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Knowledge in Flux presents a theory of rational changes of belief, focusing particularly on revisions that occur when the agent receives new information that is inconsistent with the present epistemic state. It brings together, systematizes, and enlarges upon an already influential body of work by the author and his colleagues on the dynamics of theories and epistemic states. The problem of knowledge representation is one of the most important current research problems in philosophy, artificial intelligence, and cognitive science in general. While most of the research has been devoted to analyzing the structure of epistemic states, this book is unique in describing the dynamics of knowledge and belief, and in presenting models of knowledge that focus on expansions, revisions, and contractions of epistemic states.
The goal of this book is to teach computational scientists and engineers how to develop tailored, flexible, and efficient working environments built from small programs (scripts) written in the easy-to-learn, very high-level language Python. The focus is on examples and applications of relevance to computational science: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping programs with graphical user interfaces; making computational Web services; creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran; and building flexible object-oriented programming interfaces to existing C/C++ or Fortran libraries. In short, scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - under Unix, Windows and MacIntosh.
(3)perhaps the most engaging and creative tour of modern physics that has ever been written (Sunday Times )
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