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Simulated Annealing:
One way to get around the problem of local maxima, and related problems like ridges and plateaux in hill climbing is to allow the agent to go downhill to some extent. In virtual annealing - named just because of an analogy with cooling a liquid until it freezes - the agent chooses to consider a random move. Whether the move improves the evaluation function that it is always carried out. If the move doesn't improve the evaluation function, so the agent will carry out the move with some probability among of 0 and 1. The probability decreases as the move gets worse in terms of the evaluation function, so really bad moves are rarely carried out. This strategy can often nudge a search out of a local maximum and the search can be continue towards the global maximum.
First-Order Models: Here if we proposed first-order logic as a good knowledge representation language than propositional logic is just because there is more expressive than we
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Algorithmic Complexity theory: Moreover a similar situation occurs in broad to specific ILP systems when the inference rules are deductive thus they specialize. So at some sta
What is a disk drive? The electro mechanical mechanism that spins the disk and moves the read/write heads known as disk drive.
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