Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Preface * Introduction * The Bayes Error * Inequalities and alternatedistance measures * Linear discrimination * Nearest neighbor rules *Consistency * Slow rates of convergence Error estimation * The regularhistogram rule * Kernel rules Consistency of the k-nearest neighborrule * Vapnik-Chervonenkis theory * Combinatorial aspects of Vapnik-Chervonenkis theory * Lower bounds for empirical classifier selection* The maximum likelihood principle * Parametric classification *Generalized linear discrimination * Complexity regularization *Condensed and edited nearest neighbor rules * Tree classifiers * Data-dependent partitioning * Splitting the data * The resubstitutionestimate * Deleted estimates of the error probability * Automatickernel rules * Automatic nearest neighbor rules * Hypercubes anddiscrete spaces * Epsilon entropy and totally bounded sets * Uniformlaws of large numbers * Neural networks * Other error estimates *Feature extraction * Appendix * Notation * References * Index

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A Probabilistic Theory of Pattern Recognition

This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective "unsupervised" is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM) algorithm, it does not require careful initialization. The proposed method also avoids another drawback of EM for mixture fitting: the possibility of convergence toward a singular estimate at the boundary of the parameter space. The novelty of our approach is that we do not use a model selection criterion to choose one among a set of preestimated candidate models; instead, we seamlessly integrate estimation and model selection in a single algorithm. Our technique can be applied to any type of parametric mixture model for which it is possible to write an EM algorithm; in this paper, we illustrate it with experiments involving Gaussian mixtures. These experiments testify for the good performance of our approach.

Unsupervised learning of finite mixture models

During the past decade, interest has grown tremendously in the design and synthesis of crystalline materials constructed from molecular clusters linked by extended groups of atoms. Most notable are metal-organic frameworks (MOFs), in which polyatomic inorganic metal-containing clusters are joined by polytopic linkers. (Although these materials are sometimes referred to as coordination polymers, we prefer to differentiate them, because MOFs are based on strong linkages that yield robust frameworks.) The realization that MOFs could be designed and synthesized in a rational way from molecular building blocks led to the emergence of a discipline that we call reticular chemistry. MOFs can be represented as a special kind of graph called a periodic net. Such descriptions date back to the earliest crystallographic studies but have become much more common recently because thousands of new structures and hundreds of underlying nets have been reported. In the simplest cases (e.g., the structure of diamond), the atoms in the crystal become the vertices of the net, and bonds are the links (edges) that connect them. In the case of MOFs, polyatomic groups act as the vertices and edges of the net. Because of the explosive growth in this area, a need has arisen for a universal system of nomenclature, classification, identification, and retrieval of these topological structures. We have developed a system of symbols for the identification of three periodic nets of interest, and this system is now in wide use. In this Account, we explain the underlying methodology of assigning symbols and describe the Reticular Chemistry Structure Resource (RCSR), in which about 1600 such nets are collected and illustrated in a database that can be searched by symbol, name, keywords, and attributes. The resource also contains searchable data for polyhedra and layers. The database entries come from systematic enumerations or from known chemical compounds or both. In the latter case, references to occurrences are provided. We describe some crystallographic, topological, and other attributes of nets and explain how they are reported in the database. We also describe how the database can be used as a tool for the design and structural analysis of new materials. Associated with each net is a natural tiling, which is a natural partition of space into space-filling tiles. The database allows export of data that can be used to analyze and illustrate such tilings.

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The Reticular Chemistry Structure Resource (RCSR) Database of, and Symbols for, Crystal Nets

Deconstructing the Crystal Structures of Metal Organic Frameworks and Related Materials into Their Underlying Nets Michael O’Keeffe* and Omar M. Yaghi* Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287, United States Center for Reticular Chemistry, Center for Global Mentoring, Department of Chemistry and Biochemistry, University of California Los Angeles, 607 Charles E. Young Dr. East, Los Angeles, California 90095, United States Graduate School of EEWS, Korea Advanced Institute of Science and Technology, Daejeon, Korea

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Werner Fischer

Papers

Sphere Packings, Lattices and Groups

Existenzbedingungen homogener Kugelpackungen zu kubischen Gitterkomplexen mit weniger als drei Freiheitsgraden

Geometrical packing analysis of molecular compounds

On 3-periodic minimal surfaces

Existenzbedingungen homogener Kugelpackungen in Raumgruppen tetragonaler Symmetrie