There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae. Each level of the architecture represents features at a different level of abstraction, defined as a composition of lower-level features. Searching the parameter space of deep architectures is a difficult task, but new algorithms have been discovered and a new sub-area has emerged in the machine learning community since 2006, following these discoveries. Learning algorithms such as those for Deep Belief Networks and other related unsupervised learning algorithms have recently been proposed to train deep architectures, yielding exciting results and beating the state-of-the-art in certain areas. Learning Deep Architectures for AI discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed and discussed, highlighting challenges and suggesting avenues for future explorations in this area.

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Learning Deep Architectures for AI

Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.

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Principles of Model Checking

A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, the subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed. The relation between decision trees and neutral networks (NN) is also discussed. >

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A survey of decision tree classifier methodology

This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are Basic ES algorithms and design principles for variation and selection operators as well as theoretical issues are presented, and future branches of ES research are discussed

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Evolution strategies –A comprehensive introduction

Introduction to the Theory of Boolean Functions and Circuits. The Minimimization of Boolean Functions. The Design of Efficient Circuits for Some Fundamental Functions. Asymptotic Results and Universal Circuits. Lower Bounds on Circuit Complexity. Monotone Circuits. Relations between Circuit Size, Formula Size and Depth. Formula Size. Circuits and other Non-Uniform Computation Methods vs. Turing Machines and other Uniform Computation Models. Hierarchies, Mass Production, and Reductions. Bounded-Depth Circuits. Synchronous, Planar, and Probabilistic Circuits. PRAMs and WRAMs: Parallel Random Access Machines. Branching Programs. References. Index.

http://www.thi.informatik.uni-frankfurt.de/~jukna/boolean/blaues_book.pdf

The Complexity of Boolean Functions

Ordered binary decision diagrams are a useful representation of Boolean functions, if a good variable ordering is known. Variable orderings are computed by heuristic algorithms and then improved with local search and simulated annealing algorithms. This approach is based on the conjecture that the following problem is NP-complete. Given an OBDD G representing f and a size bound s, does there exist an OBDD G* (respecting an arbitrary variable ordering) representing f with at most s nodes? This conjecture is proved.

Improving the variable ordering of OBDDs is NP-complete

Branching Programs and Binary Decision Diagrams

Preface Introduction 1. Introduction 2. BPs and Decision Trees (DTs) 3. Ordered Binary Decision Diagrams (OBDDs) 4. The OBDD Size of Selected Functions 5. The Variable-Ordering Problem 6. Free BDDs (FBDDs) and Read-Once BPs 7. BDDs with Repeated Tests 8. Decision Diagrams (DDs) Based on Other Decomposition Rules 9. Integer-Valued DDs 10. Nondeterministic DDs 11. Randomized BDDs and Algorithms 12. Summary of the Theoretical Results 13. Applications in Verification and Model Checking 14. Further CAD Applications 15. Application in Optimization, Counting, and Genetic Programming Bibliography Index.

Ingo Wegener

Papers

The Complexity of Boolean Functions

Improving the variable ordering of OBDDs is NP-complete

Branching Programs and Binary Decision Diagrams

Branching Programs and Binary Decision Diagrams: Theory and Applications

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