Data Mining Practical Machine Learning Tools and Techniques

An Introduction to MultiAgent Systems.

Unsupervised real-time anomaly detection for streaming data

'Satisfiability (SAT) related topics have attracted researchers from various disciplines: logic, applied areas such as planning, scheduling, operations research and combinatorial optimization, but also theoretical issues on the theme of complexity and much more, they all are connected through SAT. My personal interest in SAT stems from actual solving: The increase in power of modern SAT solvers over the past 15 years has been phenomenal. It has become the key enabling technology in automated verification of both computer hardware and software' - Edmund M. Clarke (FORE Systems University Professor of Computer Science and Professor of Electrical and Computer Engineering at Carnegie Mellon University). 'Bounded Model Checking (BMC) of computer hardware is now probably the most widely used model checking technique. The counterexamples that it finds are just satisfying instances of a Boolean formula obtained by unwinding to some fixed depth a sequential circuit and its specification in linear temporal logic. Extending model checking to software verification is a much more difficult problem on the frontier of current research. One promising approach for languages like C with finite word-length integers is to use the same idea as in BMC but with a decision procedure for the theory of bit-vectors instead of SAT. All decision procedures for bit-vectors that I am familiar with ultimately make use of a fast SAT solver to handle complex formulas' - Edmund M. Clarke (FORE Systems University Professor of Computer Science and Professor of Electrical and Computer Engineering at Carnegie Mellon University). 'Decision procedures for more complicated theories, like linear real and integer arithmetic, are also used in program verification. Most of them use powerful SAT solvers in an essential way. Clearly, efficient SAT solving is a key technology for 21st century computer science. I expect this collection of papers on all theoretical and practical aspects of SAT solving will be extremely useful to both students and researchers and will lead to many further advances in the field' - Edmund M. Clarke (FORE Systems University Professor of Computer Science and Professor of Electrical and Computer Engineering at Carnegie Mellon University).

/pdf/handbook-of-satisfiability-2dsvkz551d.pdf

Handbook of Satisfiability

Advances in mathematics

We consider the minimal unsatisfiability problem for propositional formulas over n variables with n+k clauses for fixed k. We will show that in case of at most n clauses no formula is minimal unsatisfiable. For n+1 clauses the minimal unsatisfiability problem is solvable in quadratic time. Further, we present a characterization of minimal unsatisfiable formulas with n+1 clauses in terms of a certain form of matrices.

An efficient algorithm for the minimal unsatisfiability problem for a subclass of CNF

A tailored model of a system is the prerequisite for various analysis tasks, such as anomaly detection, fault identification, or quality assurance. This paper deals with the algorithmic learning of a system's behavior model given a sample of observations. In particular, we consider real-world production plants where the learned model must capture timing behavior, dependencies between system variables, as well as mode switches--in short: hybrid system's characteristics. Usually, such model formation tasks are solved by human engineers, entailing the well-known bunch of problems including knowledge acquisition, development cost, or lack of experience.

Our contributions to the outlined field are as follows. (1) We present a taxonomy of learning problems related to model formation tasks. As a result, an important open learning problem for the domain of production system is identified: The learning of hybrid timed automata. (2) For this class of models, the learning algorithm HyBUTLA is presented. This algorithm is the first of its kind to solve the underlying model formation problem at scalable precision. (3) We present two case studies that illustrate the usability of this approach in realistic settings. (4) We give a proof for the learning and runtime properties of HyBUTLA.

/pdf/learning-behavior-models-for-hybrid-timed-systems-5fezbl6r14.pdf

Learning behavior models for hybrid timed systems

Theory of Quantified Boolean Formulas.

On subclasses of minimal unsatisfiable formulas

We present a new approach for preprocessing Quantified Boolean Formulas (QBF) in conjunctive normal form (CNF) by expanding a selection of universally quantified variables with bounded expansion costs. We describe a suitable selection strategy which exploits locality of universals and combines cost estimates with goal orientation by taking into account unit literals which might be obtained.

Furthermore, we investigate how Q-resolution can be integrated into this method. In particular, resolution is applied specifically to reduce the amount of copying necessary for universal expansion.

Experimental results demonstrate that our preprocessing can successfully improve the performance of state-of-the-art QBF solvers on wellknown problems from the QBFLIB collection.

/pdf/bounded-universal-expansion-for-preprocessing-qbf-1laa1i3kj5.pdf

Hans Kleine Büning

Papers

An efficient algorithm for the minimal unsatisfiability problem for a subclass of CNF

Learning behavior models for hybrid timed systems

Theory of Quantified Boolean Formulas.

On subclasses of minimal unsatisfiable formulas

Bounded universal expansion for preprocessing QBF