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Why is it snowing?

Discover how EMYCIN, an advanced problem-solving system, can help you navigate through daily challenges. From deciding the weather's influence on snow to understanding if Bob causes snow, EMYCIN provides the answers you need.

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Why is it snowing?

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  1. Why is it snowing? EMYCIN solves our daily problems

  2. Description • If someone often wears shorts and sandals, then there is a good chance (.9) that they will cause snow. • If someone often wears shorts and thinks the weather is nice then there is a good chance (.7) that they will plan a barbeque. • If someone plans a barbeque and puts their winter jacket away then there is a good chance (.8) that they will cause snow. • If someone plans a barbeque and seldom wears shorts there is a good chance (.8) that they will not cause snow.

  3. Rules • (e1) wearsSandals(X, often)  wearsShorts(X, often) 1 causes(X, snow) • I(e1)=.9 • (e2) wearsShorts(X, often)  thinksWeather(X, nice) 2 plans(X, barbeque) • I(e2)=.7 • (e3) plans(X, barbeque)  putsAwayJacket(X, likely) 3 causes(X, snow) • I(e3)=.8 • (e4) plans(X, barbeque)  wearsShorts(X, Seldom) 4causes(X, snow) • I(wearsSandals(Bob, often))=.6 • I(wearsShorts(Bob, often))=.8 • I(thinksWeather(Bob, nice))=.9 • I(putsAwayJaxket(Bob, likely))=.8 • I(wearsShorts(Bob, seldom)) = .2

  4. What to do? • We want to know if Bob causes snow • causes(Bob, snow) • So we need to know the Measure of Belief (MB) and the Measure of Disbelief (MD) • To calcualte both of these we need to know the MB and MD of plans(Bob, barbeque) to calculate I(plans(Bob, barbeque))

  5. Barbeque • MD(plans(Bob, barbeque)) • In this case no rules produce  plans(Bob, barbeque) • MB(plans(Bob, barbeque)) • MB(plans(Bob, barbeque), e) = MB(plans(Bob, barbeque), {e2}) = I({e2}) * max(0, min(I(wearsShorts(Bob, often)), I(thinksWeather(Bob, nice)))) = .7 * max(0, min(.8, .7)) = .7 * .7 = .49 • I(plans(Bob, barbeque)) = MB – MD = .49 – 0 =.49

  6. causes(Bob, Snow) • MD • MD(causes(Bob, snow), e) = MD(causes(Bob, snow), {e4}) = I({e4}) * max(0, min(I(plans(Bob, barbeque)), I(wearsShorts(Bob, seldom)))) = .8 * max(0, min(.49, .2)) = 0.16

  7. causes(Bob, Snow) • MB • MB(causes(Bob, Snow), {e1}) = I({e1}) * max(O, min(I(wearsSandals(Bob, often)), I(wearsShorts(Bob, often)))) = .9 * max(0, (.6, .8) = .54 • MB(causes(Bob, Snow), {e3}) = I({e3}) * max(0, min(I(plans(Bob, barbeque)), I(putsAwayJacket(Bob, likely)))) = .8 * max(0, min(.49, .54)) = .39

  8. causes(Bob, Snow) • MB(causes(Bob, snow), {e1, e3}) = MB(causes(Bob, snow), {e1} + MB(causes(Bob, snow), {e3})*(1-MB(causes(Bob, snow), {e1})) =.54 + .49 * .46 = .77

  9. I(causes(Bob, Snow) • I(causes(Bob, Snow)) = MB – MD = .77 - .16 = .61

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