LURCH Explanation Conventional algorithms Conventional algorithms for exploring a system's state space are deterministic, in that they have specific decision paths for mapping inputs to outputs. Nondeterministic algorithms, on the other hand, do not have such specific paths, allowing for the same inputs to result in different outputs. Deterministic analysis is often considered safer than nondeterministic methods since it explores all possible system states in an exhaustive and thorough way. Nondeterministic analysis, however, may only explore a subset of the entire state space, and thereby miss some of the possible faults. Nondeterministic analysis methods Decisions on using LURCH Menzies et al. in  argue that LURCH is no less safe than conventional deterministic algorithms for software model analysis; that LURCH is simple, competent, fast, scalable, and a stable nondeterministic analysis method: See also References
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Up and Down the Ladder of Abstraction In real life, you would never use a ladder that only let you go up. Likewise, when creating abstractions, stepping down is as important as stepping up. Here, we take the abstraction from the previous section, and overlay a concrete representation on top of it. That is, we draw the trajectory that represents all time, but we also draw the car at some particular time. How do we select which particular time to show? We could use the slider, which has reappeared. This is a general and powerful technique. Try cranking up the turning rate to 8° or so, and then inspecting the car's behavior as it makes its first two turns. In this case, it's fairly easy to look at the trajectory and imagine the car moving along.
Python Programming Language – Official Website David Dodds Natural Language Processing with Numeracy Competency | bnrebookblog This page of my BNR eBook site is dedicated to my NLP system that I have been working on for over two years. About half a year ago now I decided that I was going to add a Numeracy Competency capability to my NLP system, so that it could handle math concepts used in natural language, technology, and in Science. I was motivated by Jean Piaget’s writings about the conceptualization (growth) in children, and also by a want to be able to have my NLP system read and solve math word problems. Additionally I was motivated to provide the text based system to be able to “see” “read” and otherwise deal with mathematical notation, which has a bunch of symbols not present in ordinary text, and hence not even “visible” to most NLP systems. The articles are copywrited. Adult level information about how to improve any part of the system is received gladly. He wanted to use an entire paragraph of text, sometimes even an entire page, as the input pattern. * The Mirrors Project * blog posting QUOTE from <<