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Relevant bibliographies by topics / Pursuit-evasion

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Academic literature on the topic 'Pursuit-evasion'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Pursuit-evasion"

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Scott, Allan, and Ulrike Stege. "Parameterized pursuit-evasion games." Theoretical Computer Science 411, no. 43 (October 2010): 3845–58. http://dx.doi.org/10.1016/j.tcs.2010.07.004.

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Feng, Yanghe, Lanruo Dai, Jinwu Gao, and Guangquan Cheng. "Uncertain pursuit-evasion game." Soft Computing 24, no. 4 (December 12, 2018): 2425–29. http://dx.doi.org/10.1007/s00500-018-03689-3.

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ADLER, MICAH, HARALD RCKE, NAVEEN SIVADASAN, CHRISTIAN SOHLER, and BERTHOLD VCKING. "Randomized Pursuit-Evasion in Graphs." Combinatorics, Probability and Computing 12, no. 3 (May 2003): 225–44. http://dx.doi.org/10.1017/s0963548303005625.

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Lehner, Florian. "Pursuit evasion on infinite graphs." Theoretical Computer Science 655 (December 2016): 30–40. http://dx.doi.org/10.1016/j.tcs.2016.04.024.

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Merz, A. W. "Noisy satellite pursuit-evasion guidance." Journal of Guidance, Control, and Dynamics 12, no. 6 (November 1989): 901–5. http://dx.doi.org/10.2514/3.20498.

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Chung, F. R. K., Joel E. Cohen, and R. L. Graham. "Pursuit—Evasion games on graphs." Journal of Graph Theory 12, no. 2 (1988): 159–67. http://dx.doi.org/10.1002/jgt.3190120205.

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Gutman, S., M. Esh, and M. Gefen. "Simple linear pursuit-evasion games." Computers & Mathematics with Applications 13, no. 1-3 (1987): 83–95. http://dx.doi.org/10.1016/0898-1221(87)90095-2.

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Mycielski, J. "Theories of pursuit and evasion." Journal of Optimization Theory and Applications 56, no. 2 (February 1988): 271–84. http://dx.doi.org/10.1007/bf00939412.

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Mycielski, J. "Theories of pursuit and evasion." Journal of Optimization Theory and Applications 61, no. 1 (April 1989): 147. http://dx.doi.org/10.1007/bf00940851.

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Klein, Kyle, and Subhash Suri. "Pursuit Evasion on Polyhedral Surfaces." Algorithmica 73, no. 4 (April 29, 2015): 730–47. http://dx.doi.org/10.1007/s00453-015-9988-7.

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Dissertations / Theses on the topic "Pursuit-evasion"

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Li, Dongxu. "Multi-player pursuit-evasion differential games." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1164738831.

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Prasad, Deepika. "Pursuit Evasion From Multiple Pursuers Using Speed Fluctuation." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1367928486.

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Soares, Ronan Pardo. "Pursuit-evasion games, decompositions and convexity on graphs." Universidade Federal do CearÃ, 2013. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=11105.

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CoordenaÃÃo de AperfeiÃoamento de NÃvel Superior
Esta tese Ã centrada no estudo de propriedades estruturais de grafos cujas compressÃes permitem a concepÃÃo de algoritmos eficientes para resolver problemas de otimizaÃÃo. Estamos particularmente interessados em decomposiÃÃes, em jogos de perseguiÃÃo-evasÃo e em convexidade. O jogo de Processo foi definido como um modelo para a reconfiguraÃÃo de roteamento em redes WDM. Muitas vezes, jogos de perseguiÃÃo-evasÃo, em que uma equipe de agentes tem como objetivo limpar um grafo nÃo direcionado, estÃo intimamente relacionados com decomposiÃÃes em grafos. No caso de grafos direcionados, mostramos que o jogo de Processo Ã monotÃnico e definimos uma nova decomposiÃÃo em grafos equivalente a tal jogo. A partir de entÃo, investigamos outras decomposiÃÃes em grafos. Propomos um algoritmo FPT para calcular vÃrios parÃmetros de largura em grafos. Em particular, este Ã o primeiro algoritmo FPT para calcular a largura em Ãrvore especial e a largura em Ãrvore q-ramificada de um grafo. Em seguida, estudamos um outro jogo perseguiÃÃo-evasÃo que modela problemas de prÃ-obtenÃÃo. NÃs introduzimos uma versÃo mais realista do jogo de VigilÃncia a versÃo on-line. Estudamos a diferenÃa entre o jogo de VigilÃncia clÃssico e suas versÃes conectadas e on-line, fornecendo novos limites para essa diferenÃa. NÃs, entÃo, definimos um modelo geral para o estudo de jogos perseguiÃÃo-evasÃo, com base em tÃcnicas de programaÃÃo linear. Este mÃtodo permite-nos dar os primeiros resultados de aproximaÃÃo para alguns desses jogos. Finalmente, estudamos outro parÃmetro relacionado com a convexidade e a propagaÃÃo da infecÃÃo em redes, o âhull numberâ. NÃs fornecemos vÃrios resultados de complexidade computacional, dependendo das propriedades estruturais do grafo de entrada e usando decomposiÃÃes em grafos. Alguns destes resultados respondem problemas em aberto na literatura.
This thesis focuses on the study of structural properties of graphs whose understanding enables the design of efficient algorithms for solving optimization problems. We are particularly interested in methods of decomposition, pursuit-evasion games and the notion of convexity. The Process game has been defined as a model for the routing reconfiguration problem in WDM networks. Often, such games where a team of searchers have to clear an undirected graph are closely related to graph decompositions. In digraphs, we show that the Process game is monotone and we define a new equivalent digraph decomposition. Then, we further investigate graph decompositions. We propose a unified FPT-algorithm to compute several graph width parameters. This algorithm turns to be the first FPTalgorithm for the special and the q-branched tree-width of a graph. We then study another pursuit-evasion game which models prefetching problems. We introduce the more realistic online variant of the Surveillance game. We investigate the gap between the classical Surveillance Game and its connected and online versions by providing new bounds. We then define a general framework for studying pursuit-evasion games, based on linear programming techniques. This method allows us to give first approximation results for some of these games. Finally, we study another parameter related to graph convexity and to the spreading of infection in networks, namely the hull number. We provide several complexity results depending on the graph structures making use of graph decompositions. Some of these results answer open questions of the literature.

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Thunberg, Johan. "Consensus and Pursuit-Evasion in Nonlinear Multi-Agent Systems." Doctoral thesis, KTH, Optimeringslära och systemteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143658.

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Within the field of multi-agent systems theory, we study the problems of consensus and pursuit-evasion. In our study of the consensus problem, we first provide some theoretical results and then consider the problem of consensus on SO(3) or attitude synchronization. In Chapter 2, for agents with states in R^m, we present two theorems along the lines of Lyapunov’s second method that, under different conditions, guarantee asymptotic state consensus in multi-agent systems where the interconnection topologies are switching. The first theorem is formulated by using the states of the agents in the multi-agent system, whereas the second theorem is formulated by using the pairwise states for pairs of agents in the multi-agent system. In Chapter 3, the problem of consensus on SO(3) for a multi-agent system with directed and switching interconnection topologies is addressed. We provide two different types of kinematic control laws for a broad class of local representations of SO(3). The first control law consists of a weighted sum of pairwise differences between positions of neighboring agents, expressed as coordinates in a local representation. The structure of the control law is well known in the consensus community for being used in systems of agents in the Euclidean space, and here we show that the same type of control law can be used in the context of consensus on SO(3). In a later part of this chapter, based on the kinematic control laws, we introduce torque control laws for a system of rigid bodies in space and show that the system reaches consensus when these control laws are used. Chapter 4 addresses the problem of consensus on SO(3) for networks of uncalibrated cameras. Under the assumption that each agent uses a camera in order to measure its rotation, we prove convergence to the consensus set for two types of kinematic control laws, where only conjugate rotation matrices are available for the agents. In these conjugate rotations, the rotation matrix can be seen as distorted by the (unknown) intrinsic parameters of the camera. For the conjugate rotations we introduce distorted versions of well known local parameterizations of SO(3) and show consensus by using control laws that are similar to the ones in Chapter 3, with the difference that the distorted local representations are used instead. In Chapter 5, we study the output consensus problem for homogeneous systems of agents with linear continuous time-invariant dynamics. We derive control laws that solve the problem, while minimizing a cost functional of the control signal. Instead of considering a fixed communication topology for the system, we derive the optimal control law without any restrictions on the topology. We show that for all linear output controllable homogeneous systems, the optimal control law uses only relative information but requires the connectivity graph to be complete and in general requires measurements of the state errors. We identify cases where the optimal control law is only based on output errors. In Chapter 6, we address the multi-pursuer version of the visibility pursuit-evasion problem in polygonal environments. By discretizing the problem and applying a Mixed Integer Linear Programming (MILP) framework, we are able to address problems requiring so called recontamination and also impose additional constraints, such as connectivity between the pursuers. The proposed MILP formulation is less conservative than solutions based on graph discretizations of the environment, but still somewhat more conservative than the original underlying problem. It is well known that MILPs, as well as multi-pursuer pursuit-evasion problems, are NP-hard. Therefore we apply an iterative Receding Horizon Control (RHC) scheme, where a number of smaller MILPs are solved over shorter planning horizons. The proposed approach is illustrated by a number of solved examples.

QC 20140327

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Gren, Olaf, and Dennis Magnusson. "A Method for Finding Strategies in Pursuit-Evasion Games." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280341.

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Many real-world situations can be described as games over finite graphs, con- sisting of a set of agents performing joint actions affecting the state of the game. One class of games over finite graphs are the so called pursuit-evasion games, where a set of pursuers try to capture an evader on a finite map. In some pursuit-evasion games where the position of the evader is unknown finding an optimal strategy to ensure victory for the pursuers can be difficult. One way to simplify this process is by using the multiplayer knowledge-based subset construction (MKBSC) to transform the game graph to an expanded graph where the pursuers’ knowledge is included in the construction. In this report we investigate the usefulness of MKBSC for finding knowledge-based strate- gies for pursuit-evasion games by analyzing the generated graph by hand and extracting useful information from it. It was found that in general it is difficult to find the best knowledge-based strategies for pursuit-evasion games by hand with a non-symbolic representation of the game. This is mainly due to the fact that the sizes of the expanded graphs tended to be very large. It is pos- sible that MKBSC can be useful for finding knowledge-based strategies for pursuit-evasion games with the use of symbolic representations of the game or by algorithmically finding the strategies based on the generated graphs.
Många situationer kan beskrivas som spel på ändliga grafer bestående av en mängd agenter som utför sammansatta handlingar som påverkar spelets till- stånd. En klass sådana spel är de så kallade jaktflyktspelen, där en mängd jägare försöker fånga en flykting på en ändlig spelplan. I vissa jaktflyktspel där flyktingens position är okänd för jägarna kan det vara svårt att hitta en strategi som försäkrar vinst för jägarna. En metod för att förenkla detta är genom att använda sig av multiplayer knowledge-based subset construction (MKBSC) för att expandera spelgrafen till en expanderad graf som innehåller jägarnas kunskap. I denna rapport undersöker vi användbarheten av MKBSC för att hitta kunskapsbaserade strategier för jaktflyktspel genom att analysera de expanderade graferna för hand och extrahera användbar information från dem. Resultatet var att det generellt sett är svårt att hitta användbara kunskapsbaserade strategier för jaktflyktspel genom att för hand analysera den expanderade grafen med en icke-symbolisk representation av spelet. Detta är huvudsakligen på grund av att storleken på det expanderade spelet tenderar att vara mycket stor. Det är möjligt att MKBSC kan vara användbart för att hitta kunskapsbaserade strategier för jaktflyktspel genom att använda en symbolisk representation av spelet eller genom att söka genom den expanderade grafen med hjälp av algoritmer.

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Pang, Jing-En. "Pursuit-evasion with acceleration, sensing limitation, and electronic counter measures." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1193079487/.

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Jiao, Yue, and Ivan Skvortsov. "An optimization approach to the multi-player pursuit-evasion problem." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210825.

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In this paper a scenario of one evader being chased by multiple pursuers in two specific simulation environments is studied. The simulation environments are divided into an open area without obstacles and a closed area with obstacles. In the open area a fairly accurate system of dynamics are implemented for both pursuers and evader. The Virtual Vehicle Approach is used to provide a reference trajectory for the pursuers to follow in order to catch the evader. The main purpose of this thesis is to find a decentralized robust control method for the dynamics of the pursuers. In the closed area, the line of sight and field of view are introduced and the solution to the Minimum time UGV surveillance problem and the Centroidal Voronoi partitions. Different capturing strategies, encirclement and one-on-one chase, are both studied and compared. The numerical implementation and the resulting simulation are presented and analyzed. Conclusion on the optimal formation for the multiple pursuers is made.

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Hermansson, Richard, and Eric Peldan. "Pursuit and Evasion in Polygonal Environments - A Mixed Integer Linear Programming Approach." Thesis, KTH, Optimeringslära och systemteori, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105763.

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This report addresses improvements to an already existent model of a Pursuit-and-Evasion problem. The model is formulated using Mixed Integer Linear Programming (MILP). The computation time of the original model is first thoroughly examined by solving for increasingly large areas, and with a varying number of pursuers. Some improvements to the model are suggested for shortening the computation time. Finally, a new model is suggested with the aims of being more realistic and to address an issue in the original model that meant that pursuers must not share tiles (i.e they must stay separated at all times).
Denna rapport introducerar förbättringar till en existerande modell av ett Pursuit-And- Evasion problem. Modellen är formulerad med hjälp av Mixed Integer Linear Programming (MILP). Först testas lösningstiden för originalmodellen utförligt på större och större områden, och med olika antal sökare. Några förbättringar föreslås för att förkorta beräkningstiden, och sedan föreslås även en helt ny modell. Syftet med den nya modellen är att den ska vara mer realistisk, och dessutom så försöker den åtgärda ett problem i originalmodellen som gör att sökare inte får stå för nära varandra.

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Grimm, Christopher Lee Jr. "A tensor-train-decomposition-based algorithm for high-dimensional pursuit-evasion games." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105615.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 99-100).
The research presented in this thesis was inspired by an interest in determining feedback strategies for high-dimensional pursuit-evasion games. When a problem is high-dimensional or involves a state space that is defined by several variables, various methods used to solve pursuit-evasion games often require unrealistic computation time. This problem, called the curse of dimensionality, can be mitigated under certain circumstances by utilizing tensor-train (TT) decomposition. By using this intuition, a new algorithm for solving high dimensional pursuit-evasion problems called Best-Response Tensor-Train-decomposition-based Value Iteration (BR-TT-VI) was developed. BR-TT-VI builds on concepts from game theory, dynamic programming (DP), and tensor-train decomposition. By using TT decomposition, BR-TT-VI greatly reduces the effects of the curse of dimensionality. This work culminates in the application of BR-TT-VI to two different pursuit-evasion problems. First, a four-dimensional problem capable of being solved by traditional value iteration(VI) is tackled by the BR-TT-VI algorithm. This problem allows a direct comparison between VI and BR-TT-VI to demonstrate the reduced computational time of the new algorithm. Finally, BR-TT-VI is used to solve a six-dimensional problem involving two Dubins vehicles that is impractical to solve with VI.
by Christopher Lee Grimm Jr.
S.M.

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Phillpot, John. "Line-of-Sight Pursuit and Evasion Games on Polytopes in R^n." Scholarship @ Claremont, 2016. https://scholarship.claremont.edu/hmc_theses/80.

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We study single-pursuer, line-of-sight Pursuit and Evasion games in polytopes in $\mathbb{R}^n$. We develop winning Pursuer strategies for simple classes of polytopes (monotone prisms) in Rn, using proven algorithms for polygons as inspiration and as subroutines. More generally, we show that any Pursuer-win polytope can be extended to a new Pursuer-win polytope in more dimensions. We also show that some more general classes of polytopes (monotone products) do not admit a deterministic winning Pursuer strategy. Though we provide bounds on which polytopes are Pursuer-win, these bounds are not tight. Closing the gap between those polytopes known to be Pursuer-win and those known not to be remains an problem for future researchers.

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Books on the topic "Pursuit-evasion"

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Nahin, Paul J. Chases and escapes: The mathematics of pursuit and evasion. Princeton, NJ: Princeton University Press, 2007.

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Hájek, Otomar. Pursuit games: An introduction to the theory and applications of differential games of pursuit and evasion. Mineola, N.Y: Dover Publications, 2008.

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Office, General Accounting. Tax administration: Reducing delays in the pursuit of tax revenue on closed criminal cases : report to the Joint Committee on Taxation. Washington, D.C: The Office, 1989.

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1935-, Yavin Yaakov, Pachter M, and Rodin Ervin Y. 1932-, eds. Pursuit-evasion differential games. Oxford, England: Pergamon Press, 1987.

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Pursuit-Evasion Differential Games. Elsevier, 1987. http://dx.doi.org/10.1016/c2009-0-07900-8.

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Nahin, Paul J. Chases and Escapes: The Mathematics of Pursuit and Evasion. Princeton University Press, 2012.

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Chases And Escapes The Mathematics Of Pursuit And Evasion. Princeton University Press, 2012.

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Nahin, Paul J. Chases and Escapes: The Mathematics of Pursuit and Evasion. Princeton University Press, 2007.

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Pursuit Games: An Introduction to the Theory and Applications of Differential Games of Pursuit and Evasion. Dover Publications, 2008.

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L, Duke Eugene, and United States. National Aeronautics and Space Administration., eds. Time-optimal aircraft pursuit-evasion with a weapon envelope constraint: Final report. Atlanta, GA: Georgia Institute of Technology, School of Aerospace Engineering, 1990.

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Book chapters on the topic "Pursuit-evasion"

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Patsko, Valerii, Sergey Kumkov, and Varvara Turova. "Pursuit-Evasion Games." In Handbook of Dynamic Game Theory, 1–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-27335-8_30-1.

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Patsko, Valerii, Sergey Kumkov, and Varvara Turova. "Pursuit-Evasion Games." In Handbook of Dynamic Game Theory, 1–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-27335-8_30-2.

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Patsko, Valerii, Sergey Kumkov, and Varvara Turova. "Pursuit-Evasion Games." In Handbook of Dynamic Game Theory, 951–1038. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-44374-4_30.

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Ramachandran, Kandethody M., and Chris P. Tsokos. "Stochastic Linear Pursuit-Evasion Game." In Stochastic Differential Games. Theory and Applications, 25–45. Paris: Atlantis Press, 2012. http://dx.doi.org/10.2991/978-94-91216-47-3_2.

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Klein, Kyle, and Subhash Suri. "Pursuit Evasion on Polyhedral Surfaces." In Algorithms and Computation, 284–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45030-3_27.

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Adler, Micah, Harald Räcke, Naveen Sivadasan, Christian Sohler, and Berthold Vöcking. "Randomized Pursuit-Evasion in Graphs." In Automata, Languages and Programming, 901–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45465-9_77.

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Friedman, Avner. "A mini-max pursuit evasion algorithm." In Mathematics in Industrial Problems, 72–83. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-4129-2_7.

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Klein, Kyle, and Subhash Suri. "Multiagent Pursuit Evasion, or Playing Kabaddi." In Springer Tracts in Advanced Robotics, 89–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17452-0_6.

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Pachter, Meir. "Isaacs’ Two-on-One Pursuit-Evasion Game." In Annals of the International Society of Dynamic Games, 25–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56534-3_2.

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Al-Bluwi, Ibrahim, and Ashraf Elnagar. "Pursuit Evasion in Dynamic Environments with Visibility Constraints." In Intelligent Robotics and Applications, 116–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16587-0_12.

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Conference papers on the topic "Pursuit-evasion"

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Spirakis, P., and B. Tampakas. "Distributed pursuit-evasion." In the thirteenth annual ACM symposium. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/197917.198191.

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MERZ, A. "Noisy satellite pursuit-evasion." In Guidance, Navigation and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1987. http://dx.doi.org/10.2514/6.1987-2319.

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Robin, Cyril, and Simon Lacroix. "Failure anticipation in pursuit-evasion." In Robotics: Science and Systems 2012. Robotics: Science and Systems Foundation, 2012. http://dx.doi.org/10.15607/rss.2012.viii.046.

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Stiffler, Nicholas M., and Jason M. O'Kane. "Pursuit-evasion with fixed beams." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487621.

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M. Vieira, Marcos A., Ramesh Govindan, and Gaurav S.Sukhatme. "Scalable and Practical Pursuit-Evasion." In 2nd International ICST Conference on Robot Communication and Coordination. IEEE, 2009. http://dx.doi.org/10.4108/icst.robocomm2009.5838.

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Guan, Yue, Dipankar Maity, Christopher M. Kroninger, and Panagiotis Tsiotras. "Bounded-Rational Pursuit-Evasion Games." In 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9483152.

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Zhu, Jiagang, Wei Zou, and Zheng Zhu. "Learning Evasion Strategy in Pursuit-Evasion by Deep Q-network." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8546182.

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Sunkara, Vishwamithra, Animesh Chakravarthy, and Debasish Ghose. "Pursuit Evasion Games using Collision Cones." In 2018 AIAA Guidance, Navigation, and Control Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-2108.

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Walrand, Jean, Elijah Polak, and Hoam Chung. "Harbor attack: A pursuit-evasion game." In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2011. http://dx.doi.org/10.1109/allerton.2011.6120357.

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Cheung, Warren, and William Evans. "Pursuit-Evasion Voronoi Diagrams in \ell_1." In 4th International Symposium on Voronoi Diagrams in Science and Engineering (ISVD 2007). IEEE, 2007. http://dx.doi.org/10.1109/isvd.2007.33.

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Reports on the topic "Pursuit-evasion"

1

Rodin, Ervin Y. Artificial Intelligence Methods in Pursuit Evasion Differential Games. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada227366.

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2

Bopardikar, Shaunak D., Francesco Bullo, and Joao P. Hespanha. On Discrete-Time Pursuit-Evasion Games with Sensing Limitations. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada480943.

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