Knowledge, hidden in voluminous data repositories routinely created and maintained by todaya (TM)s applications, can be extracted by data mining. The next step is to transform this discovered knowledge into the inference mechanisms or simply the behavior of agents and multi-agent systems. Agent Intelligence Through Data Mining addresses this issue, as well as the arguable challenge of generating intelligence from data while transferring it to a separate, possibly autonomous, software entity. This book contains a methodology, tools and techniques, and several examples of agent-based applications developed with this approach. This volume focuses mainly on the use of data mining for smarter, more efficient agents.
Agent Intelligence Through Data Mining is designed for a professional audience of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science.
Blasting principles for open pit mining deals with both the engineering and the scientific aspects of blasting with special application to open pit mining. Blasting principles has been divided into two volumes. Volume 1 entitled Principles of blast design is intended to introduce the reader to the basic engineering concepts and building blocks which make up a blast design. It consist of ten chapters: a historical perspective; The fragmentation system concept; Explosives as a source of fragmentation energy; Preliminary blast design guidelines; Drilling patterns and hole sequencing; Drop cut design; Bulk blasting agents; Initiation systems; Ground motion, air blast and flyrock; Perimeter blasting. Volume 2 entitled Theoretical foundations is intended to provide the reader additional depth and breadth for better understanding some of the fundamental concepts involved in rock blasting. It consists of 11 chapters: Fundamentals of explosives; Blasting in the absence of a free face; The effect of the shock wave;Attenuation; Spherical charges; Cylindrical charges; Decoupling; Heave; Livingston crater theory; Hydrodynamic-based models; Selected Russian contributions.
Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.
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