逻辑、计算和博弈 (11).pdf
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1、 LOGIC,COMPUTATION AND GAMES Probability&Logic puzzle occurs in many different forms:The Jailer Glasses The Quizmaster Discussion Answer 1:no difference,Answer 2:Door 2 has become more probable,you should switch.The right solution is to switch?Better:analyze what is involved.puzzle occurs in many fo
2、rms:The Jailer,Three Glasses Bayes Law:Two Computations P(Door 2|Door 3)=P(Door 3|Door 2)P(Door 2)/P(Door 3)=(1 x 1/3)/2/3=1/2 P(Door 2|QM opens 3)=P(QM opens 3|Door 2)P(Door 2)/P(QM opens 3)=(1 x 1/3)/1/2=2/3 What is the right computation?Bayes Law does not tell.Tree Representation epistemic model
3、probability tree Issues represent the problem update with the right information only then:compute infer does the text give enough information?role of protocol for the quizmaster what is the update mechanism?what logic fits with this?are the probabilities over-kill,given the qualitative decision prob
4、lem“Switch”?Logic and Probability,Three Approaches combine logical and probabilistic systems (often in practice)find qualitative base logic underneath probability,then derive probability measure(de Finettis program)find probability underneath logic,then derive logical notions as qualitative shorthan
5、d (Locke on belief,several philosophical logicians today)Epistemic Probabilistic Logic,1 models M=(W,R,V)as usual,we will ignore agent indices finite models!Note:Linear Inequalities the linear inequalities in the syntax may look surprising,but they are useful when proving a completeness theorem wher
6、e we have to produce a suitable probability measure.With our later dynamic update logic PDEL with events,another test is that the linear inequality format is just what is needed to make the recursion axioms work.In combined logics of quantifiers and counting(ongoing work)integer programming problems
7、 with inequalities turn out just the right counterpart for basic logical reasoning.Epistemic Probabilistic Logic,2 for simplicity,assume that all worlds in an epistemic equivalence class assign the same probability measure.I.e.,agents know their probability distribution add:s I t Epistemic Probabili
8、stic Logic,3 Theorem (Halpern)Epistemic probability logic is axiomatizable and decidable.The restriction to the rationals may not be essential.Theorem(Tarski)The first-order theory of the reals(R,0,1,+,)is decidable.PS about combining knowledge and probability probability 1 and knowledge:are they th
9、e same,or not?language:iterated knowledge vs.iterated probability Probabilistic PAL,Recursion Axioms PAL update M|on epistemic probability model M defined just as in Week 1,except that the probability measure is normalized to s in M|M,s|=valid recursion law the version on p.148 of LDII mistakenly dr
10、opped need to extend this to conditional probability:HW 3 Theorem Probabilistic epistemic PAL is complete.Aside:PAL and Bayes Law !P()=k looks like P(|)=k but they are not the same Fact Bayes Law fails for the PAL modality see posted LDII book,p.150 Explanation truth values after update for complex
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