By Peter Haddawy (eds.)
This monograph integrates AI and decision-theoretic ways to the illustration of making plans difficulties via constructing a first-order good judgment of time, likelihood, and motion for representing and reasoning approximately plans. The semantics of the good judgment accommodates intuitive houses of time, probability, and motion principal to the making plans challenge. The logical language integrates either modal and probabilistic constructs and permits quantification through the years issues, chance values, and area members. The language can symbolize the opportunity that proof carry and occasions take place at a variety of instances and that activities and different occasions have an effect on the long run. An set of rules for the matter of creating development making plans is constructed and the good judgment is used to end up the set of rules correct.
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Extra info for Representing Plans Under Uncertainty: A Logic of Time, Chance, and Action
4. From r ---+lb infer Ore ---+Dtlb. r --* lb Hypothesis Ot(r --+ lb) NEC Ot(r --+ lb) --+ ( o r e ~ O:lb) I2 O,r ~ O t bl MoPo: 2,3 T h e o r e m 7. Ot(r -+ lb) --+ (<>re -+ <>:lb) 1. 2. 3. 4. o , ( r --, r ~ O,(-~lb --, -,r o,(-,r162 ~ o~-,r o,-1r O,-~lb -~ O,-~r -+ <>re ~ <>tlb O:(r ---, r ~ (<>,r ---, <>tlb) Theorem8. Def of I2 Def of -* and <> Theorem 4:1-3 From r ---+lb infer <>re --+ <>:lb. 1. r ---, lb Hypothesis 2. Q:(r NEC 3. <>:r ---, <>,lb Theorem 7 and MoPo Theorem9. 1. 2. 3. 4. 5.
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Representing Plans Under Uncertainty: A Logic of Time, Chance, and Action by Peter Haddawy (eds.)