A note on assumptions about skolem functions
价值还是自由——康德定言命令式的基础性问题

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察这些价值能否成为康德伦理学的基础①’
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② 艾伦i伍德和保罗i盖耶强调康德坚持道德基本原则是“不可论证的”,都不同意对道德的基本物质价值(最终目的)进 行任何形式的论证'伍德通过设定绝对价值发展出选择实质论'同样,盖耶认为,康德将设定和追求自己选择目的的能力视为自
的基本 形式, 将 本身 为 有 价值'
③ WatRi+c、FitzpatricR、La+gO+、Her*a+-KweriRc都支持对康德哲学进行再次深入文本解读,其中Heenan明确否认存在某种
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On a Problem in Formal Logic(拉姆齐)

F . P. KAMSEY
[Dec.
13,
ON A PBOBLEM OF FOKMAL LOGIC
By F. P. KAMSEY. [Received 28 November, 1928.—Read 13 December, 1928.]
This paper is primarily concerned with a special case of one of the leading problems of mathematical logic, the problem of finding a regular procedure to determine the truth or falsity of any given logical formula*. But in the course of this investigation it is necessary to use certain theorems on combinations which have an independent interest and are most conveniently set out by themselves beforehand. I. The theorems which we actually require concern finite classes only, but we shall begin with a similar theorem about infinite classes which is easier to prove and gives a simple example of the method of argument.
• Called iu German the Entscheidungsproblem; see Hilbert und Ack9rmann, Grundzilge def Theoretischen Logik, 72-81.
Lecture_2

Economics2010a Fall2003Edward L.Glaeser Lecture22.Choice and Utility Functionsa.Choice in Consumer Demand Theory and Walrasian Demandb.Properties of demand from continuity and properties from WARPc.Representing Preferences with a Utility Functiond.Demand as Derived from Utility Maximizatione.Application:Fertilityx i denotes commodities,continuous numbersx x 1,x 2,....x Lvector of discretecommodities p p 1,p 2,....p L vector of prices w wealth available to be spentThe budget constraint p x i 1Lp i x iwMWG Definition2.D.1The Walrasian Budget SetB p,w x L:p x wis the set of all feasible consumption bundles for the consumer faces market prices p and has wealth w.Note:We will be treating all prices and consumption levels as being weakly positive.Prices are treated as exogenous–as they will be in the production case.While neither consumer nor producer chooses prices(generally)prices are the extra parameter in each side’s problem that ensures that demand and supply are equal.Non-linear prices are certainly possible(example2.D.4).The Walrasian Demand Function is the set C B p,w which is defined for all p,w ,or at least for a full dimensional subsetp,w L 1We generally assume that C B p,w has a single element(for convenience)but it doesn’t need to.We writeC B p,w x p,w x1 p,w ,...x L p,wWe will also generally assume that demand is continuous and differentiable. MWG Definition2.E.1:The Walrasian Demand Function ishomogeneous of degree zero ifx p, w x p,w for any p,w and 0.This property follows from the fact that choice is only a function of the budget set and B p,w x L:p x w is the same set asB p, w x L: p x wThis fairly obvious claim is in many ways the underlying intellectual basis for the economic bias that the price level doesn’t matter.Differentiating x p, w x p,w totally with respect to gives us the following equation:i 1L x k p ,w p i p i x k p ,w ww 0 i 1L x k p ,w p i p i x k x k p ,w w w x k i 1L pi k w k 0This tells you that for any commodity,the sum of own and cross price elasticities equals -1times the income elasticity.MWG Definition2.E.2Walras’Law:The Walrasian Demand correspondence x p,w satisfies Walras’law if for every p 0and w 0,we have p x w for all x x p,w .This just says that the consumer spends all of his wealth.Looking ahead,Walras’law will come about as long as consumers are not “satiated”in at least one of the goods.Walras’Law and differentiability give us two convenient equalities.Differentiating p x w with respect to w yields:i 1Lp i x i p ,w w 1or manipulating this slightly yields:i 1L x i p ,w w w x i p i x i w i 1Lw i i0,where i p i x i w ,the budget share of good i .This means that income elasticities (when weighted by budget shares)sum to one.All goods can’t be luxuries,etc.Sometimes this is known as Engel aggregation (income effects are after all drawn with Engel curves).Differentiating p x w with respect to p k yields:i 1Lp i x i p ,wp k x k 0or multiplying the whole expression by p k w : i 1L x i p ,w p k p k x i p i x i w p k x k w i 1Lp k ii k0,where again i p i x i wThis means that cross price elasticities sum to-1times the budget share of the relevant good.Overall,these elasticities have to sum to a negative number.MWG Definition2.F.1The Walrasian Demand function satisfies the weak axiom of revealed preference if the following property holds for any two price-wealth situations p,w and p ,w : If p x p ,w w and x p ,w x p,wthen p x p,w w .In words–if the goods that are chosen with budget set(a)are affordable at budget set (b),and not the same as the goods that are chosen at budget set(b),then the goods that are chosen at budget set(b) are not affordable at budget set(a).Just like in the last lecture,WARP meansthat if bundle(b)is preferred to bundle(a) in one setting,it will be preferred in all other settings.A property that follows from WARP:price changes that are fully income compensated make consumers weakly better off.Take any p,w and let p p p Compensate the consumer with an income change so that the old bundle is exactly affordable at the new prices,i.e.:w w w x p,w pThe consumer’s new consumption level atp ,w satisfies the WARP condition:p x p,w w ,which then implies thatp x p ,w w,and this holds with strict equality whenx p ,w x p,wGiven that the consumer could have chosen the old bundle,the consumer must have weakly preferred the new bundle.MWG Proposition2.F.1:WARP implies the law of compensated demand.If the Walrasian demand function x p,w satisfies Walras’Law and is homogeneous of degree zero,then x p,w satisfies the weak axiom if and only if the following property holds.For any compensated price change from an initial situation p,w to a new price wealth pair p ,w p ,p x p,x ,then p p x p ,w x p,x 0,and this equality holds strictly whenx p ,w x p,x .We are now interested only in the weakaxiom- law of demand part of the proposition.First,note that if x p ,w x p,w we’re done,so consideronly the case where x p ,w x p,w . In that case,Walras’law gives us:p x p ,w w ,and we havedefined w so that w p x p,w Together,this gives us thatp x p ,w x p,w 0But we also know that as p ,w satisfies theWARP condition:p x p,w w andx p ,w x p,wit follows that p x p ,w w.Using p x p,w w(again by Walras’Law),this gives us that:p x p ,w x p,w 0Subtraction then yields:p p x p ,w x p,w 0 Change in prices times change in quantities is negative.Since p x 0for all compensated price changes,this also holds in the limit for very small price changes and dp dx 0.We can use matrix notation and write:dx D p x p ,w dp D w x p ,w dwwhich just means:dxi 1L x 1 p i dp i x 1 w dw ,.., i 1L x L p i dp i x L w ordx D p x p ,w dp D w x p ,w x p ,w dp ordxD p x p ,w D w x p ,w x p ,wT dpThe D p x p ,w D w x p ,w x p ,wTterm is a matrix where the element in row i ,column j of the matrix isx ip k x i w xkx 1 p 1 x 1 w x 1....x 1 p L x 1w x L.............x L p 1 x L w x 1.... x L p L x Lw xLand thendpD p x p ,w D w x p ,w x p ,wT dp 0The term in brackets is the Slutsky matrix.MWG Proposition2.F.2:If a differentiable Walrasian demand function x p,w satisfies Walras’law, homogeneity of degree zero and WARP, then at any p,w the Slutsky matrix is negative semi-definite,i.e.v Sv 0for any v L.MWG Proposition2.F.3:Suppose that the Walrasian demand function x p,w is differentiably homogeneous of degree zero,and satisfies Walras’Law.Then p S p,w 0 and S p,w p 0for any p,wUtility Functions–Finally getting to the basic tool of99%ofeconomics.Back to preferences.We want fourattributes:(1)completeness and(2)transitivityThese were defined in lecture1and we refer to preferences with these attributes as being rational.Definition3.B.2.:The preference relation on X is monotone if x,y X,and y x impliesy x.It is strongly monotone if y x and y x implies y x.In many cases,we won’t naturally have monotonicity,but then a little redefinition of variables does the trick(turn a bad into a good by multiplying by-1).A slight twist(I mentioned this earlier):Definition3.B.3:The preference relation on Xis locally nonsatiated if for every x,y X,and every0,there exists a ysuch that y x and y x.This means that there exists a y vector that is arbitrarily close to x that is strictly preferred to x.One last property:Definition 3.C.1.:The preference relation on X is continuous if it is preserved under limits.That is,for any sequence of pairs x n ,y n n 1 with x n y n for all n ,x lim n x n and y lim n y n we have x y .The famous counterexample islexicographic preferences,where more of good 2is preferred to less,but unless the bundles have thesame amount good 1,then the bundle with more good 1is always preferred.Consider sequence 1,with no units of good 2and 1/n units of good 1,and sequence 2with no units of good 1,and1 1/n units of good2.For all finite n,sequence1is preferred to sequence1,but in the limit sequence1 yields zero unit of either good and sequence2yields1unit of good2and is therefore preferred.Lexicographic preferences are a famous example,but hardly amainstay of either theory or empirical work.MWG Definition1.B.2:A functionu:X is a utility function representing preference relation if for all x,y X, x y if and only if u x u y Proposition3.C.1:If the rational preference relation on L is continuous, then there is a continuous utility functionu x that represents .The proof in MWG requires monotonicity–a slight variant is toto take a probability measure on L that has positive density everywhere,and then letu x 1 prob y y x .By construction,if x y then u x u y and if u x u y then x y.Now we’ve gotten to a utility function and we know that it is continuous.MWG Definition3.B.4:The preference relation is convex if for every x X the upper contour sety Y:y x is convex,that is if y x and z x then y 1 z x for every 0,1 .MWG Definition3.B.5:The preference relation is strictly convex if for every x X if y x and z x andy z implies that y 1 z x for every 0,1 .Convex preferences imply that u . is quasi-concave,i.e.the sety L:u y u xis concave(equivalentlyu y 1 x Min u x ,u y for any x,y and all 0,1 .Strict convexity implies strictquasi-concavity.The utility maximization problem is to maximize u x subject to theconstraint p x 0.MWG Proposition3.D.1:If p 0and u . is continuous then the utility maximization problem has a solution. The proof relies on the fact that a continuous function always has a maximum on a compact set,so you just need to show that the budget set is closed and bounded,pact.Proposition3.D.2:Suppose that u . is a continuous utility functionrepresenting a locally non-satiated preference relation defined on the consumption set X L.Then the Walrasian demand correspondence has the following properties:(1)x p,w is homogeneous of degree zero in prices and wealth,i.e.x p,w x p, w for every 0(2)Walras’law p x p,w w(3)If is convex so that u . isquasi-concave,then x p,w is a convex set.Moreover if is strictly convex,so that u . is strictly quasi-concave,then x p,w consists of a single element.In practice,we write down a Langrangianmax x, U x1,x2,...x Lwi 1Lp i xiwhich yields us a system of first order conditions:U x1,x2,...x Lx ip iThese are L equations and we have L 1 unknowns,so we need to use the budget set as well to solve the problem.An application:fertility decisions.Many empirical puzzles:(1)Why does fertility drop with income so substantially across countries?(2)Why is fertility below replacement in all of Europe,but not in the U.S.?How would we capture this:Begin with U C,N –utility over consumption and kids.Assume that kids have both a cash cost(k),and a time cost(t)Assume that you have a time budget that can either be used producing kids or making money.Write down the total budget constraintWT Y C k tW NSolving C from the budget set the agent’s maximization problem reduces toU WT Y k tW N,NmaxNWhich in turn produces a F.O.C.k tW U1 WT Y k tW N,NU2 WT Y k tW N,N 0 where U1is the partial w.r.t.the1st argumentof U and similarly for U2.The marginal utility of another kid is weighed off against the marginal cost in terms of time and money.Comparative statics can be derived by (1)Using the implicit function theorem to define N Z ,where Z is a vectorrepresenting all of the parameters in this equation(i.e.,Z t,T,Y,W,k )and(2)Totally differentiating the F.O.C.to findNZ.LetF N,Z k tW U1 WT Y k tW N,NU2 WT Y k tW N,Nso that the F.O.C is F N Z ,Z 0.Differentiation w.r.t parameter Z then gives us:F1 N Z ,Z NZF2 N Z ,Z 0 from whichN Z F2 NZ ,ZF1 N Z ,Z.In this caseF1 N ,Z k tW 2U11 C ,N U22 C ,NU22 C ,N 2 k tW U12 C ,N and C WT Y k tW N .We generally assume that terms likeF1 N Z ,Z are negative(Why?)so thesign of Ndepends entirely on the sign ofZF2 N ,Z F N ,Z.ZIn the case of Z Y for example:Y k tW U1 WT Y k tW N ,N U2 WT Y k tW N ,Nk tW U11 WT Y k tW N ,N U12 WT Y k tW N ,NWhat can we say about this?Does this give us any intuition about anything?In the case of W:W k tW U1 WT Y k tW N ,N U2 WT Y k tW N ,Nk tW T tN U11 WT Y k tW N ,N T tN U12 WT Y k tW N ,NtU1 WT Y k tW N ,NIt’s the same as the unearned incomeeffect expect for the third term.What doesthat third term represent?Now let’s get a little more fancy and assume that parents care about both quantity and quantity,i.e.U C,Q,N .For simplicity assume utility is a separableand quasi-linear in consumption,soU C,Q,N C V Q,Z Q W N,Z N . Assume children cost WtQN.Then the first order conditions are:V Q,Z QWtNQandW N,Z NWtQNIf a parameter increases the marginal return to quality–this will have the following effect2V Q,Z QQ2 QZ Q2V Q,Z QQ Z QWt NZ Qand2W N,Z NN2 NZ QWt QZ Qor QZ Q2V Q,Z QQ Z Q2V Q,ZQQ2Wt 22W N,Z NN2and NZ Q2V Q,Z QQ Z Q2V Q,ZQQ22W N,Z NN2Wt 2How can we sign these two things?Each equation also tells you that the price per child rises as the quality of each child increases,and the price of quality rises as the number of children rises.Through the budget set there is an inherent substitutability between quantity and quality of children.。
第三章 谓词逻辑与归结原理

以正向推理所得结果作为假设进 行反向推理
退出
是 还需要正向推理吗?
否
2014-4-9
18
华北电力大学
概述-推理的控制策略
搜索策略
推理时,要反复用到知识库中的规则,而知识库中 的规则又很多,这样就存在着如何在知识库中寻找 可用规则的问题 为有效控制规则的选取,可以采用各种搜索策略 常用搜索策略:
归结推理方法在人工智能推理方法中有着很重 要的历史地位,是机器定理证明的主要方法
2014-4-9
25
华北电力大学
归结法的特点
归结法是一阶逻辑中,至今为止的最有效的半可 判定的算法。也是最适合计算机进行推理的逻辑 演算方法 半可判定 一阶逻辑中任意恒真公式,使用归结原理,总 可以在有限步内给以判定(证明其为永真式) 当不知道该公式是否为恒真时,使用归结原理 不能得到任何结论
(5) 上下文限制
上下文限制就是把产生式规则按它们所描述的上下文分组,在某种 上下文条件下,只能从与其相对应的那组规则中选择可应用的规则
2014-4-9
22
华北电力大学
概述-推理的控制策略
推理的控制策略
3.冲突解决策略
(6) 按匹配度排序
在不精确匹配中,为了确定两个知识模式是否可以进行匹配,需要 计算这两个模式的相似程度,当其相似度达到某个预先规定的值时,就 认为它们是可匹配的。若有几条规则均可匹配成功,则可根据它们的匹 配度来决定哪一个产生式规则可优先被应用
如专家系统、智能机器人、模式识别、自然语言理解等
推理
按照某种策略从已有事实和知识推出结论的过程。 推理是由程序实现的,
称为推理机
医疗诊断专家系统
• 知识库中存储经验及医学常识 • 数据库中存放病人的症状、化验结果等初始事实 • 利用知识库中的知识及一定的控制策略,为病人诊治疾病、开出医疗处方就 是推理过程
Combinatorial Nullstellensatz

h i gi .
s∈Si (xi
In the special case m = n, where each gi is a univariate polynomial of the form stronger conclusion holds, as follows.
− s), a
Theorem 1.1 Let F be an arbitrary field, and let f = f (x1 , . . . , xn ) be a polynomial in F [x1 , . . . , xn ]. Let S1 , . . . , Sn be nonempty subsets of F and define gi2 , . . . , sn ∈ Sn so that
In this paper we prove these two theorems, which may be called Combinatorial Nullstellensatz, and describe several combinatorial applications of them. After presenting the (simple) proofs of the above theorems in Section 2, we show, in Section 3 that the classical theorem of Chevalley and Warning on roots of systems of polynomials as well as the basic theorem of Cauchy and Davenport on the addition of residue classes follow as simple consequences. We proceed to describe additional applications in Additive Number Theory and in Graph Theory and Combinatorics in Sections 4,5,6,7 and 8. Many of these applications are known results, proved here in a unified way, and some are new. There are several known results that assert that a combinatorial structure satisfies certain combinatorial property if and only if an appropriate polynomial associated with it lies in a properly defined ideal. In Section 9 we apply our technique and obtain several new results of this form. The final Section 10 contains some concluding remarks and open problems.
remsey定理

De nition 1.1 For X N, let X ]n denote the size n subsets of X . Sup-
pose that n and m are positive integers and F is a function from N]n to f0; : : :; m ? 1g. We say that H N is homogeneous for F if F is constant on H ]n .
2 Analysis by recursion theoretic complexity.
In this section, we prove Seetapun's theorem and answer Question 1.4. The proof that we give is due to Jockusch, which is an improved version of Seetapun's original proof.
1.2 Fragments of second order arithmetic
In Section 3, we analyze Ramsey's Theorem as a formal statement within second order arithmetic. To review, P ? + I 0 states the algebraic properties 1 of addition and multiplication and the scheme that every set that is de ned by a 0 formula, contains 0 and is closed under the successor function con1 tains every natural number. Primarily, the second order systems which will concern us are RCA0 , P ? + I 0 with the scheme for recursive comprehen1 sion; WKL0, RCA0 with the statement that every in nite binary tree has an in nite path; and ACA0 , RCA0 with the scheme for arithmetic comprehension. A detailed discussion of these systems can be found in (Friedman 1975). Jockusch's theorem can be recast in terms of fragments of arithmetic: (1) (2)
人工智能原理及其应用(王万森)第3版课后习题答案
人工智能原理及其应用(王万森)第3版课后习题答案第1章人工智能概述课后题答案1.1什么是智能?智能包含哪几种能力?解:智能主要是指人类的自然智能。
一般认为,智能是是一种认识客观事物和运用知识解决问题的综合能力。
智能包含感知能力,记忆与思维能力,学习和自适应能力,行为能力1.2人类有哪几种思维方式?各有什么特点?解:人类思维方式有形象思维、抽象思维和灵感思维形象思维也称直感思维,是一种基于形象概念,根据感性形象认识材料,对客观对象进行处理的一种思维方式。
抽象思维也称逻辑思维,是一种基于抽象概念,根据逻辑规则对信息或知识进行处理的理性思维形式。
灵感思维也称顿悟思维,是一种显意识与潜意识相互作用的思维方式。
1.3什么是人工智能?它的研究目标是什么?解:从能力的角度讲,人工智能是指用人工的方法在机器(计算机)上实现智能;从学科的角度看,人工智能是一门研究如何构造智能机器或智能系统,使它能模拟、延伸和扩展人类智能的学科。
研究目标:对智能行为有效解释的理论分析;解释人类智能;构造具有智能的人工产品;1.4什么是图灵实验?图灵实验说明了什么?解:图灵实验可描述如下,该实验的参加者由一位测试主持人和两个被测试对象组成。
其中,两个被测试对象中一个是人,另一个是机器。
测试规则为:测试主持人和每个被测试对象分别位于彼此不能看见的房间中,相互之间只能通过计算机终端进行会话。
测试开始后,由测试主持人向被测试对象提出各种具有智能性的问题,但不能询问测试者的物理特征。
被测试对象在回答问题时,都应尽量使测试者相信自己是“人”,而另一位是”机器”。
在这个前提下,要求测试主持人区分这两个被测试对象中哪个是人,哪个是机器。
如果无论如何更换测试主持人和被测试对象的人,测试主持人总能分辨出人和机器的概率都小于50%,则认为该机器具有了智能。
1.5人工智能的发展经历了哪几个阶段?解:孕育期,形成期,知识应用期,从学派分立走向综合,智能科学技术学科的兴起1.6人工智能研究的基本内容有哪些?解:与脑科学与认知科学的交叉研究智能模拟的方法和技术研究1.7人工智能有哪几个主要学派?各自的特点是什么?解:符号主义:又称为逻辑主义、心理学派或计算机学派,是基于物理符号系统假设和有限合理性原理的人工智能学派。
《人工智能》课程习题
《人工智能》课程习题第一章绪论1-1. 什么是人工智能?试从学科和能力两方面加以说明。
1-2. 在人工智能的发展过程中,有哪些思想和思潮起了重要作用?1-3. 为什么能够用机器(计算机)模仿人的智能?1-4. 现在人工智能有哪些学派?它们的认知观是什么?1-5. 你认为应从哪些层次对认知行为进行研究?1-6. 人工智能的主要研究和应用领域是什么?其中,哪些是新的研究热点?第二章知识表示方法2-1状态空间法、问题归约法、谓词逻辑法和语义网络法的要点是什么?它们有何本质上的联系及异同点?2-2设有3个传教士和3个野人来到河边,打算乘一只船从右岸渡到左岸去。
该船的负载能力为两人。
在任何时候,如果野人人数超过传教士人数,那么野人就会把传教士吃掉。
他们怎样才能用这条船安全地把所有人都渡过河去?再定义描述过河方案的谓词:L-R(x, x1, y, y1,S):x1个修道士和y1个野人渡船从河的左岸到河的右岸条件:Safety(L,x-x1,y-y1,S’)∧Safety(R,3-x+x1,3-y+y1,S’)∧Boat(L,S)动作:Safety(L,x-x1,y-y1,S’)∧Safety(R,3-x+x1,3-y+y1,S’)∧Boat(R,S’)R-L (x, x1, y, y1,S):x2个修道士和y2个野人渡船从河的左岸到河的右岸条件:Safety(R,3-x-x2,3-y-y2,S’)∧Safety(L,x+x2,y+y2,S’)∧Boat(R,S)动作:Safety(R,3-x-x2,3-y-y2,S’)∧Safety(L,x+x2,y+y2,S’)∧Boat(L,S’)(2) 过河方案Safety(L,3,3,S0)∧Safety(R,0,0,S0)∧Boat(L,S0)L-R(3, 1, 3, 1,S0) L-R(3, 0, 3, 2,S0)Safety(L,2,2,S1)∧Safety(R,1,1,S1)∧Boat(R,S1)Safety(L,3,1,S1’)∧Safety(R,0,2,S1’)∧Boat(R,S1’)R-L (2, 1, 2, 0,S1) R-L (3,0, 1, 1,S1’)Safety(L,3,2,S2)∧Safety(R,0,1,S2)∧Boat(L,S2)L-R(3, 0, 2, 2,S2)Safety(L,3,0,S3)∧Safety(R,0,3,S3)∧Boat(R,S3)R-L (3, 0, 0, 1,S3)Safety(L,3,1,S4)∧Safety(R,0,2,S1)∧Boat(L,S4)L-R(3, 2, 1, 0,S4)Safety(L,1,1,S5)∧Safety(R,2,2,S5)∧Boat(R,S5)R-L (1, 1, 1, 1,S5)Safety(L,2,2,S6)∧Safety(R,1,1,S6)∧Boat(L,S6)L-R(2, 2, 2, 0,S6)Safety(L,0,2,S7)∧Safety(R,3,1,S7)∧Boat(R,S7)R-L (0, 0, 2, 1,S7)Safety(L,0,3,S8)∧Safety(R,3,0,S8)∧Boat(L,S8)L-R(0, 0, 3, 2,S8)Safety(L,0,1,S9)∧Safety(R,3,2,S9)∧Boat(R,S9)R-L (0, 1, 1, 0,S9)Safety(L,1,1,S10)∧Safety(R,2,2,S10)∧Boat(L,S10)2-3利用图2.3,用状态空间法规划一个最短的旅行路程:此旅程从城市A开始,访问其他城市不多于一次,并返回A。
nutz
Bruno Bouchard
†
Ludovic Moreau June 28, 2012
§
arXiv:1206.6325v1 [math.OC] 27 Jun 2012
We study a stochastic game where one player tries to find a strategy such that the state process reaches a target of controlled-loss-type, no matter which action is chosen by the other player. We provide, in a general setup, a relaxed geometric dynamic programming for this problem and derive, for the case of a controlled SDE, the corresponding dynamic programming equation in the sense of viscosity solutions. As an example, we consider a problem of partial hedging under Knightian uncertainty. Keywords Stochastic target; Stochastic game; Geometric dynamic programming principle; Viscosity solution AMS 2000 Subject Classifications 49N70; 91A23; 91A60; 49L20; 49L25
高级微观经济学(上海财经大学 陶佶)note01
d x , y ≡ ( x1 − y1 ) 2 + ( x2 − y2 ) 2 ≡ x − y
for x and y in . It is obvious to see that the space with the metric d above is a metric space. The metric d called as Euclidean metric or Euclidean norm (欧几里德范数) can be generalized to an n-dimensional Euclidean space. Definition 8. Open and Closed ε -Balls (开球和闭球): Let ε be a real positive number.
11nn10and0nnnn?????????????
2005 年秋季
高等微观经济学 I
实分析简介
Lecture Note I
1. Logic Consider two statements, A and B. Suppose B ⇒ A is true. 1. A is necessary (必要条件) for B. 2. B is sufficient (充分条件) for A. Contra-positive (逆否) form of B ⇒ A: ~A ⇒ ~B. If both A ⇒ B and B ⇒ A are true, then A and B are equivalent: A ⇔ B. 2. Set Theory We begin with a few definitions. A set (集合) is a collection of objects called elements (元素). Usually, sets are denoted by the capital letters A, B,
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A Note on Assumptions about Skolem FunctionsHans J¨u rgen Ohlbach and Christoph WeidenbachMax-Planck-Institut f¨u r InformatikIm Stadtwald66123Saarbr¨u cken,Germanyemail:(ohlbach,weidenb)@mpi-sb.mpg.deJournal of Automated Reasoning15(2):267–275November10,1997Abstract.Skolemization is not an equivalence preserving transformation.For the purposes of refutational theorem proving it is sufficient that Skolemization preserves satisfiability and unsatis-fiability.Therefore there is sometimes some freedom in interpreting Skolem functions in a particu-lar way.We show that in certain cases it is possible to exploit this freedom for simplifying formulae considerably.Examples for cases where this occurs systematically are the relational translation from modal logics to predicate logic and the relativization offirst-order logics with sorts.Key words:Skolemization,Refutational Theorem Proving1.IntroductionRefutational theorem proving has a certain degree of freedom which so far is not very often exploited.All kinds of transformations preserving satisfiability and unsatisfiability of the formulae to be refuted are allowed.Skolemization is a typical transformation which is not equivalence preserving but satisfiability and unsatis-fiability preserving.But this is usually the only routinely applied transformation with this property.For an existential quantification,Skolemization introduces a new function sym-bol whose interpretation is in general not completely determined.Sometimes it is possible to make additional assumptions about the new Skolem function without changing satisfiability and unsatisfiability of the formulae.As an example consider the formula∀x(Φ(x)⊃∃y(B(x,y)∧C(x,y)))Skolemization and clausification yields as an intermediate resultΦ(x)⊃B(x,f(x))(1)Φ(x)⊃C(x,f(x))(2) IfΦis a big formula this duplication may be disastrous.If B is serial,i.e.∀x∃y B(x,y) holds,we claim that the following optimized transformation is possible:B(x,f(x))(3)Φ(x)⊃C(x,f(x))(4)2Hans J¨u rgen Ohlbach and Christoph Weidenbachwhere one occurrence ofΦis dropped or,equivalently,the Skolem form B(x,f(x))of B(x,y)is moved to the top-level of the formula.That means the axiomatization of f is made stronger and the clauses become shorter.The nontrivial part of thecorrectness proof,which also shows the idea behind the transformation,amountsto transforming a model for(1)and(2)into a model for(3)and(4).Since f isa Skolem function,we use the freedom to define a suitable interpretation for f.SupposeΦ(x)is true for some assignment x/a.Then by the clause(1),B(x,f(x))is true as well,i.e.(3)is true,where f(a)is the same value in(1)and(3).This isthe unproblematic case.Now supposeΦ(x)is false for the assignment x/a.Then(4)is true independently of the meaning of f.But what about(3)?Here we usethe seriality assumption for B.We assume∀x∃y B(x,y).This tells us that thereis some c such that B(x,y)is true for the assignment[x/a,y/c].That means wecan define f(a)to be just this c and then(3)becomes true as well.This informal description is made precise in the next section.In section3weshow that certain well known transformations in sorted logics and modal logics arein fact instances of this optimized Skolemization.We give several examples which show that optimized Skolemization can improve the proof search considerably.2.Optimized SkolemizationFor a precise definition of the optimized Skolemization we need to manipulatesubformulae of a formula at a certain position inside the subformula and witha certain polarity.To this end we introduce the standard definitions of formulaoccurrences and polarities.An occurrence is a word over I N.Let denote the empty word.Then we definethe set of occurrences occ(Φ)of a formulaΦas follows:(i)the empty word is in occ(Φ)(ii)i.πis in occ(Φ)iffΦ=Ψ1∧...∧Ψn orΦ=Ψ1∨...∨Ψn,1≤i≤n andπ∈occ(Ψi)(iii)1.π(2.π)is in occ(Φ)iffΦ=∀xΨorΦ=∃xΨorΦ=¬ΨorΦ=Ψ⊃ΘorΦ=Ψ≡Θandπ∈occ(Ψ)(π∈occ(Θ)).Now ifπ∈occ(Ψ)thenΨ| =ΨandΨ|i.π=Ψi|πwhereΨi is the i th subformula ofΨ(see above).Intuitively,the polarity of some formulaΦ|π=ΨinΦis1ifΨoccurs below aneven number of(explicit or implicit)negation symbols,it is-1ifΨoccurs belowan odd number of negation symbols and it is0ifΨoccurs below an equivalencesymbol.We define the polarity pol(Φ,π)of a formulaΦ|πin a formulaΦby:(i)pol(Φ, )=1(ii)pol(Φ,i.π)=pol(Φ|i,π)ifΦis a conjunction,disjunction,quantifier formula orΦis an implication and i=2(iii)pol(Φ,i.π)=−pol(Φ|i,π)ifΦis a negation orΦis an implication and i=1(iv)pol(Φ,i.π)=0ifΦis anequivalence.We also use occurrences to define the replacement of a formula inside anotherformula.Φ[π/Ψ]withπ∈occ(Φ)is the formula obtained fromΦby replacingΦ|πinΦwithΨ.skolem.tex;10/11/1997;1:54;no v.;p.3A Note on Assumptions about Skolem Functions3Usually,Skolemization is defined with respect to a formula in negation normal form.We generalize this definition to the case of arbitrary formulae.The polarity function tells us whether a formula remains unchanged by producing the negation normal form.A formulaΦ|π,π∈occ(Φ),is in the scope of a universal quantifier,if there exists a prefixµofπsuch thatΦ|µ=∀xΘand pol(Φ,µ)=1orΦ|µ=∃xΘandpol(Φ,µ)=−1or pol(Φ,µ)=0andΦ|µis an arbitrary quantificational formula.It is in the scope of an existential quantifier,if there exists a prefixνofπsuch thatΦ|ν=∀xΘand pol(Φ,ν)=−1orΦ|ν=∃xΘand pol(Φ,ν)=1or pol(Φ,µ)=0andΦ|µis an arbitrary quantificational formula.LEMMA1.LetΦ|π=Ψwithπ∈occ(Φ)and pol(Φ,π)=1and M be an inter-pretation.Thenif M|=Φ∧(Ψ⊃Θ)then M|=Φ[π/Θ]Proof.The proof is due to Loveland[2,Lemma1.5.1,p.40].THEOREM2(Optimized Skolemization).LetΦ|π=∃y(Ψ∧Θ),π∈occ(Φ),pol(Φ,π)= 1,∃y(Ψ∧Θ)is not in the scope of an existential quantifier and let x1,...,x n bethe universally quantified variables which occur freely in∃y(Ψ∧Θ).In addition,we assume the seriality condition|=Φ⊃∀x1,...,x n∃yΘ.ThenΦis satisfiableiffΦ[π/Ψ{y/f(x1,...,x n)}]∧∀x1,...,x nΘ{y/f(x1,...,x n)}is satisfiablewhere f is a new n-place Skolem function.Proof.“⇒”Assume M|=Φ.Since f is new toΦit is sufficient to construct an interpretation M which is like M,but in addition specifies an interpretation for f such that M |=Φ[π/Ψ{y/f(x1,...,x n)}]∧∀x1,...,x nΘ{y/f(x1,...,x n)}.Con-sider domain elements a1,...,a n as assignments for the universally quantified vari-ables x1,...,x n.If M[x1/a1,...,x n/a n]|=∃y(Ψ∧Θ),then there exists some b as assignment for y such that M[x1/a1,...,x n/a n,y/b]|=Ψ∧Θ.We choose b as val-ue for f,i.e.f M (a1,...,a n)def=b.If M[x1/a1,...,x n/a n]|=∃y(Ψ∧Θ)we choosef M (a1,...,a n)def=c,where M[x1/a1,...,x n/a n,y/c]|=Θ.Such a c always existsby the seriality assumption M|=∀x1,...,x n∃yΘwhich implies,as f is new toΦ,M |=∀x1,...,x n∃yΘ.Now by construction of f M we have M |=Φ∧(∃y(Ψ∧Θ)⊃Ψ{y/f(x1,...,x n)})and thus by Lemma1,M |=Φ[π/Ψ{y/f(x1,...,x n)}].In addition,M |=∀x1,...,x n∃yΘ∧(∃yΘ⊃Θ{y/f(x1,...,x n)})and thus againby Lemma1,M |=∀x1,...,x nΘ{y/f(x1,...,x n)}.“⇐”Assume M|=Φ[π/Ψ{y/f(x1,...,x n)}]∧∀x1,...,x nΘ{y/f(x1,...,x n)}. Then we have:M|=Ψ{y/f(x1,...,x n)}⊃∃y(Ψ∧Θ)by choosing y/f M(a1,...,a n)for y in∃y(Ψ∧Θ)and any assignment a1,...,a n of the x1,...,x n.Now by Lem-ma1we conclude M|=Φ.skolem.tex;10/11/1997;1:54;no v.;p.44Hans J¨u rgen Ohlbach and Christoph WeidenbachEXAMPLE3.We apply the optimized Skolemization to Pelletier’s[6]problem no29:(1)∃x F(x)(2)∃x G(x)(3)¬[(∀x(F(x)⊃H(x))∧∀x(G(x)⊃J(x)))≡∀x,y((F(x)∧G(y))⊃(H(x)∧J(y)))]Elimination of the equivalence symbol in(3)by¬[Φ≡Ψ]iff(Φ∨Ψ)∧(¬Φ∨¬Ψ) gives:(4)∀x(F(x)⊃H(x))∧∀x(G(x)⊃J(x)))∨∀x,y((F(x)∧G(y))⊃(H(x)∧J(y)))(5)¬(∀x(F(x)⊃H(x))∧∀x(G(x)⊃J(x)))∨¬∀x,y((F(x)∧G(y))⊃(H(x)∧J(y)))For the purpose of readability we move the negation symbols and quantifiers occur-ring in(5)inside:(6)∃x(F(x)∧¬H(x))∨∃x(G(x)∧¬J(x))∨∃x(F(x)∧∃y(G(y)∧(¬H(x)∨¬J(y)))) There are the following occurrences of existential formulae:(6)|1=∃x(F(x)∧¬H(x))(6)|2=∃x(G(x)∧¬J(x))(6)|3=∃x(F(x)∧∃y(G(y)∧(¬H(x)∨¬J(y))))(6)|312=∃y(G(y)∧(¬H(x)∨¬J(y)))Theorem2is applicable to all these occurrences,because all occurrences have polarity1and the formulae(1),(2)guarantee the seriality condition for the atoms of the form F(x),G(x),which we want to move outside.Thus we get after opti-mized Skolemization:(7)¬H(a)∨¬J(b)∨¬H(c)∨¬J(d)(8)F(a)∧G(b)∧F(c)∧G(d)It is obvious that a refutation of the formulae(1),(2),(4),(7),(8)is much simpler than refuting(1),(2),(4),(5).In fact,OTTER[3](version3.0,auto mode)needed half of the time and clauses to refute the formulae with optimized Skolemization compared to the formulae translated with OTTER’s standard Skolemization pro-cedure.In addition,the optimized Skolemization proof is shorter and has a lower proof complexity.skolem.tex;10/11/1997;1:54;no v.;p.5A Note on Assumptions about Skolem Functions5 As pointed out by a reviewer,our Skolemization technique is not new in the sense that it can be simulated by standard Skolemization and equivalence preserving transformations.The formulaΦof Theorem2is equivalent to the formula∀x1,...,x n∃yΦ[π/(Ψ∧Θ)]∧∀x1,...,x n∃yΘ(5)because|=Φ⊃∀x1,...,x n∃yΘ.Now(5)is equivalent to the formula∀x1,...,x n∃y[Φ[π/Ψ]∧Θ](6)This can be proved by techniques similar to those used to prove Theorem2.Even-tually standard Skolemization yields∀x1,...,x n[Φ[π/Ψ{y/f(x1,...,x n)}]∧Θ{y/f(x1,...,x n)}](7)which is exactly the result of Theorem2if the universal quantifiers are moved inside.However,we prefer the formulation of Theorem2,because we interpret ∀x1,...,x nΘ{y/f(x1,...,x n)}as a(stronger)definition of the Skolem function f.In addition,the formulation of Theorem2is compatible with the usual tech-niques for clause normal form,e.g.anti-prenexing,whereas the above argumenta-tion requires to move quantifiers outwards.3.D´e ja vuThe optimized Skolemization has been used implicitly in some other systems. 3.1.Sorted LogicThe fact that information about Skolem functions can be moved from a local context to the top-level has been implicitly exploited in sorted logic.Consider the formulaΦ⊃∃x B C(x B),which is the sorted formalization ofΦ⊃∃x(B(x)∧C(x)).In sorted logics,where all sorts are a priori assumed non-empty,it gets Skolemized toΦ⊃C(a)and the sort declaration B(a)is added to the top-level sort declarations[9,8].Thus,the sort declaration for a does not depend on the conditionΦanymore.That means global sort declarations about Skolem functions implicitly apply the optimized Skolemization.Weidenbach[10,11]shows how sorted Skolemization is applied if the sorts are not a priori assumed non-empty.Then it is only possible to move the sort declarations of Skolem functions outside,if the sort of the existential variable can be proved non-empty.Otherwise the sort declaration remains inside the formula and makes a more general approach to sorted reasoning necessary.The example of Section2is an instance of Weidenbach’s approach to sort-ed logic.The unary predicates can be translated into sorts.This enables further simplifications of formula(4):skolem.tex;10/11/1997;1:54;no v.;p.66Hans J¨u rgen Ohlbach and Christoph Weidenbach(4 )(∀x F H(x F)∧∀y G J(y G))∨∀x F,y G(H(x F)∧J(y G))Since the sorts F and G are non-empty(see(1),(2)),(4 )is further simplified to(4 )∀x F H(x F)∧∀y G J(y G)Now a refutation of(1),(2),(4 ),(7),(8)by resolution extended with sorts [11]yields no search anymore.Every possible resolution step contributes to the proof.3.2.Modal LogicModal Logic is an extension of predicate logic with the two operators2and3[1]. The standard Kripke semantics of normal modal systems interprets the2-operator as a universal quantification over accessible worlds and the3-operator as an exis-tential quantification over accessible worlds.This semantics can be exploited to define a“relational”translation from modal to predicate logic.For example23P is translated into∀w(R(o,w)⊃∃v(R(w,v)∧P(v))).R denotes the accessibility relation and o some initial world.Notice that the translation of the3-operator has the typical pattern where our optimized Skolemization is applicable—provided the accessibility relation is serial,i.e.we have modal systems above D.The overall effect of the optimized Skolemization is that the conditions on R coming from3-operators become pos-itive unit clauses.From the2-operator we obtain only negative literals in the clauses.Then the negative R literals can be viewed as constraints over the theory consisting of the positive R-unit clauses and the formulae of the specific modal logic.This approach has been studied by Scherl[7].EXAMPLE4.We show the power of the optimized Skolemization by an exam-ple taken from modal logic KD45[1].In modal logic KD45the formula32P≡3232P is a theorem.The theorem can be translated intofirst-order logic by introducing an accessibility relation R.Then the theorem is:∃x(R(o,x)∧∀y(R(x,y)⊃P(y)))≡∃x(R(o,x)∧∀y(R(x,y)⊃∃z(R(y,z)∧∀u(R(z,u)⊃P(u)))))Here o names the initial world and R(x,y)means that world y is accessible from world x.The properties of R in modal logic KD45are expressed by the following formulae:(1)∀x∃y R(x,y)(2)∀x,y,z(R(x,y)∧R(y,z)⊃R(x,z))(3)∀x,y,z(R(x,y)∧R(x,z)⊃R(y,z))skolem.tex;10/11/1997;1:54;no v.;p.7A Note on Assumptions about Skolem Functions7 Now we apply Theorem2to the negated theorem.In order to get positive polarities for the existential subformulae,we eliminate the equivalence symbol by¬[Φ≡Ψ] iff(Φ∨Ψ)∧(¬Φ∨¬Ψ).For better readability we move the negation sign inside. The result is:(4)∃x(R(o,x)∧∀y(R(x,y)⊃P(y)))∨∃x(R(o,x)∧∀y(R(x,y)⊃∃z(R(y,z)∧∀u(R(z,u)⊃P(u)))))(5)∀x(R(o,x)⊃∃y(R(x,y)∧¬P(y)))∨∀x(R(o,x)⊃∃y(R(x,y)∧∀z(R(y,z)⊃∃u(R(z,u)∧¬P(u)))) There are the following occurrences in(4)and(5)which name existentially quan-tified subformulae:(4)|1=∃x(R(o,x)∧∀y(R(x,y)⊃P(y)))(4)|2=∃x(R(o,x)∧∀y(R(x,y)⊃∃z(R(y,z)∧∀u(R(z,u)⊃P(u)))))(4)|21212=∃z(R(y,z)∧∀u(R(z,u)⊃P(u)))(5)|112=∃y(R(x,y)∧¬P(y))(5)|212=∃y(R(x,y)∧∀z(R(y,z)⊃∃u(R(z,u)∧¬P(u))))(5)|2121212=∃u(R(z,u)∧¬P(u))All occurrences have polarity1.They are either of the form∃w(R(o,w)∧Ψ)or ∃w(R(v,w)∧Ψ),where v,w are variables.In order to apply Theorem2and to move the formula R(o,w)(R(v,w))outside,we must prove the seriality condition |=((1)∧(2)∧(3)∧(4)∧(5))⊃∃wR(o,w)(|=((1)∧(2)∧(3)∧(4)∧(5))⊃∃wR(v,w)). The proof is trivial,since(1)already implies the seriality of R.Thus Theorem2 is applicable to all occurrences of the existential quantifiers.For example we start with the occurrence1of(4).We introduce a new constant a,replace∃x(R(o,x)∧∀y(R(x,y)⊃P(y)))with∀y(R(a,y)⊃P(y)))and add R(o,a)as a conjunct to (4).The procedure can be repeated for the other occurrences.Eventually,we get(6)∀y(R(a,y)⊃P(y)))∨∀y(R(b,y)⊃∀u(R(h(y),u)⊃P(u)))(7)R(o,a)∧R(o,b)∧∀y R(y,h(y))(8)∀x(R(o,x)⊃¬P(i(x)))∨∀x(R(o,x)⊃∀z(R(f(x),z)⊃¬P(g(x,z))))(9)∀x R(x,i(x))∧∀x R(x,f(x))∧∀x,z R(z,g(x,z))where(6)is the optimized Skolemization of(4),(8)is the optimized Skolemization of(5),(7)are the R atoms moved outside(4)and(9)are the R atoms moved outside(5).The obvious advantage of this Skolemization technique are the stronger defi-nitions for the Skolem functions(constants).Using standard Skolemization these definitions usually occur in disjunctions with other literals from the theorem.Thisskolem.tex;10/11/1997;1:54;no v.;p.88Hans J¨u rgen Ohlbach and Christoph Weidenbachmakes a proof of the theorem more complicated.The theorem prover OTTER (version3.0,auto mode)proves the theorem with optimized Skolemization in less than one minute,i.e.it refutes the formulae(1),(2),(3),(6),(7),(8),(9).Although we tried various parameter settings,OTTER did notfind a proof of the theorem in the version with standard Skolemization,i.e.OTTER fails to refute the formulae (1),(2),(3),(4),(5)using its standard Skolemization.However,it should be noted that the special translation techniques developed for modal logic,e.g.see the work of Nonnengart[4]or the work of Ohlbach[5],are still more powerful than our optimized Skolemization,because they also eliminate the formulae coming from the specific modal logics(in our case the formulae(1), (2),(3)).Translating the above example using Nonnengart’s approach we get (1 )∀x,y,z R(x,y:z)(4 )∀x(R(o:a,x)⊃P(x))∨∀y(R(o:b,y)⊃∀z(R(y:h(y),z)⊃P(z)))(5 )∀x(R(o,x)⊃P(x:i(x)))∨∀y(R(o,y)⊃∀z(R(y:f(y),z)⊃P(z:g(y,z)))) where“:”is a new two-place function symbol written in infix notation.The formula (1 )is the translation of(1),(2),(3),(4 )is the translation of(4)and(5 )is the translation of(5).The formulae(1 ),(4 ),(5 )are refuted by OTTER in less than one second.4.SummaryWe have presented an optimized Skolemization of existential quantifiers which moves information about the Skolem function from the local context of the occur-rence of the existential quantifier to the top-level of the formula.Instances of this optimized Skolemization have been used implicitly or explicitly in special appli-cations.We have defined it now in such a way that it can be used as a general method for arbitrary formulae.However,the proof of the seriality condition may be as complex as the proof of the input formula,in general.Therefore optimized Skolemization requires a more sophisticated implementation concept than standard Skolemization.Nevertheless there are many examples where the seriality condition can be easily proved(e.g.see the examples above,other problems of the Pelletier collection)and then optimized Skolemization avoids duplication of literals,yields shorter clauses,shorter and less complex proofs and a smaller search space.In some cases optimized Skolemization makes a proof possible where proof procedures using standard Skolemization fail.References1. 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