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What is interesting about this chess-learning task is that humans appear to learn such target concepts from just a handful of training examples! In fact, after considering only the single example shown in Figure 111, most people would be willing to suggest a general hypothesis for the target concept, such as "board positions in which the black king and queen are simultaneously attacked," and would not even consider the (equally consistent) hypothesis "board positions in which four white pawns are still in their original locations" How is it that humans can generalize so successfully from just this one example The answer appears to be that people rely heavily on explaining, or analyzing, the training example in terms of their prior knowledge about the legal moves of chess If asked to explain why the training example of Figure 111 is a positive example of "positions in which the queen will be lost in two moves," most people would give an explanation similar to the following: "Because white's knight is attacking both the king and queen, black must move out of check, thereby allowing the knight to capture the queen" The importance of such explanations is that they provide the information needed to rationally generalize from the details of the training example to a correct general hypothesis Features of the training example that are mentioned by the explanation (eg, the position of the white knight, black king, and black queen) are relevant to the target concept and should be included in the general hypothesis In contrast, features of the example that are not mentioned by the explanation (eg, the fact that there are six black pawns on the board) can be assumed to be irrelevant details What exactly is the prior knowledge needed by a learner to construct the explanation in this chess example It is simply knowledge about the legal rules of chess: knowledge of which moves are legal for the knight and other pieces, the fact that players must alternate moves in the game, and the fact that to win the game one player must capture his opponent's king Note that given just this prior knowledge it is possible in principle to calculate the optimal chess move for any board position However, in practice this calculation can be frustratingly complex and despite the fact that we humans ourselves possess this complete, perfect knowledge of chess, we remain unable to play the game optimally As a result, much of human learning in chess (and in other search-intensive problems such as scheduling and planning) involves a long process of uncovering the consequences of our prior knowledge, guided by specific training examples encountered as we play the game This chapter describes learning algorithms that automatically construct and learn from such explanations In the remainder of this section we define more precisely the analytical learning problem The next section presents a particular explanation-based learning algorithm called PROLOG-EBG Subsequent sections then examine the general properties of this algorithm and its relationship to inductive learning algorithms discussed in other chapters The final section describes the application of explanation-based learning to improving performance at large state-space search problems In this chapter we consider the special case in which explanations are generated from prior knowledge that is perfectly correct, as it is for us humans in the above chess example In 12 we consider the more general case of learning when prior knowledge is only approximately correct. excel code 128 barcode macro Install Code 128 Fonts Add-In in Excel - BarCodeWiz
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