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The next design choice is to determine exactly what type of knowledge will be learned and how this will be used by the performance program Let us begin with a checkers-playing program that can generate the legal moves from any board state The program needs only to learn how to choose the best move from among these legal moves This learning task is representative of a large class of tasks for which the legal moves that define some large search space are known a priori, but for which the best search strategy is not known Many optimization problems fall into this class, such as the problems of scheduling and controlling manufacturing processes where the available manufacturing steps are well understood, but the best strategy for sequencing them is not Given this setting where we must learn to choose among the legal moves, the most obvious choice for the type of information to be learned is a program, or function, that chooses the best move for any given board state Let us call this function ChooseMove and use the notation ChooseMove : B -+ M to indicate that this function accepts as input any board from the set of legal board states B and produces as output some move from the set of legal moves M Throughout our discussion of machine learning we will find it useful to reduce the problem of improving performance P at task T to the problem of learning some particular targetfunction such as ChooseMove The choice of the target function will therefore be a key design choice Although ChooseMove is an obvious choice for the target function in our example, this function will turn out to be very difficult to learn given the kind of indirect training experience available to our system An alternative target functionand one that will turn out to be easier to learn in this setting-is an evaluation function that assigns a numerical score to any given board state Let us call this target function V and again use the notation V : B + 8 to denote that V maps any legal board state from the set B to some real value (we use 8 to denote the set of real numbers) We intend for this target function V to assign higher scores to better board states If the system can successfully learn such a target function V , then it can easily use it to select the best move from any current board position This can be accomplished by generating the successor board state produced by every legal move, then using V to choose the best successor state and therefore the best legal move What exactly should be the value of the target function V for any given board state Of course any evaluation function that assigns higher scores to better board states will do Nevertheless, we will find it useful to define one particular target function V among the many that produce optimal play As we shall see, this will make it easier to design a training algorithm Let us therefore define the target value V ( b ) for an arbitrary board state b in B , as follows:. vb.net convert pdf to text file vb . net how to export pdf from datagridview - Stack Overflow
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PDF SDK Sample code: Programmatically create PDF in C#, Visual Basic , ASP . NET , Java, Visual C++. With PDF API. 4 if b is a not a final state in the game, then V(b) = V(bl), where b' is the best final board state that can be achieved starting from b and playing optimally until the end of the game (assuming the opponent plays optimally, as well) HONORS AND ACHIEVEMENTS Stella Florence Pettypiece Memorial Prize in chemistry for outstanding performance in first year chemistry 1992 Clarence Charles Zess Mathematics Prize for Excellence in Calculus II Captain, National Championship Swim Team 1991 All-America, high school swimming 1990, 1991 Deborah Wingert Athletic Scholarship (swimming), University of Tennessee 1992 1994 Figure 15 19. A successful installation! vb.net code to convert pdf to text What is the Acrobat Software Developer Kit? | Adobe Developer ...
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