 1}, pages 286--291, 1996.
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R.~E. Korf.
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\bibitem{korf2001time}
R.~E. Korf, M.~Reid, and S.~Edelkamp.
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R.~E. Korf and L.~A. Taylor.
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R.~E. Korf, W.~Zhang, I.~Thayer, and H.~Hohwald.
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\newblock In {\em AAAI/IAAI}, pages 930--935, 2000.
\bibitem{zhang1993depth}
W.~Zhang and R.~E. Korf.
\newblock Depth-first vs. best-first search: New results.
\newblock In {\em AAAI}, pages 769--775, 1993.
\bibitem{zhou2002memory}
R.~Zhou and E.~A. Hansen.
\newblock Memory-bounded a* graph search.
\newblock In {\em FLAIRS conference}, pages 203--209, 2002.
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R.~Zhou and E.~A. Hansen.
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\end{thebibliography}
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