P-log: Probabilistic Reasoning with Answer Set Programming

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P-log Causal probability statements The indiference principle has set all outcomes equally probable, but we can fix probabilities. Suppose that, when you select color white, stones are introduced in an irregular urn so that, due to the stone shapes, the probability of picking the black stone is 1 3 . We add the statement: [r] pr(draw=1|selcolor=white)=1/3. Which is the probability of getting help now when selecting white? and when we select black? P. Cabalar ( Depto. Computación P-log University of Corunna, SPAIN ) May 9, 2013 21 / 25
P-log Causal probability statements The indiference principle has set all outcomes equally probable, but we can fix probabilities. Suppose that, when you select color white, stones are introduced in an irregular urn so that, due to the stone shapes, the probability of picking the black stone is 1 3 . We add the statement: [r] pr(draw=1|selcolor=white)=1/3. Which is the probability of getting help now when selecting white? and when we select black? Compute the probability of picking stone 2 in both cases. When selcolor = white the rest of stones are equally probable (1 − 1/3)/9 = 2/27 = 0.074 P. Cabalar ( Depto. Computación P-log University of Corunna, SPAIN ) May 9, 2013 21 / 25
Page 1 and 2: P-log: Probabilistic Reasoning with
Page 3 and 4: Introduction 1 Introduction 2 P-log
Page 5 and 6: Motivation Introduction Probability
Page 7 and 8: Motivation Introduction Probability
Page 9 and 10: Introduction Example 1. Monty Hall
Page 11 and 12: Introduction Example 1. Monty Hall
Page 13 and 14: Introduction Example 2. Simpson’s
Page 21 and 22: Introduction Probabilistic Reasonin
Page 23 and 24: Introduction Probabilistic models S
Page 25 and 26: Probabilistic models Introduction S
Page 27 and 28: Probabilistic models Introduction I
Page 29 and 30: Introduction Probabilistic models I
Page 31 and 32: P-log P-log P-log [Baral, Gelfond,
Page 37 and 38: P-log The jungle example in P-log %
Page 39 and 40: P-log The jungle example in P-log W
Page 41: P-log Causal probability statements
Page 45 and 46: P-log Observations and intervention

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