Download Chess Metaphors: Artificial Intelligence and the Human Mind by Diego Rasskin-Gutman, Deborah Klosky PDF

By Diego Rasskin-Gutman, Deborah Klosky

After we play the traditional and noble video game of chess, we grapple with rules approximately honesty, deceitfulness, bravery, worry, aggression, attractiveness, and creativity, which echo (or let us leave from) the attitudes we take up our day-by-day lives. Chess is an job within which we set up just about all our to be had cognitive assets; for that reason, it makes a great laboratory for research into the workings of the brain. certainly, learn into synthetic intelligence (AI) has used chess as a version for clever habit because the Fifties. In Chess Metaphors, Diego Rasskin-Gutman explores primary questions about reminiscence, concept, emotion, awareness, and different cognitive techniques throughout the online game of chess, utilizing the strikes of thirty-two items over sixty-four squares to map the structural and useful association of the mind.

Rasskin-Gutman specializes in the cognitive activity of challenge fixing, exploring it from the views of either biology and AI. studying AI researchers' efforts to application a working laptop or computer that may beat a flesh-and-blood grandmaster (and win a global chess championship), he reveals that the consequences fall brief when put next to the actually artistic nature of the human brain.

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Extra resources for Chess Metaphors: Artificial Intelligence and the Human Mind

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The cost function E^^^(wij;J\f, Ce) is local minima free if the network M and the associated learning environment Ce meet the following PR\ (pattern recognition) hypotheses: 1. n{l + 1) < «(/), / = 1 , . . , L — 1 (pyramidal hypothesis). 2. The weight layer matrices W/, / = 1 , . . , L — 1, are full-rank matrices. 3. Ker[A'^]n5f ={0}. Proof Sketch (see [50] for more details). 3, 5i = {X^yy\ = 0 implies ^ i = 0. According to the backpropagation step, yi = 3^/+i W/. 2. Because 3^1 = 0, yi =0 follows by induction on /.

With other modes, the algorithm's behavior becomes difficult to understand only on the basis of the shape of the cost function, although it can still be useful. For example, if we use pattern mode on a large database, a potential problem is that the use of too large learning rates may lead to updating the weights only on the basis of the "recently seen" patterns. This forgetting behavior is not the only problem one has to face. Numerous different problems may emerge from special updating techniques, depending on the choice of the learning parameters.

The next theorem gives a result that only involves the frame structure. THEOREM 8. The cost function ElfJ^^iwj j , w^f, J\f, Ce) has no local minima if the network M and the learning environment Ce satisfy the following hypotheses: • Network The matrix W^ is composed of nonnegative weights. • Learning environment All the frames of Ce are linearly separable into two classes^ depending on the token to which they belong. Proof (SQQ [69]). • Remark 7 (Network architecture). In practice, the assumption on the wj 's sign is not restrictive.

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