P-log: Probabilistic Reasoning with Answer Set Programming

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P-log Observations and interventions Try, one by one, the following queries: ?{recover=t}|do(drug=t). ?{recover=t}|do(drug=f). ?{recover=t}|obs(drug=t). ?{recover=t}|obs(drug=f). P. Cabalar ( Depto. Computación P-log University of Corunna, SPAIN ) May 9, 2013 24 / 25
P-log Observations and interventions Try, one by one, the following queries: ?{recover=t}|do(drug=t). ?{recover=t}|do(drug=f). ?{recover=t}|obs(drug=t). ?{recover=t}|obs(drug=f). Using causal interventions yields the expected result (we shouldn’t take the drug). Using just observations leads to (what seemed a) paradox. P. Cabalar ( Depto. Computación P-log University of Corunna, SPAIN ) May 9, 2013 24 / 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 and 42: P-log Causal probability statements
Page 43 and 44: P-log Causal probability statements
Page 45 and 46: P-log Observations and intervention
Page 47: P-log Observations and intervention

P-log: Probabilistic Reasoning with Answer Set Programming

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