In: Proceedings of the Fourteenth International Joint Conference on Articial Intelligence (IJCAI-95), C. Mellish

Size: px
Start display at page:

Download "In: Proceedings of the Fourteenth International Joint Conference on Articial Intelligence (IJCAI-95), C. Mellish"

Transcription

1 I: rceedigs f the Furteeth Iteratial Jit Cferece Articial Itelligece (IJCAI-95), C. Mellish ed, Mrga Kaufma, I press. Sematics ad Cmplexity f Abducti frm Default Theries (Exteded abstract ) Thmas Eiter, Gerg Gttlb Christia Dppler Lab fr Expert Systems Techical Uiversity f Viea aiglgasse 16, A-1040 Wie, Austria (eiterjgttlb)@dbai.tuwie.ac.at Abstract Sice lgical kwledge represetati is cmmly based classical frmalisms like default lgic, autepistemic lgic, r circumscripti, it is ecessary t perfrm abductive reasig frm theries f classical lgics. I this paper, we ivestigate hw abducti ca be perfrmed frm theries i default lgic. Dieret mdes f abducti are plausible, based credulus ad skeptical default reasig they appear useful fr dieret applicatis such as diagsis ad plaig. Mrever, we aalyze the cmplexity f the mai abductive reasig tasks. They are itractable i the geeral case we als preset kw classes f default theries fr which abducti is tractable. 1 Itrducti Abductive reasig has bee recgized as a imprtat priciple f cmm-sese reasig havig fruitful applicatis i a umber f areas such diverse as mdelbased diagsis le, 1989], speech recgiti Hbbs et al., 1988], maiteace f database views Kakas ad Macarella, 1990], ad visi Chariak ad McDermtt, 1985]. Util w, maily abducti frm theries f classical lgic has bee studied. Hwever, lgical kwledge represetati is cmmly based classical frmalisms like default lgic, autepistemic lgic, r circumscripti. Thus, i such situatis it is ecessary t perfrm abductive reasig frm theries (i.e. kwledge bases) f classical lgics. Sice default lgic is widely prpsed fr kwledge represetati, it is imprtat t ivestigate hw abducti ca be perfrmed frm theries hw Di i default lgic. We ifrmally pursue this a example. Example 1 Csider the fllwig set f default rules, which represet kwledge abut Bill's skiig habits: : :skiig Bill weeked : :swig : :swig :skiig Bill skiig Bill :swig A mre elabrate versi icludig prfs is available request t the authrs. y Wrk carried ut while visitig the Christia Dppler Lab. Nicla Lee y Istitut per la Sistemistica e l'ifrmatica C.N.R. c/ DEIS { Uiversity f Calabria 8706 Rede, Italy ik@si.deis.uical.it The defaults ituitively state the fllwig: (i) Bill is usually t ut fr skiig (ii) Bill is ut fr skiig weekeds, if we ca assume that it is t swig (iii) usually it is t swig. Fr the certai kwledge W = f weeked g (ecdig that it is Saturday r Suday), the default thery T = hw Di has e extesi which ctais :swig ad skiig Bill. Suppse w we bserve that Bill is t ut fr skiig (which is icsistet with the extesi). Abducti meas t d a explaati fr this bservati, that is, t idetify a set f facts, chse frm a set f hyptheses, whse presece i the thery at had wuld etail the bservati :skiig Bill, i.e., cause that :skiig Bill is i the extesi. We d such a explaati by adptig the hypthesis swig. Ideed, if we add swig t W, the default thery T 0 = hf weeked swig g Di has a sigle extesi, which ctais :skiig Bill. We say that swig is abduced frm the bservati :skiig Bill, r that it is a abductive explaati f :skiig Bill. Observe that the descripti f the abve situati requires the specicati f sme default prperties that ca t be represeted prperly i classical lgic. I geeral, as ppsed t the example, a default thery may have several r eve extesis. Fr deductive etailmet, this gives rise t credulus etailmet, uder which is etailed frm a default thery T (deted T `c ) i belgs t at least e extesi f T, ad t skeptical etailmet, uder which fllws frm T (T `s ) i belgs t all extesis f T. Accrdigly, tw variats f abducti frm default theries arise: credulus abducti, where etailmet f a bservati is based `c, ad skeptical abducti, which is based `s. I practice, the user will chse credulus r skeptical abducti the basis f the particular applicati dmai. We argue that credulus abducti is well suited fr diagsis, while skeptical abducti is adequate fr plaig. (Cf. le, 1989] ad Eshghi, 1988 Ng ad Mey, 1991] fr abducti i lgic-based diagsis ad plaig & pla recgiti, respectively.) I fact, csider a system represeted by a default thery hw Di. If it receives sme iput, reected by addig a set A f facts t W, the each extesi f hw A Di is a pssible evluti f the system, i.e., each extesi represets a pssible reacti f the system t A.

2 Abductive diagsis csists, lsely speakig, i derivig frm a bserved system state (characterized by the truth f a set F f facts), a suitable iput A which caused this evluti (cf. le, 1989]). Nw, sice each extesi f hw A Di is a pssible evluti f the system with iput A, we ca assert that A is a pssible iput that caused F if hw A Di `c F. Thus, diagstic prblems ca be aturally represeted by abductive prblems with credulus etailmet. Example Assume there are tw sky rutes, rv1 ad rv, betwee Rme ad Viea, ad three sky rutes mv1, mv, ad mv betwee Mila ad Viea. Rute mv1 itersects rute rv1, ad mv itersects rv. O rmal speed ad ight cditis, tw plaes frm Mila ad Rme t Viea will cllide if the plae frm Mila takes 0 miutes after the plae frm Rme ad they y itersectig rutes. This kwledge abut pssible cllisis is represeted i simplied frm by the fllwig set D f prpsitial defaults: mv1 ^ rv1 ^ m 0mi later : cllisi cllisi mv ^ rv ^ m 0mi later : cllisi cllisi : :rv :rv : :cllisi :cllisi : :rv1 :rv1 : :mv1 :mv1 : :mv :mv : :mv :mv Nw, yu are ifrmed that plaes yig frm Mila ad Rme t Viea cllided. A diagsis fr the cllisi ca be btaied by abducig a explaati fr the bservati cllisi frm the thery T = h Di. I this case, we wat t kw pssible ight schedules that ca have caused the cllisi. I ther wrds, we are lkig fr schedules S such that cllisi is i sme extesi f the thery T 0 = hs Di (T 0 `c cllisi). Credulus abducti crrectly ideties such explaatis. Fr istace, it is easy t recgize that bth E1 = fmv1 rv1 m 0mi laterg ad E = fmv rv m 0mi laterg are credulus explaatis fr cllisi. Suppse w we wat that the system evlves it a certai state (described by a set F f facts), ad we have t determie the \right" iput that efrces this state f the system (plaig). I this case it is t suciet t chse a iput A such that F is true i sme pssible evluti f the system rather, we lk fr a iput A such that F is true i all pssible evlutis, as we wat be sure that the system reacts i that particular way. I ther wrds, we lk fr A such that hw A Di `s F. Hece, plaig activities ca be represeted by abductive prblems with skeptical etailmet. Example We kw that a plae frm Rme t Viea left at 7.50 (r 7:50), but we d t kw which rute. We have t schedule the ight f a plae frm Mila t Viea, where take is pssible at 8.10 (m 8:10) ad at 8.0 (m 8:0). The cllisi-free schedules ca be btaied by dig a abductive explaati ut f the hyptheses m 8:10, m 8:0, mv1, mv, mv fr the bservati :cllisi frm the thery T = hw D1i, W = f r 7:50 rv1 _ rv m 8:10 _ m 8:0 mv1 _ mv _ mv r 7:50 ^ m 8:10 m 0mi later g : :m 8:10 D1 = D :m 8:10 : :m 8:0 :m 8:0 As we ca t risk a cllisi, we wat that every pssible evluti f the system is cllisi-free. Thus, we have t lk fr skeptical explaatis f :cllisi. Fr istace, bth E = fm 8:0g ad E4 = fmvg are skeptical explaatis fr :cllisi that is, take at 8.0 r usig rute mv prevets a cllisi, where the rute i E ad the time i E4 ca be chse freely. The tw examples abve supprt the ituiti that credulus abducti is feasible fr diagsis, while skeptical abducti is well-suited fr plaig. O the ther had, Secti 4 shws that skeptical abducti has mst likely a higher cmplexity tha credulus abducti thus, frm the abve pit f view, plaig is mst likely harder tha diagsis. Fr space reass, we ly preset sme prf sketches. rfs f all results are give i the full paper. relimiaries ad Ntati We assume that the reader kws the basic ccepts f default lgic Reiter, 1980] (cf. als Marek ad Truszczyski, 199] fr a extesive study). We fcus prpsitial default theries T = hw Di ver a prpsitial laguage L (icludig? fr falsity), i.e. W is a subset f L ad D a set f defaults :1:::m, m 1 where, 1 : : : m, are frm L. The extesis f T, which are deductively clsed sets E L, are deed by a xpit equati i particular, L is a extesi f T (ad, i this case, uique) i W is t csistet. Recall that T is rmal i each default i D is rmal, i.e., f frm : a rmal T always has a extesi. Fr N-cmpleteess ad cmplexity thery, cf. Jhs, 1990]. The classes k ad k f the plymial hierarchy are deed as fllws: = 0 0 =, ad k = N k?1 k = c- k fr all k 11: I particular, N = 1 ad c-n = 1. The class D k, which is deed as the class f prblems that csist f the cjucti f tw (idepedet) prblems frm k ad k, respectively, is csidered t be further restricted i cmputatial pwer. Fr all k 1, clearly k D k k+1 bth iclusis are believed t be strict. May mtic reasig prblems are cmplete fr classes at the lwer ed f the plymial hierarchy Cadli ad Schaerf, 199 Nebel, 1994]. It is well-kw that decidig whether a prpsitial default thery has a extesi is - cmplete, ad that credulus ad skeptical reasig frm default theries are cmplete fr ad, respectively. This remais true if icsistet extesis are excluded ad, fr the latter prblems, if default theries are i additi rmal Gttlb, 199

3 Stillma, 199]. Cases f lwer cmplexity ad tractable fragmets were idetied i Kautz ad Selma, 1991 Stillma, 1990]. Frmalizig default abducti I this secti, we describe a basic frmal mdel fr abducti frm prpsitial default theries ad state the mai decisial reasig tasks fr abductive reasig. Our frmalizati f a abducti sceari is as fllws. Deiti 1 A prpsitial default abducti prblem (DA) is a quadruple hh M W Di where H is a set f prpsitial literals (called hyptheses, r abducibles), M is a set f prpsitial literals (bservatis, r maifestatis), ad hw Di is a prpsitial default thery. is rmal i each default i D is rmal. Nte that hyptheses ad maifestatis may be literals rather tha atms. Allwig literals as hyptheses is cmm i abducti, cf. Selma ad Levesque, 1990]. Hwever, this has eect the expressive pwer r cmplexity f the frmalism i geeral. Credulus ad skeptical explaatis are as fllws. Deiti Let = hh M W Di be a DA, ad let E H. The, E is a credulus explaati fr i (i) hw E Di `c M, ad (ii) hw E Di has a csistet extesi. Similarly, E is a skeptical explaati fr i (i) hw E Di `s M ad (ii) hw E Di has a csistet extesi. The existece f a csistet extesi fr hw E Di (i this case, all extesis are csistet) assures that the explaati E is csistet with the kwledge represeted i hw Di. This is aalgus t the csistecy criteri i abducti frm classical theries. It is cmm i abductive reasig t prue the set f all explaatis ad t fcus, guided by sme priciple f explaati preferece, a set f preferred explaatis. The mst imprtat such priciple is, fllwig Occam's priciple f parsimy, t prefer redudat explaatis, i.e., explaatis which d t ctai ay ther explaati prperly, cf. eg ad Reggia, 1990 Selma ad Levesque, 1990 Klige, 199]. We refer t such explaatis as miimal explaatis. I Example E = fm 8:0g ad E4 = fmvg are the miimal explaatis they represet the smallest partial schedules that ca be arbitrarily cmpleted t cllisi-free schedules, ad thus prvide the greatest exibility. I the sequel, we will write Exp() fr the set f explaatis fr the DA, abstractig frm the chse type f explaatis (credulus, skeptical, miimal credulus, r miimal skeptical). The fllwig prperties f a hypthesis i a DA are imprtat with respect t cmputig explaatis. Deiti Let = hh M W Di be a DA ad h H. The, h is relevat (resp. ecessary) fr i h E fr sme (resp. every) E Exp(). The ppsite f ecessity is als termed dispesability (cf. Jsephs et al., 1987]). I Example, m 0mi later is ecessary, while each hypthesis rv1 rv mv1 mv is relevat, but t ecessary. Mrever, i Example mv is relevat w.r.t. miimal (skeptical) explaatis, but t ecessary. Nte that i the same example rv1 is relevat uder arbitrary explaatis, but t relevat uder miimal explaatis. The mai decisial prblems i abductive reasig amut t the fllwig. Give a DA = hh M W Di, (Csistecy): des there exist a explaati fr? (Relevace): is a give hypthesis h H relevat fr, i.e., des h ctribute t sme explaati f? (Necessity): is a give hypthesis h H ecessary fr, i.e., is h ctaied i all explaatis f? Due t the fllwig simple fact, we shall t deal i ur aalysis explicitly with Necessity i the case f miimal explaatis. rpsiti 1 Let = hh M W Di be a DA ad let h H. The, h is ecessary fr uder miimal credulus (resp. skeptical) explaatis i h is ecessary fr uder credulus (resp. skeptical) explaatis. 4 Results The mai results the cmplexity f abducti frm geeral prpsitial default theries are summarized i Table 1. I ur aalysis, we pay particular atteti t rmal DAs, sice this class crrespds t the mst imprtat fragmet f default lgic. All hardess results i Table 1 have bee derived fr the case where the uderlyig default thery hw Di is rmal. Thus like deducti, abducti frm rmal default theries is as hard as abducti frm arbitrary default theries. We itrduce sme additial tati. Fr a set A f prpsitial atms, we dete by :A the set f:a j a Ag ad by A 0 the set f atms fa 0 j a Ag. 4.1 Arbitrary explaatis Our rst result shws that abducti frm default theries based credulus explaatis ca be ecietly reduced t deductive reasig frm prpsitial default theries. This is smewhat uexpected ad surprisig, sice i case f classical theries, abducti ca t be ecietly reduced t deducti. Give a DA = hh M W Di, we cstruct a default thery T = hw D i such that the credulus explaatis f crrespd t the extesis f T. Ideed, dee W = W fa h h j h Hg, j m M D = D ::m? :a h a h ::a h :a j h h H where fr each h H, a h is a ew prpsitial atm. The, we have: Therem 1 Let = hh M W Di be a DA. The, (i) if E is a credulus explaati fr, the there exists a csistet extesi E 0 f T such that E = fh H j a h E 0 g (ii) if E 0 is a csistet extesi f T, the E = fh H j a h E 0 g is a credulus explaati fr.

4 DA = hh M W Di arbitrary explaatis miimal explaatis rblem: credulus skeptical credulus skeptical Exp() 6= E Exp() E Exp() is miimal D D h H is relevat fr 4 h H is ecessary fr Table 1: Cmplexity results fr abducti frm prpsitial default theries Usig (i) ad (ii), the mai decisial abductive reasig tasks ca be ecietly trasfrmed t similar deductive reasig tasks i default lgic. Crllary 1 Let be a DA based credulus explaatis. The, (i) Csistecy, (ii) Relevace, ad (iii) Necessity are equivalet t (i') existece f a csistet extesi f T, (ii') membership f a h i sme csistet extesi f T, ad (iii') membership f a h i all extesis f T, respectively. By the results the cmplexity f prpsitial default lgic Gttlb, 199 Stillma, 199], it fllws that (i) ad (ii) are i ad that (iii) is i. We als btai matchig hardess by reductis frm deductive default reasig. Let T = hw Di be a rmal default thery such that W is csistet, ad a frmula. Let h q be ew prpsitial atms. The, the DA () h fqg W f qg Di has a credulus explaati i T `c h is relevat fr the DA () hfhg fqg W f qg Di i T `c ad h is ecessary fr the DA ( ) hfhg fqg W f _ h qg Di i T 6`c. Sice the reasig prblems fr T i (*), (**) are -hard ad the e i (***) is -hard Gttlb, 199], the hardess results fllw. It is iterestig t te that verifyig a credulus explaati is as hard as dig e. The frmer prblem ca be easily reduced t the latter mrever, is the ly pssible credulus explaati fr the DA (*). Thus, Therem Let = hh M W Di be a DA. Decidig if E H is a credulus explaati fr is - cmplete, with hardess hldig eve fr rmal. Nw csider abducti based skeptical reasig. It wuld be useful t have a reducti f abductive reasig t deductive reasig which ca be cmputed ecietly. Hwever, by usig skeptical reasig the abductive reasig tasks grw mre cmplex, by e level f the plymial hierarchy. This strgly suggests that such a eciet reducti is t pssible. We rst csider the prblem f recgizig skeptical slutis. Clearly, this reduces t decidig if a certai default thery has a csistet extesi (which is i ) ad if each extesi icludes all maifestatis ( ). Thus, the prblem is a lgical cjucti f a prblem i ad a prblem i, ad hece i the class D. Mrever, it is als hard fr this class. Therem Let = hh M W Di be a DA. Decidig if E H is a skeptical explaati fr is D - cmplete. Thus, as i the case f credulus explaatis, recgizig a skeptical explaati is at the secd level f the plymial hierarchy. Hwever, sice this prblems ivlves bth a ad a -hard subtask (as ppsed t ly a -hard e), dig a skeptical explaati resides at the third level. We sketch here the -hardess prf fr Csistecy by a trasfrmati f decidig if a quatied Blea frmula (QBF) = 9X8Y 9ZF is valid (cf. Jhs, 1990] fr a deiti f QBFs). Dee ::a :a :a a j a X Y :F F = hx f:x j x Xg ffg f f F g Di where f is a ew atm. The, has a skeptical explaati i is valid. Hw des this result cmpare t ther mtic lgics, i particular, which mtic lgic has similar cmplexity? We kw that Klige's mderately gruded autepistemic lgic Klige, 1988] ad several ther grud mtic mdal lgics have the same cmplexity Eiter ad Gttlb, 199 Dii et al., 1995] thus, we ca use a therem prver fr such lgics t perfrm abductive reasig frm default theries based skeptical explaatis. 4. Miimal explaatis As metied abve, e is usually iterested i miimal explaatis fr bservatis. The results i Eiter ad Gttlb, 1995] were that the cmplexity f abducti frm classical theries des t icrease if miimal explaatis are used istead f arbitrary explaatis. Hwever, this is t true i fr abducti frm default lgic. Here, checkig miimality f a explaati is a surce f cmplexity, which causes a icrease i cmplexity by e level f the plymial hierarchy. Csider rst credulus explaatis. Checkig miimality f a explaati E has cmplemetary cmplexity f checkig the explaati prperty. Ntice

5 that E is t miimal i fr sme h E, the DA he? fhg M W Di has a credulus sluti hece, it fllws that the prblem is i. O the ther had, recsider (***). Clearly, fhg is a credulus explaati mrever, it is miimal i h is ecessary fr. Thus, -hardess fllws. Nte that recgizig miimal credulus explaatis, which csists i checkig the sluti prperty ad testig miimality, is i D, ad als cmplete fr this class. Thus, this prblem ca be trasfrmed it recgiti f skeptical explaatis fr a certai DA ad vice versa. Due t the cmplexity f miimality checkig, prblem Relevace migrates t the ext level f the plymial hierarchy. Therem 4 Let be a DA based miimal credulus explaatis. The, prblem Relevace is - cmplete, with hardess hldig eve fr rmal. rf. (Sketch) Membership. A guess E fr a miimal credulus explaati fr such that h E ca be veried by tw calls t a racle. Hece, the prblem is i. Hardess. We utlie a reducti frm decidig validity f a QBF = 9X8Y 9ZF. Let s ad q be ew atms, ad dee ::s s^y :q :s q x:x 0 x 0 :x:x0 x 0 :s^:f :q q j x X ::y :y j y Y : Let = hx :X Y fsg X 0 fqg Di. The, e ca shw that s is relevat fr a miimal credulus explaati fr i is valid. Nw let us csider miimal skeptical explaatis. Testig miimality f a skeptical explaati is much mre ivlved tha f a credulus explaati. While the latter has rughly the same cmplexity as testig the explaati prperty, the frmer is harder by e level f the plymial hierarchy. Ituitively, this ca be explaied as fllws. Sice verifyig a credulus explaati E is i, it has a plymial-size \prf" which ca be checked with a N racle i plymial time. Thus, if we ask fr a smaller explaati E 0 E, we ca simultaeusly guess E 0 ad its prf, ad check the prf i plymial time with the N racle. Hwever, verifyig a skeptical explaati E is -hard, ad hece E des t have such a \prf". Here, vericati eeds the full pwer f a racle. Therem 5 Let = hh M W Di be a DA. Decidig if a skeptical explaati E fr is miimal is - cmplete, with hardess hldig eve fr rmal. rf. (Sketch) Membership. A guess fr a smaller skeptical explaati E 0 E ca be veried with tw calls t a racle, ad hece decidig the existece f such a E 0 is i. Csequetly, the prblem is i. Hardess. We describe here a reducti frm decidig whether a QBF = 8X9Y 8ZF is valid. Let s ad q be ew atms, ad dee ::s :s s^x:q q :y y :s::f ^q :F ^q ::y :y j y Y : ::x :x j x X Let = hx fsg fqg Di. Check that E = X fsg is a skeptical explaati fr. Mrever, E is miimal i is valid. Nte that recgizig miimal skeptical explaatis is i, sice the cmplexity f decidig miimality ( ) dmiates the cmplexity f the sluti prperty (\ly" ), ad is als cmplete fr this class. The cmplexity f decidig relevace f a hypthesis icreases by the same amut as testig miimality if skeptical explaatis are used istead f credulus explaatis. I fact, the prblem resides at the furth level f the plymial hierarchy. Therem 6 Let be a DA based miimal skeptical explaatis. The, prblem Relevace is 4 - cmplete, with hardess hldig eve fr rmal. rf. (Sketch) Membership. A guess fr a miimal skeptical explaati E fr such that h E ca be veried with e call t a racle. Hardess. We utlie a reducti frm decidig validity f a QBF = 9R8X9Y 8ZF, which is a extesi t the reducti i the prf f Therem 5. Let as there be s ad q ew atms, ad dee r D1 = D 0 :r^r 00 r^r 00 :r0 ::r^r 00 :r^r 00 j r R where D is the same set f defaults as i the prf f Therem 5. Dee = hh R 00 fqg D1i, where H = R 0 :R 0 X fsg. (Nte that if W wuld be empty, the wuld be idetical t the DA i the prf f Therem 5). It hlds that fr each subset R1 R, the set R1 0 :(R? R1) 0 X fsg is a skeptical explaati fr. Mrever, it ca be shw that s is relevat fr a miimal skeptical explaati fr i is valid. There is well-kw mtic lgic that has similar cmplexity, ad thus e ca t take advatage f therem prvers fr such lgics t perfrm skeptical abducti frm default theries. 4. Tractable cases Frm the practical side, the results frm abve are discuragig, sice abducti frm default theries has eve higher cmplexity tha deducti, i particular fr skeptical explaatis. The reasig tasks suer frm several itermigled surces f cmplexity, whse umber is (at least) the level at the plymial hierarchy. Fr example, Relevace fr = hh M W Di usig miimal skeptical explaatis (cmplete fr 4 ) suers frm the fllwig fur \rthgal" surces f cmplexity: (1.) classical deductive iferece (j=), (.) the umber f extesis f hw E Di, (.) the umber f cadidates E fr a skeptical explaati, ad (4.) the umber f pssible smaller explaatis, where each umber ca be expetial. Fr dealig with abducti frm default theries i practice, we have t d tractable cases r cases where gd algrithms fr hadlig hard prblems like GSAT Selma et al., 199] are applicable. A example f the latter case is credulus abducti frm default theries where all prpsitial frmulas

6 are frm a tractable fragmet f the prpsitial laguage, e.g. Hr frmulas r Krm frmulas (clauses with at mst tw literals). I such a case, classical iferece j= vaishes as surce f cmplexity. I particular, the -cmplete abductive reasig tasks fall back t N. Thus, we ca use e.g. GSAT Selma et al., 199], which prvides a gd heuristics fr slvig N-cmplete prblems, t slve the prblems quickly. Fr tractable cases f default abducti, all surces f cmplexity must be elimiated. I particular, the uderlyig default reasig tasks must be tractable. Kautz ad Selma Kautz ad Selma, 1991] ad Stillma Stillma, 1990] gave a very detailed picture f plymial vs. itractable cases f deductive default reasig. Fr the fllwig tw classes f default theries hw Di, they prved tractability f credulus iferece hw Di `c ` f a sigle literal `: Literal-Hr Kautz ad Selma, 1991]: W is a set f literals ad each default i D is Hr, i.e., f frm, where the a i 's are atms ad ` is a literal. a 1^^a k:` ` Krm-pf-rmal Stillma, 1990]: W is a set f Krm frmulas, ad each default i D is f frm :`1^^`k, `1^^`k where all `i's are literals. A atural geeralizati f the prf i Kautz ad Selma, 1991] yields the fllwig. Lemma 1 Let hw Di be a Literal-Hr default thery, ad let `1 : : : ` be literals. The, decidig hw Di `c `1 ^ ^ ` is plymial. Fr Krm-pf-rmal, such a geeralizati is t evidet as hw Di `c `1^ ^` is N-hard. Hwever, it is pssible fr a small cjucti. I what fllws, we call a set L f literals small i jlj c fr sme xed cstat c. Lemma Let hw Di be Krm-pf-rmal, ad let L = f`1 : : : `kg be a small set f literals. The, decidig hw Di `c `1 ^ ^ `k is plymial. Based these tractable cases f credulus default reasig, we btai tractable cases f credulus default abducti. Similar tractability results fr skeptical default abducti are ulikely, sice the uderlyig skeptical iferece hw Di `s ` is c-n-cmplete i bth cases (cf. Kautz ad Selma, 1991] fr Literal-Hr). Literal-Hr default theries I this case, the mai reasig tasks fr credulus abducti are tractable. Therem 7 Let = hh M W Di be a DA based credulus explaatis ad hw Di Literal-Hr. The, Csistecy, Relevace, ad Necessity are plymial. rf. (Sketch) Cstruct a Literal-Hr T 1 = hw bh:h h ::bh :b h :bh b h where D1i, where D1 = j h H each b h is a ew prpsitial atm. The, it ca be shw that has a explaati i W is csistet ad T 1 `c `1^ ^`k, where M = f`1 : : : `kg. By Lemma 1, this ca be decided i plymial time. Thus, Csistecy is plymial. Relevace ad Necessity ca be easily reduced t Csistecy resp. its cmplemet. Ntice that a plymial algrithm fr dig a credulus explaati (eve ctaiig a give hypthesis), ca be extracted frm the prf. Mrever, there is als a plymial algrithm fr dig a miimal credulus explaati. Ideed, a explaati E fr = hh M W Di is miimal i he? fhg M W Di has explaati fr each h E. Thus, fr as abve, e ca check i plytime whether E is miimal ad, if t, d a smaller explaati E1 E. By repeatig this test, we ca miimize E. Therem 8 Let = hh M W Di be a DA where hw Di is Literal-Hr. The, a miimal credulus explaati fr ca be fud i plymial time. Hwever, Relevace based miimal credulus explaatis fr DAs with Literal-Hr default theries ca be shw t be N-cmplete. Krm-pf-rmal default theries Fr this fragmet, we have the fllwig results. Therem 9 Let = hh M W Di be a DA based credulus explaatis such that M = f`1 : : : `kg is small ad hw Di is Krm-pf-rmal. The, Csistecy, Relevace, ad Necessity are plymial. rf. (Sketch) Cstruct a Krm-pf-rmal default thery T = hw Di, where W = fc h h j h Hg D :ch ch ::ch :ch j h H where each c h is a ew prpsitial atm. The, has a explaati i W is csistet ad T `c `1 ^ ^ `k, which are bth plymial. Csistecy f W ad T `c `1 ^ ^ `k ca be decided i plymial time (cf. Lemma ). Hece, Csistecy is plymial. Sice Relevace ad Necessity ca be easily reduced t Csistecy resp. its cmplemet, these prblems are als plymial. Agai, a plymial time algrithm fr dig a explaati ca be extracted frm the prf. Ufrtuately, Therem 9 ca t be geeralized t a arbitrary set M f literals. I fact, due t the N-hardess f hw Di `c `1 ^ ^ ` fr Krm-pf-rmal hw Di, the prblem is N-hard. Iterestigly, the umber f hyptheses i a miimal credulus explaati is buded by the umber f maifestatis. Ituitively, this is explaied by the fact that always a sigle hypthesis ca explai a maifestati. rpsiti Let E be ay miimal credulus explaati fr = hh M W Di where hw Di is Krm-pfrmal ad M = f`1 : : : `g. The, jej jm j. I particular, fr a sigle maifestati (M = f`g), the miimal explaatis csist f sigle hyptheses, if hyptheses are eeded fr a explaati. A csequece f this characterizati ad Lemma is that all miimal credulus explaatis fr a small set M ca be cmputed by exhaustive testig f all subsets E H with jej jm j i plymial time. Therem 10 Give a DA = hh M W Di where hw Di is Krm-pf-rmal ad M is small, all miimal credulus explaatis fr ca be cmputed i plymial time.

7 Csequetly, als Relevace fr miimal explaatis is plymial if M is small. 5 Cclusi ad further research We prpsed a basic mdel f abducti frm default theries, ad aalyzed its cmputatial cmplexity. Mrever, we have shw that credulus abducti frm the previusly kw classes f Literal-Hr ad Krmpf-rmal default theries is tractable. Besides idetifyig further tractable ad maageable cases f default abducti, the fllwig issues are curretly uder ivestigati. The size f a explaati (cf. eg ad Reggia, 1990]) r, mre geeral, its cst, give by the sum f the predeed csts f its hyptheses, ca be used fr further pruig miimal (i.e., redudat) explaatis. Results fr abducti frm classical theries Eiter ad Gttlb, 1995] suggest usig such explaatis, abducti frm default theries yields cmplete prblems fr the classes k ad k O(lg )] f the plymial hierarchy. Ather issue is default lgic with a uderlyig laguage richer tha a plai prpsitial e. A geeralizati f ur abducti mdel t a prpsitial laguage ver atms p(t 1 : : : t ) where the t i are variables r cstats, is straightfrward here, a istace f a abducti prblem reduces t the prpsitial abducti prblem btaied by replacig frmulas with all grud istaces. Sice the gruded prpsitial versi ca be expetially larger, this leads ituitively t a expetial icrease i cmplexity. Thus, abducti frm default theries i this grud laguage is expected t be cmplete fr the expetial aalgues f k, k etc. Ackwledgmet Nicla Lee has bee partially supprted by the EC-US prject "Deus ex Machia" ad by MURST40% prject "Sistemi frmali e strumeti per basi di dati evlute". Refereces Cadli ad Schaerf, 199] M. Cadli ad M. Schaerf. A Survey f Cmplexity Results fr N-mtic Lgics. Jural f Lgic rgrammig, 17:17{160, 199. Chariak ad McDermtt, 1985] E. Chariak ad. Mc Dermtt. Itrducti t Articial Itelligece Dii et al., 1995] F.M. Dii, D. Nardi, ad R. Rsati. Grud Nmtic Mdal Lgics fr Kwledge Represetati. I rc. Wrld Cgress fr AI (WOCFAI-95), Frthcmig. Eiter ad Gttlb, 199] Thmas Eiter ad Gerg Gttlb. Reasig with arsimius ad Mderately Gruded Expasis. Fudameta Ifrm., 17(1,):1{5, 199. Eiter ad Gttlb, 1995] Thmas Eiter ad Gerg Gttlb. The Cmplexity f Lgic-Based Abducti. Jural f the ACM, Jauary Abstract i rc. STACS-9, LNCS 665, pp. 70{79, 199. Eshghi, 1988] K. Eshghi. Abductive laig with Evet Calculus. I rc. 5th It'l Cf. ad Symp. Lgic rgrammig, pp. 56{ Gttlb, 199] Gerg Gttlb. Cmplexity Results fr Nmtic Lgics. Jural f Lgic ad Cmputati, ():97{45, Jue 199. Hbbs et al., 1988] J. R. Hbbs, M. E. Stickel,. Marti, ad D. Edwards. Iterpretati as Abducti. I rc. 6th Aual Meetig f the Assc. fr Cmputatial Liguistics, pp. 95{10, Bual (NY), Jhs, 1990] D. S. Jhs. A Catalg f Cmplexity Classes. I Hadbk f Theret. Cmp. Sc. A, Jsephs et al., 1987] J.R. Jsephs, B. Chadrasekara, Jr. J. W. Smith, ad M.C. Taer. A Mechaism fr Frmig Cmpsite Explaatry Hyptheses. IEEE Trasactis, TSMC-17:445{454, Kakas ad Macarella, 1990] A.C. Kakas ad. Macarella. Database Updates Thrugh Abducti. I rc. VLDB-90, pp. 650{661, Kautz ad Selma, 1991] H. Kautz ad B. Selma. Hard rblems fr Simple Default Lgics. Articial Itelligece, 49:4{79, Klige, 1988] K. Klige. O the Relatiship betwee Default ad Autepistemic Lgic. Articial Itelligece, 5:4{8, 1988, + 41:115, 1989/90. Klige, 199] K. Klige. Abducti versus clsure i causal theries. Art. Itell., 5:55{7, 199. Marek ad Truszczyski, 199] W. Marek ad M. Truszczyski. Nmtic Lgics. Spriger, 199. Nebel, 1994] B. Nebel. Articial Itelligece: A Cmputatial erspective. Nvember T appear i \Lgic ad Cmputati i AI", G. Brewka ed. Ng ad Mey, 1991] H. T. Ng ad R. J. Mey. A eciet rst-rder Hr clause abducti system based the ATMS. I rc. AAAI-91, pp. 494{499. eg ad Reggia, 1990] Y. eg ad J.A. Reggia. Abductive Iferece Mdels fr Diagstic rblem Slvig le, 1989] D. le. Nrmality ad Faults i Lgic Based Diagsis. I rc. IJCAI-89, pp. 104{110. Reiter, 1980] R. Reiter. A Lgic fr Default Reasig. Articial Itelligece, 1:81{1, Selma ad Levesque, 1990] Bart Selma ad Hectr J. Levesque. Abductive ad Default Reasig: A Cmputatial Cre. I rc. AAAI-90, pp. 4{ 48, July Selma et al., 199] B. Selma, H. Levesque, ad D. Mitchell. A New Methd fr Slvig Hard Satisability rblems. I rc. AAAI-9, pp. 440{446. Stillma, 1990] J. Stillma. It's Nt My Default: The Cmplexity f Membership rblems i Restricted rpsitial Default Lgic. I rc. AAAI-90, pp. 571{579, Stillma, 199] J. Stillma. The Cmplexity f rpsitial Default Lgic. I rc. AAAI-9, pp. 794{799.

Multi-objective Programming Approach for. Fuzzy Linear Programming Problems

Multi-objective Programming Approach for. Fuzzy Linear Programming Problems Applied Mathematical Scieces Vl. 7 03. 37 8-87 HIKARI Ltd www.m-hikari.cm Multi-bective Prgrammig Apprach fr Fuzzy Liear Prgrammig Prblems P. Padia Departmet f Mathematics Schl f Advaced Scieces VIT Uiversity

More information

Chapter 3.1: Polynomial Functions

Chapter 3.1: Polynomial Functions Ntes 3.1: Ply Fucs Chapter 3.1: Plymial Fuctis I Algebra I ad Algebra II, yu ecutered sme very famus plymial fuctis. I this secti, yu will meet may ther members f the plymial family, what sets them apart

More information

Semantics and Complexity of Abduction from Default Theories

Semantics and Complexity of Abduction from Default Theories Semantics and Complexity of Abduction from Default Theories (Extended abstract*) Thomas Eiter, Georg Gottlob Christian Doppler Lab for Expert Systems Technical University of Vienna Paniglgasse 16, A-1040

More information

is caused by a latet utreated frm f syphilis, althugh the prbability that latet utreated syphilis leads t paresis is ly 25%. Nte that the directialiti

is caused by a latet utreated frm f syphilis, althugh the prbability that latet utreated syphilis leads t paresis is ly 25%. Nte that the directialiti Tempral Reasig with i-abducti Secd Draft Marc Deecker Kristf Va Belleghem Departmet f Cmputer Sciece, K.U.Leuve, Celestijelaa 200A, B-3001 Heverlee, Belgium. e-mail : marcd@cs.kuleuve.ac.be Abstract Abducti

More information

D.S.G. POLLOCK: TOPICS IN TIME-SERIES ANALYSIS STATISTICAL FOURIER ANALYSIS

D.S.G. POLLOCK: TOPICS IN TIME-SERIES ANALYSIS STATISTICAL FOURIER ANALYSIS STATISTICAL FOURIER ANALYSIS The Furier Represetati f a Sequece Accrdig t the basic result f Furier aalysis, it is always pssible t apprximate a arbitrary aalytic fucti defied ver a fiite iterval f the

More information

Mean residual life of coherent systems consisting of multiple types of dependent components

Mean residual life of coherent systems consisting of multiple types of dependent components Mea residual life f cheret systems csistig f multiple types f depedet cmpets Serka Eryilmaz, Frak P.A. Cle y ad Tahai Cle-Maturi z February 20, 208 Abstract Mea residual life is a useful dyamic characteristic

More information

ENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ]

ENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ] ENGI 441 Cetral Limit Therem Page 11-01 Cetral Limit Therem [Navidi, secti 4.11; Devre sectis 5.3-5.4] If X i is t rmally distributed, but E X i, V X i ad is large (apprximately 30 r mre), the, t a gd

More information

Alternative Approaches to Default Logic. Fachgebiet Intellektik. Technische Hochschule Darmstadt. Alexanderstrae 10. W. Ken Jackson. Burnaby, B.C.

Alternative Approaches to Default Logic. Fachgebiet Intellektik. Technische Hochschule Darmstadt. Alexanderstrae 10. W. Ken Jackson. Burnaby, B.C. Alterative Appraches t Default Lgic James P. Delgrade Schl f Cmputig Sciece Sim Fraser Uiversity Buraby, B.C. Caada V5A 1S6 Trste Schaub Fachgebiet Itellektik Techische Hchschule Darmstadt Alexaderstrae

More information

A New Method for Finding an Optimal Solution. of Fully Interval Integer Transportation Problems

A New Method for Finding an Optimal Solution. of Fully Interval Integer Transportation Problems Applied Matheatical Scieces, Vl. 4, 200,. 37, 89-830 A New Methd fr Fidig a Optial Sluti f Fully Iterval Iteger Trasprtati Prbles P. Padia ad G. Nataraja Departet f Matheatics, Schl f Advaced Scieces,

More information

Fourier Method for Solving Transportation. Problems with Mixed Constraints

Fourier Method for Solving Transportation. Problems with Mixed Constraints It. J. Ctemp. Math. Scieces, Vl. 5, 200,. 28, 385-395 Furier Methd fr Slvig Trasprtati Prblems with Mixed Cstraits P. Padia ad G. Nataraja Departmet f Mathematics, Schl f Advaced Scieces V I T Uiversity,

More information

Fourier Series & Fourier Transforms

Fourier Series & Fourier Transforms Experimet 1 Furier Series & Furier Trasfrms MATLAB Simulati Objectives Furier aalysis plays a imprtat rle i cmmuicati thery. The mai bjectives f this experimet are: 1) T gai a gd uderstadig ad practice

More information

Solutions. Definitions pertaining to solutions

Solutions. Definitions pertaining to solutions Slutis Defiitis pertaiig t slutis Slute is the substace that is disslved. It is usually preset i the smaller amut. Slvet is the substace that des the disslvig. It is usually preset i the larger amut. Slubility

More information

Quantum Mechanics for Scientists and Engineers. David Miller

Quantum Mechanics for Scientists and Engineers. David Miller Quatum Mechaics fr Scietists ad Egieers David Miller Time-depedet perturbati thery Time-depedet perturbati thery Time-depedet perturbati basics Time-depedet perturbati thery Fr time-depedet prblems csider

More information

ENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ]

ENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ] ENGI 441 Cetral Limit Therem Page 11-01 Cetral Limit Therem [Navidi, secti 4.11; Devre sectis 5.3-5.4] If X i is t rmally distributed, but E X i, V X i ad is large (apprximately 30 r mre), the, t a gd

More information

Intermediate Division Solutions

Intermediate Division Solutions Itermediate Divisi Slutis 1. Cmpute the largest 4-digit umber f the frm ABBA which is exactly divisible by 7. Sluti ABBA 1000A + 100B +10B+A 1001A + 110B 1001 is divisible by 7 (1001 7 143), s 1001A is

More information

Author. Introduction. Author. o Asmir Tobudic. ISE 599 Computational Modeling of Expressive Performance

Author. Introduction. Author. o Asmir Tobudic. ISE 599 Computational Modeling of Expressive Performance ISE 599 Cmputatial Mdelig f Expressive Perfrmace Playig Mzart by Aalgy: Learig Multi-level Timig ad Dyamics Strategies by Gerhard Widmer ad Asmir Tbudic Preseted by Tsug-Ha (Rbert) Chiag April 5, 2006

More information

[1 & α(t & T 1. ' ρ 1

[1 & α(t & T 1. ' ρ 1 NAME 89.304 - IGNEOUS & METAMORPHIC PETROLOGY DENSITY & VISCOSITY OF MAGMAS I. Desity The desity (mass/vlume) f a magma is a imprtat parameter which plays a rle i a umber f aspects f magma behavir ad evluti.

More information

K [f(t)] 2 [ (st) /2 K A GENERALIZED MEIJER TRANSFORMATION. Ku(z) ()x) t -)-I e. K(z) r( + ) () (t 2 I) -1/2 e -zt dt, G. L. N. RAO L.

K [f(t)] 2 [ (st) /2 K A GENERALIZED MEIJER TRANSFORMATION. Ku(z) ()x) t -)-I e. K(z) r( + ) () (t 2 I) -1/2 e -zt dt, G. L. N. RAO L. Iterat. J. Math. & Math. Scl. Vl. 8 N. 2 (1985) 359-365 359 A GENERALIZED MEIJER TRANSFORMATION G. L. N. RAO Departmet f Mathematics Jamshedpur C-perative Cllege f the Rachi Uiversity Jamshedpur, Idia

More information

The Complexity of Translation Membership for Macro Tree Transducers

The Complexity of Translation Membership for Macro Tree Transducers The Cmplexity f Traslati Membership fr Macr Tree Trasducers Kazuhir Iaba The Uiversity f Tky kiaba@is.s.u-tky.ac.jp Sebastia Maeth NICTA ad Uiversity f New Suth Wales sebastia.maeth@icta.cm.au ABSTRACT

More information

ALE 26. Equilibria for Cell Reactions. What happens to the cell potential as the reaction proceeds over time?

ALE 26. Equilibria for Cell Reactions. What happens to the cell potential as the reaction proceeds over time? Name Chem 163 Secti: Team Number: AL 26. quilibria fr Cell Reactis (Referece: 21.4 Silberberg 5 th editi) What happes t the ptetial as the reacti prceeds ver time? The Mdel: Basis fr the Nerst quati Previusly,

More information

Grade 3 Mathematics Course Syllabus Prince George s County Public Schools

Grade 3 Mathematics Course Syllabus Prince George s County Public Schools Ctet Grade 3 Mathematics Curse Syllabus Price Gerge s Cuty Public Schls Prerequisites: Ne Curse Descripti: I Grade 3, istructial time shuld fcus fur critical areas: (1) develpig uderstadig f multiplicati

More information

BIO752: Advanced Methods in Biostatistics, II TERM 2, 2010 T. A. Louis. BIO 752: MIDTERM EXAMINATION: ANSWERS 30 November 2010

BIO752: Advanced Methods in Biostatistics, II TERM 2, 2010 T. A. Louis. BIO 752: MIDTERM EXAMINATION: ANSWERS 30 November 2010 BIO752: Advaced Methds i Bistatistics, II TERM 2, 2010 T. A. Luis BIO 752: MIDTERM EXAMINATION: ANSWERS 30 Nvember 2010 Questi #1 (15 pits): Let X ad Y be radm variables with a jit distributi ad assume

More information

MATH Midterm Examination Victor Matveev October 26, 2016

MATH Midterm Examination Victor Matveev October 26, 2016 MATH 33- Midterm Examiati Victr Matveev Octber 6, 6. (5pts, mi) Suppse f(x) equals si x the iterval < x < (=), ad is a eve peridic extesi f this fucti t the rest f the real lie. Fid the csie series fr

More information

5.1 Two-Step Conditional Density Estimator

5.1 Two-Step Conditional Density Estimator 5.1 Tw-Step Cditial Desity Estimatr We ca write y = g(x) + e where g(x) is the cditial mea fucti ad e is the regressi errr. Let f e (e j x) be the cditial desity f e give X = x: The the cditial desity

More information

AP Statistics Notes Unit Eight: Introduction to Inference

AP Statistics Notes Unit Eight: Introduction to Inference AP Statistics Ntes Uit Eight: Itrducti t Iferece Syllabus Objectives: 4.1 The studet will estimate ppulati parameters ad margis f errrs fr meas. 4.2 The studet will discuss the prperties f pit estimatrs,

More information

Unifying the Derivations for. the Akaike and Corrected Akaike. Information Criteria. from Statistics & Probability Letters,

Unifying the Derivations for. the Akaike and Corrected Akaike. Information Criteria. from Statistics & Probability Letters, Uifyig the Derivatis fr the Akaike ad Crrected Akaike Ifrmati Criteria frm Statistics & Prbability Letters, Vlume 33, 1997, pages 201{208. by Jseph E. Cavaaugh Departmet f Statistics, Uiversity f Missuri,

More information

A Study on Estimation of Lifetime Distribution with Covariates Under Misspecification

A Study on Estimation of Lifetime Distribution with Covariates Under Misspecification Prceedigs f the Wrld Cgress Egieerig ad Cmputer Sciece 2015 Vl II, Octber 21-23, 2015, Sa Fracisc, USA A Study Estimati f Lifetime Distributi with Cvariates Uder Misspecificati Masahir Ykyama, Member,

More information

Markov processes and the Kolmogorov equations

Markov processes and the Kolmogorov equations Chapter 6 Markv prcesses ad the Klmgrv equatis 6. Stchastic Differetial Equatis Csider the stchastic differetial equati: dx(t) =a(t X(t)) dt + (t X(t)) db(t): (SDE) Here a(t x) ad (t x) are give fuctis,

More information

Active redundancy allocation in systems. R. Romera; J. Valdés; R. Zequeira*

Active redundancy allocation in systems. R. Romera; J. Valdés; R. Zequeira* Wrkig Paper -6 (3) Statistics ad Ecmetrics Series March Departamet de Estadística y Ecmetría Uiversidad Carls III de Madrid Calle Madrid, 6 893 Getafe (Spai) Fax (34) 9 64-98-49 Active redudacy allcati

More information

A Hartree-Fock Calculation of the Water Molecule

A Hartree-Fock Calculation of the Water Molecule Chemistry 460 Fall 2017 Dr. Jea M. Stadard Nvember 29, 2017 A Hartree-Fck Calculati f the Water Mlecule Itrducti A example Hartree-Fck calculati f the water mlecule will be preseted. I this case, the water

More information

The Excel FFT Function v1.1 P. T. Debevec February 12, The discrete Fourier transform may be used to identify periodic structures in time ht.

The Excel FFT Function v1.1 P. T. Debevec February 12, The discrete Fourier transform may be used to identify periodic structures in time ht. The Excel FFT Fucti v P T Debevec February 2, 26 The discrete Furier trasfrm may be used t idetify peridic structures i time ht series data Suppse that a physical prcess is represeted by the fucti f time,

More information

Function representation of a noncommutative uniform algebra

Function representation of a noncommutative uniform algebra Fucti represetati f a cmmutative uifrm algebra Krzysztf Jarsz Abstract. We cstruct a Gelfad type represetati f a real cmmutative Baach algebra A satisfyig f 2 = kfk 2, fr all f 2 A:. Itrducti A uifrm algebra

More information

Ch. 1 Introduction to Estimation 1/15

Ch. 1 Introduction to Estimation 1/15 Ch. Itrducti t stimati /5 ample stimati Prblem: DSB R S f M f s f f f ; f, φ m tcsπf t + φ t f lectrics dds ise wt usually white BPF & mp t s t + w t st. lg. f & φ X udi mp cs π f + φ t Oscillatr w/ f

More information

Recovery of Third Order Tensors via Convex Optimization

Recovery of Third Order Tensors via Convex Optimization Recvery f Third Order Tesrs via Cvex Optimizati Hlger Rauhut RWTH Aache Uiversity Lehrstuhl C für Mathematik (Aalysis) Ptdriesch 10 5056 Aache Germay Email: rauhut@mathcrwth-aachede Željka Stjaac RWTH

More information

UNIVERSITY OF TECHNOLOGY. Department of Mathematics PROBABILITY THEORY, STATISTICS AND OPERATIONS RESEARCH GROUP. Memorandum COSOR 76-10

UNIVERSITY OF TECHNOLOGY. Department of Mathematics PROBABILITY THEORY, STATISTICS AND OPERATIONS RESEARCH GROUP. Memorandum COSOR 76-10 EI~~HOVEN UNIVERSITY OF TECHNOLOGY Departmet f Mathematics PROBABILITY THEORY, STATISTICS AND OPERATIONS RESEARCH GROUP Memradum COSOR 76-10 O a class f embedded Markv prcesses ad recurrece by F.H. Sims

More information

ON FREE RING EXTENSIONS OF DEGREE N

ON FREE RING EXTENSIONS OF DEGREE N I terat. J. Math. & Mah. Sci. Vl. 4 N. 4 (1981) 703-709 703 ON FREE RING EXTENSIONS OF DEGREE N GEORGE SZETO Mathematics Departmet Bradley Uiversity Peria, Illiis 61625 U.S.A. (Received Jue 25, 1980) ABSTRACT.

More information

Axial Temperature Distribution in W-Tailored Optical Fibers

Axial Temperature Distribution in W-Tailored Optical Fibers Axial Temperature Distributi i W-Tailred Optical ibers Mhamed I. Shehata (m.ismail34@yah.cm), Mustafa H. Aly(drmsaly@gmail.cm) OSA Member, ad M. B. Saleh (Basheer@aast.edu) Arab Academy fr Sciece, Techlgy

More information

Canonical Sets of Horn Clauses. Nachum Dershowitz. University of Illinois West Springeld Avenue. Urbana, IL 61801, U.S.A.

Canonical Sets of Horn Clauses. Nachum Dershowitz. University of Illinois West Springeld Avenue. Urbana, IL 61801, U.S.A. Caical Sets f Hr Clauses Nachum Dershwitz Departmet f Cmputer Sciece Uiversity f Illiis 1304 West Sprigeld Aveue Urbaa, IL 61801, U.S.A. email: achum@cs.uiuc.edu 1 Backgrud Rewrite rules are rieted equatis

More information

Directional Duality Theory

Directional Duality Theory Suther Illiis Uiversity Carbdale OpeSIUC Discussi Papers Departmet f Ecmics 2004 Directial Duality Thery Daiel Primt Suther Illiis Uiversity Carbdale Rlf Fare Oreg State Uiversity Fllw this ad additial

More information

Physical Chemistry Laboratory I CHEM 445 Experiment 2 Partial Molar Volume (Revised, 01/13/03)

Physical Chemistry Laboratory I CHEM 445 Experiment 2 Partial Molar Volume (Revised, 01/13/03) Physical Chemistry Labratry I CHEM 445 Experimet Partial Mlar lume (Revised, 0/3/03) lume is, t a gd apprximati, a additive prperty. Certaily this apprximati is used i preparig slutis whse ccetratis are

More information

IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 12, December

IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 12, December IJISET - Iteratial Jural f Ivative Sciece, Egieerig & Techlgy, Vl Issue, December 5 wwwijisetcm ISSN 48 7968 Psirmal ad * Pararmal mpsiti Operatrs the Fc Space Abstract Dr N Sivamai Departmet f athematics,

More information

Study of Energy Eigenvalues of Three Dimensional. Quantum Wires with Variable Cross Section

Study of Energy Eigenvalues of Three Dimensional. Quantum Wires with Variable Cross Section Adv. Studies Ther. Phys. Vl. 3 009. 5 3-0 Study f Eergy Eigevalues f Three Dimesial Quatum Wires with Variale Crss Secti M.. Sltai Erde Msa Departmet f physics Islamic Aad Uiversity Share-ey rach Ira alrevahidi@yah.cm

More information

Claude Elysée Lobry Université de Nice, Faculté des Sciences, parc Valrose, NICE, France.

Claude Elysée Lobry Université de Nice, Faculté des Sciences, parc Valrose, NICE, France. CHAOS AND CELLULAR AUTOMATA Claude Elysée Lbry Uiversité de Nice, Faculté des Scieces, parc Valrse, 06000 NICE, Frace. Keywrds: Chas, bifurcati, cellularautmata, cmputersimulatis, dyamical system, ifectius

More information

5.80 Small-Molecule Spectroscopy and Dynamics

5.80 Small-Molecule Spectroscopy and Dynamics MIT OpeCurseWare http://cw.mit.edu 5.8 Small-Mlecule Spectrscpy ad Dyamics Fall 8 Fr ifrmati abut citig these materials r ur Terms f Use, visit: http://cw.mit.edu/terms. 5.8 Lecture #33 Fall, 8 Page f

More information

Copyright 1978, by the author(s). All rights reserved.

Copyright 1978, by the author(s). All rights reserved. Cpyright 1978, by the authr(s). All rights reserved. Permissi t make digital r hard cpies f all r part f this wrk fr persal r classrm use is grated withut fee prvided that cpies are t made r distributed

More information

Review for cumulative test

Review for cumulative test Hrs Math 3 review prblems Jauary, 01 cumulative: Chapters 1- page 1 Review fr cumulative test O Mday, Jauary 7, Hrs Math 3 will have a curse-wide cumulative test cverig Chapters 1-. Yu ca expect the test

More information

TEST TUBE SYSTEMS WITH CUTTING/RECOMBINATION OPERATIONS Rudolf FREUND Institut fur Computersprachen, Technische Universitat Wien Resselgasse 3, 1040 W

TEST TUBE SYSTEMS WITH CUTTING/RECOMBINATION OPERATIONS Rudolf FREUND Institut fur Computersprachen, Technische Universitat Wien Resselgasse 3, 1040 W TEST TUBE SYSTEMS WITH CUTTING/RECOMBINATION OPERATIONS Rudlf FREUND Istitut fur Cmputersprache, Techische Uiversitat Wie Resselgasse 3, 1040 Wie, Austria email: rudi@lgic.tuwie.ac.at Erzsebet CSUHAJ-VARJ

More information

Full algebra of generalized functions and non-standard asymptotic analysis

Full algebra of generalized functions and non-standard asymptotic analysis Full algebra f geeralized fuctis ad -stadard asympttic aalysis Tdr D. Tdrv Has Veraeve Abstract We cstruct a algebra f geeralized fuctis edwed with a caical embeddig f the space f Schwartz distributis.

More information

x 2 x 3 x b 0, then a, b, c log x 1 log z log x log y 1 logb log a dy 4. dx As tangent is perpendicular to the x axis, slope

x 2 x 3 x b 0, then a, b, c log x 1 log z log x log y 1 logb log a dy 4. dx As tangent is perpendicular to the x axis, slope The agle betwee the tagets draw t the parabla y = frm the pit (-,) 5 9 6 Here give pit lies the directri, hece the agle betwee the tagets frm that pit right agle Ratig :EASY The umber f values f c such

More information

are specified , are linearly independent Otherwise, they are linearly dependent, and one is expressed by a linear combination of the others

are specified , are linearly independent Otherwise, they are linearly dependent, and one is expressed by a linear combination of the others Chater 3. Higher Order Liear ODEs Kreyszig by YHLee;4; 3-3. Hmgeeus Liear ODEs The stadard frm f the th rder liear ODE ( ) ( ) = : hmgeeus if r( ) = y y y y r Hmgeeus Liear ODE: Suersiti Pricile, Geeral

More information

Lecture 21: Signal Subspaces and Sparsity

Lecture 21: Signal Subspaces and Sparsity ECE 830 Fall 00 Statistical Sigal Prcessig istructr: R. Nwak Lecture : Sigal Subspaces ad Sparsity Sigal Subspaces ad Sparsity Recall the classical liear sigal mdel: X = H + w, w N(0, where S = H, is a

More information

Design and Implementation of Cosine Transforms Employing a CORDIC Processor

Design and Implementation of Cosine Transforms Employing a CORDIC Processor C16 1 Desig ad Implemetati f Csie Trasfrms Emplyig a CORDIC Prcessr Sharaf El-Di El-Nahas, Ammar Mttie Al Hsaiy, Magdy M. Saeb Arab Academy fr Sciece ad Techlgy, Schl f Egieerig, Alexadria, EGYPT ABSTRACT

More information

CHAPTER 24: INFERENCE IN REGRESSION. Chapter 24: Make inferences about the population from which the sample data came.

CHAPTER 24: INFERENCE IN REGRESSION. Chapter 24: Make inferences about the population from which the sample data came. MATH 1342 Ch. 24 April 25 and 27, 2013 Page 1 f 5 CHAPTER 24: INFERENCE IN REGRESSION Chapters 4 and 5: Relatinships between tw quantitative variables. Be able t Make a graph (scatterplt) Summarize the

More information

, the random variable. and a sample size over the y-values 0:1:10.

, the random variable. and a sample size over the y-values 0:1:10. Lecture 3 (4//9) 000 HW PROBLEM 3(5pts) The estimatr i (c) f PROBLEM, p 000, where { } ~ iid bimial(,, is 000 e f the mst ppular statistics It is the estimatr f the ppulati prprti I PROBLEM we used simulatis

More information

General Chemistry 1 (CHEM1141) Shawnee State University Fall 2016

General Chemistry 1 (CHEM1141) Shawnee State University Fall 2016 Geeral Chemistry 1 (CHEM1141) Shawee State Uiversity Fall 2016 September 23, 2016 Name E x a m # I C Please write yur full ame, ad the exam versi (IC) that yu have the scatr sheet! Please 0 check the bx

More information

Examination No. 3 - Tuesday, Nov. 15

Examination No. 3 - Tuesday, Nov. 15 NAME (lease rit) SOLUTIONS ECE 35 - DEVICE ELECTRONICS Fall Semester 005 Examiati N 3 - Tuesday, Nv 5 3 4 5 The time fr examiati is hr 5 mi Studets are allwed t use 3 sheets f tes Please shw yur wrk, artial

More information

WEST VIRGINIA UNIVERSITY

WEST VIRGINIA UNIVERSITY WEST VIRGINIA UNIVERSITY PLASMA PHYSICS GROUP INTERNAL REPORT PL - 045 Mea Optical epth ad Optical Escape Factr fr Helium Trasitis i Helic Plasmas R.F. Bivi Nvember 000 Revised March 00 TABLE OF CONTENT.0

More information

Modelling of Clock Behaviour. Don Percival. Applied Physics Laboratory University of Washington Seattle, Washington, USA

Modelling of Clock Behaviour. Don Percival. Applied Physics Laboratory University of Washington Seattle, Washington, USA Mdelling f Clck Behaviur Dn Percival Applied Physics Labratry University f Washingtn Seattle, Washingtn, USA verheads and paper fr talk available at http://faculty.washingtn.edu/dbp/talks.html 1 Overview

More information

Every gas consists of a large number of small particles called molecules moving with very high velocities in all possible directions.

Every gas consists of a large number of small particles called molecules moving with very high velocities in all possible directions. Kietic thery f gases ( Kietic thery was develped by Berlli, Jle, Clasis, axwell ad Bltzma etc. ad represets dyamic particle r micrscpic mdel fr differet gases sice it thrws light the behir f the particles

More information

Public Key Cryptography. Tim van der Horst & Kent Seamons

Public Key Cryptography. Tim van der Horst & Kent Seamons Public Key Cryptgraphy Tim van der Hrst & Kent Seamns Last Updated: Oct 5, 2017 Asymmetric Encryptin Why Public Key Crypt is Cl Has a linear slutin t the key distributin prblem Symmetric crypt has an expnential

More information

Solutions to Midterm II. of the following equation consistent with the boundary condition stated u. y u x y

Solutions to Midterm II. of the following equation consistent with the boundary condition stated u. y u x y Sltis t Midterm II Prblem : (pts) Fid the mst geeral slti ( f the fllwig eqati csistet with the bdary cditi stated y 3 y the lie y () Slti : Sice the system () is liear the slti is give as a sperpsiti

More information

Super-efficiency Models, Part II

Super-efficiency Models, Part II Super-efficiec Mdels, Part II Emilia Niskae The 4th f Nvember S steemiaalsi Ctets. Etesis t Variable Returs-t-Scale (0.4) S steemiaalsi Radial Super-efficiec Case Prblems with Radial Super-efficiec Case

More information

Efficient Processing of Continuous Reverse k Nearest Neighbor on Moving Objects in Road Networks

Efficient Processing of Continuous Reverse k Nearest Neighbor on Moving Objects in Road Networks Iteratial Jural f Ge-Ifrmati Article Efficiet Prcessig f Ctiuus Reverse k Nearest Neighbr Mvig Objects i Rad Netwrks Muhammad Attique, Hyug-Ju Ch, Rize Ji ad Tae-Su Chug, * Departmet f Cmputer Egieerig,

More information

The generalized marginal rate of substitution

The generalized marginal rate of substitution Jural f Mathematical Ecmics 31 1999 553 560 The geeralized margial rate f substituti M Besada, C Vazuez ) Facultade de Ecmicas, UiÕersidade de Vig, Aptd 874, 3600 Vig, Spai Received 31 May 1995; accepted

More information

Aligning Anatomy Ontologies in the Ontology Alignment Evaluation Initiative

Aligning Anatomy Ontologies in the Ontology Alignment Evaluation Initiative Aligig Aatmy Otlgies i the Otlgy Aligmet Evaluati Iitiative Patrick Lambrix, Qiag Liu, He Ta Departmet f Cmputer ad Ifrmati Sciece Liköpigs uiversitet 581 83 Liköpig, Swede Abstract I recet years may tlgies

More information

Identical Particles. We would like to move from the quantum theory of hydrogen to that for the rest of the periodic table

Identical Particles. We would like to move from the quantum theory of hydrogen to that for the rest of the periodic table We wuld like t ve fr the quatu thery f hydrge t that fr the rest f the peridic table Oe electr at t ultielectr ats This is cplicated by the iteracti f the electrs with each ther ad by the fact that the

More information

MATHEMATICS 9740/01 Paper 1 14 Sep hours

MATHEMATICS 9740/01 Paper 1 14 Sep hours Cadidate Name: Class: JC PRELIMINARY EXAM Higher MATHEMATICS 9740/0 Paper 4 Sep 06 3 hurs Additial Materials: Cver page Aswer papers List f Frmulae (MF5) READ THESE INSTRUCTIONS FIRST Write yur full ame

More information

Partial-Sum Queries in OLAP Data Cubes Using Covering Codes

Partial-Sum Queries in OLAP Data Cubes Using Covering Codes 326 IEEE TRANSACTIONS ON COMPUTERS, VOL. 47, NO. 2, DECEMBER 998 Partial-Sum Queries i OLAP Data Cubes Usig Cverig Cdes Chig-Tie H, Member, IEEE, Jehshua Bruck, Seir Member, IEEE, ad Rakesh Agrawal, Seir

More information

ON THE M 3 M 1 QUESTION

ON THE M 3 M 1 QUESTION Vlume 5, 1980 Pages 77 104 http://tplgy.aubur.edu/tp/ ON THE M 3 M 1 QUESTION by Gary Gruehage Tplgy Prceedigs Web: http://tplgy.aubur.edu/tp/ Mail: Tplgy Prceedigs Departmet f Mathematics & Statistics

More information

the legitimate cmmuicatrs, called Alice ad Bb, ad the adversary (which may therwise iitiate a cversati with Alice pretedig t be Bb). We list sme ppula

the legitimate cmmuicatrs, called Alice ad Bb, ad the adversary (which may therwise iitiate a cversati with Alice pretedig t be Bb). We list sme ppula Sessi-Key Geerati usig Huma Passwrds Oly Oded Gldreich? ad Yehuda Lidell Departmet f Cmputer Sciece ad Applied Math, Weizma Istitute f Sciece, Rehvt, Israel. fded,lidellg@wisdm.weizma.ac.il Abstract. We

More information

Weathering. Title: Chemical and Mechanical Weathering. Grade Level: Subject/Content: Earth and Space Science

Weathering. Title: Chemical and Mechanical Weathering. Grade Level: Subject/Content: Earth and Space Science Weathering Title: Chemical and Mechanical Weathering Grade Level: 9-12 Subject/Cntent: Earth and Space Science Summary f Lessn: Students will test hw chemical and mechanical weathering can affect a rck

More information

Gusztav Morvai. Hungarian Academy of Sciences Goldmann Gyorgy ter 3, April 22, 1998

Gusztav Morvai. Hungarian Academy of Sciences Goldmann Gyorgy ter 3, April 22, 1998 A simple radmized algrithm fr csistet sequetial predicti f ergdic time series Laszl Gyr Departmet f Cmputer Sciece ad Ifrmati Thery Techical Uiversity f Budapest 5 Stczek u., Budapest, Hugary gyrfi@if.bme.hu

More information

Control Systems. Controllability and Observability (Chapter 6)

Control Systems. Controllability and Observability (Chapter 6) 6.53 trl Systems trllaility ad Oservaility (hapter 6) Geeral Framewrk i State-Spae pprah Give a LTI system: x x u; y x (*) The system might e ustale r des t meet the required perfrmae spe. Hw a we imprve

More information

ON THE FONTAINE-MAZUR CONJECTURE FOR NUMBER FIELDS AND AN ANALOGUE FOR FUNCTION FIELDS JOSHUA BRANDON HOLDEN

ON THE FONTAINE-MAZUR CONJECTURE FOR NUMBER FIELDS AND AN ANALOGUE FOR FUNCTION FIELDS JOSHUA BRANDON HOLDEN ON THE FONTAINE-MAZUR CONJECTURE FOR NUMBER FIELDS AND AN ANALOGUE FOR FUNCTION FIELDS JOSHUA BRANDON HOLDEN Abstract. The Ftaie-Mazur Cjecture fr umber elds predicts that iite `-adic aalytic rups cat

More information

THE MATRIX VERSION FOR THE MULTIVARIABLE HUMBERT POLYNOMIALS

THE MATRIX VERSION FOR THE MULTIVARIABLE HUMBERT POLYNOMIALS Misklc Mathematical Ntes HU ISSN 1787-2405 Vl. 13 (2012), N. 2, pp. 197 208 THE MATRI VERSION FOR THE MULTIVARIABLE HUMBERT POLYNOMIALS RABİA AKTAŞ, BAYRAM ÇEKIM, AN RECEP ŞAHI Received 4 May, 2011 Abstract.

More information

On the affine nonlinearity in circuit theory

On the affine nonlinearity in circuit theory O the affie liearity i circuit thery Emauel Gluski The Kieret Cllege the Sea f Galilee; ad Ort Braude Cllege (Carmiel), Israel. gluski@ee.bgu.ac.il; http://www.ee.bgu.ac.il/~gluski/ E. Gluski, O the affie

More information

Hypothesis Tests for One Population Mean

Hypothesis Tests for One Population Mean Hypthesis Tests fr One Ppulatin Mean Chapter 9 Ala Abdelbaki Objective Objective: T estimate the value f ne ppulatin mean Inferential statistics using statistics in rder t estimate parameters We will be

More information

A proposition is a statement that can be either true (T) or false (F), (but not both).

A proposition is a statement that can be either true (T) or false (F), (but not both). 400 lecture nte #1 [Ch 2, 3] Lgic and Prfs 1.1 Prpsitins (Prpsitinal Lgic) A prpsitin is a statement that can be either true (T) r false (F), (but nt bth). "The earth is flat." -- F "March has 31 days."

More information

Comparative analysis of bayesian control chart estimation and conventional multivariate control chart

Comparative analysis of bayesian control chart estimation and conventional multivariate control chart America Jural f Theretical ad Applied Statistics 3; ( : 7- ublished lie Jauary, 3 (http://www.sciecepublishiggrup.cm//atas di:.648/.atas.3. Cmparative aalysis f bayesia ctrl chart estimati ad cvetial multivariate

More information

E o and the equilibrium constant, K

E o and the equilibrium constant, K lectrchemical measuremets (Ch -5 t 6). T state the relati betwee ad K. (D x -b, -). Frm galvaic cell vltage measuremet (a) K sp (D xercise -8, -) (b) K sp ad γ (D xercise -9) (c) K a (D xercise -G, -6)

More information

1. Itrducti Let X fx(t) t 0g be a symmetric stable prcess f idex, with X(0) 0. That is, X has idepedet ad statiary icremets, with characteristic fucti

1. Itrducti Let X fx(t) t 0g be a symmetric stable prcess f idex, with X(0) 0. That is, X has idepedet ad statiary icremets, with characteristic fucti The mst visited sites f symmetric stable prcesses by Richard F. Bass 1, Nathalie Eisebaum ad Zha Shi Uiversity f Cecticut, Uiversite aris VI ad Uiversite aris VI Summary. Let X be a symmetric stable prcess

More information

An S-type upper bound for the largest singular value of nonnegative rectangular tensors

An S-type upper bound for the largest singular value of nonnegative rectangular tensors Ope Mat. 06 4 95 933 Ope Matematics Ope Access Researc Article Jiaxig Za* ad Caili Sag A S-type upper bud r te largest sigular value egative rectagular tesrs DOI 0.55/mat-06-0085 Received August 3, 06

More information

Efficient Static Analysis of XML Paths and Types

Efficient Static Analysis of XML Paths and Types Efficiet Static Aalysis f XML Paths ad Types Pierre Geevès, Nabil Layaïda, Ala Schmitt T cite this versi: Pierre Geevès, Nabil Layaïda, Ala Schmitt Efficiet Static Aalysis f XML Paths ad Types Prceedigs

More information

The generation of successive approximation methods for Markov decision processes by using stopping times

The generation of successive approximation methods for Markov decision processes by using stopping times The geerati f successive apprximati methds fr Markv decisi prcesses by usig stppig times Citati fr published versi (APA): va Nue, J. A. E. E., & Wessels, J. (1976). The geerati f successive apprximati

More information

Distributions, spatial statistics and a Bayesian perspective

Distributions, spatial statistics and a Bayesian perspective Distributins, spatial statistics and a Bayesian perspective Dug Nychka Natinal Center fr Atmspheric Research Distributins and densities Cnditinal distributins and Bayes Thm Bivariate nrmal Spatial statistics

More information

Graph Expansion and the Unique Games Conjecture

Graph Expansion and the Unique Games Conjecture Graph xpasi ad the ique Games Cjecture rasad Raghavedra MSR New glad Cambridge, MA David Steurer ricet iversity ricet, NJ ABSTRACT The edge expasi f a subset f vertices S V i a graph G measures the fracti

More information

CS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007

CS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007 CS 477/677 Analysis f Algrithms Fall 2007 Dr. Gerge Bebis Curse Prject Due Date: 11/29/2007 Part1: Cmparisn f Srting Algrithms (70% f the prject grade) The bjective f the first part f the assignment is

More information

READING STATECHART DIAGRAMS

READING STATECHART DIAGRAMS READING STATECHART DIAGRAMS Figure 4.48 A Statechart diagram with events The diagram in Figure 4.48 shws all states that the bject plane can be in during the curse f its life. Furthermre, it shws the pssible

More information

I.S. 239 Mark Twain. Grade 7 Mathematics Spring Performance Task: Proportional Relationships

I.S. 239 Mark Twain. Grade 7 Mathematics Spring Performance Task: Proportional Relationships I.S. 239 Mark Twain 7 ID Name: Date: Grade 7 Mathematics Spring Perfrmance Task: Prprtinal Relatinships Directins: Cmplete all parts f each sheet fr each given task. Be sure t read thrugh the rubrics s

More information

Abstract: The asympttically ptimal hypthesis testig prblem with the geeral surces as the ull ad alterative hyptheses is studied uder expetial-type err

Abstract: The asympttically ptimal hypthesis testig prblem with the geeral surces as the ull ad alterative hyptheses is studied uder expetial-type err Hypthesis Testig with the Geeral Surce y Te Su HAN z April 26, 2000 y This paper is a exteded ad revised versi f Sectis 4.4 4.7 i Chapter 4 f the Japaese bk f Ha [8]. z Te Su Ha is with the Graduate Schl

More information

w (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ.

w (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ. 2 5. Weighted umber of late jobs 5.1. Release dates ad due dates: maximimizig the weight of o-time jobs Oce we add release dates, miimizig the umber of late jobs becomes a sigificatly harder problem. For

More information

Five Whys How To Do It Better

Five Whys How To Do It Better Five Whys Definitin. As explained in the previus article, we define rt cause as simply the uncvering f hw the current prblem came int being. Fr a simple causal chain, it is the entire chain. Fr a cmplex

More information

Thermodynamic study of CdCl 2 in 2-propanol (5 mass %) + water mixture using potentiometry

Thermodynamic study of CdCl 2 in 2-propanol (5 mass %) + water mixture using potentiometry Thermdyamic study f CdCl 2 i 2-prpal (5 mass %) + water mixture usig ptetimetry Reat Tmaš, Ađelka Vrdljak UDC: 544.632.4 Uiversity f Split, Faculty f Chemistry ad Techlgy, Teslia 10/V, HR-21000 Split,

More information

Convergence of random variables. (telegram style notes) P.J.C. Spreij

Convergence of random variables. (telegram style notes) P.J.C. Spreij Covergece of radom variables (telegram style otes).j.c. Spreij this versio: September 6, 2005 Itroductio As we kow, radom variables are by defiitio measurable fuctios o some uderlyig measurable space

More information

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and This article appeared i a jural published by Elsevier The attached cpy is furished t the authr fr iteral -cmmercial research ad educati use, icludig fr istructi at the authrs istituti ad sharig with clleagues

More information

Revisiting the Socrates Example

Revisiting the Socrates Example Sectin 1.6 Sectin Summary Valid Arguments Inference Rules fr Prpsitinal Lgic Using Rules f Inference t Build Arguments Rules f Inference fr Quantified Statements Building Arguments fr Quantified Statements

More information

[COLLEGE ALGEBRA EXAM I REVIEW TOPICS] ( u s e t h i s t o m a k e s u r e y o u a r e r e a d y )

[COLLEGE ALGEBRA EXAM I REVIEW TOPICS] ( u s e t h i s t o m a k e s u r e y o u a r e r e a d y ) (Abut the final) [COLLEGE ALGEBRA EXAM I REVIEW TOPICS] ( u s e t h i s t m a k e s u r e y u a r e r e a d y ) The department writes the final exam s I dn't really knw what's n it and I can't very well

More information

A Simplified Nonlinear Generalized Maxwell Model for Predicting the Time Dependent Behavior of Viscoelastic Materials

A Simplified Nonlinear Generalized Maxwell Model for Predicting the Time Dependent Behavior of Viscoelastic Materials Wrld Jural f Mechaics, 20,, 58-67 di:0.4236/wj.20.302 Published Olie Jue 20 (http://www.scirp.rg/jural/wj) A Siplified Nliear Geeralized Maxwell Mdel fr Predictig the Tie Depedet Behavir f Viscelastic

More information

The Molecular Diffusion of Heat and Mass from Two Spheres

The Molecular Diffusion of Heat and Mass from Two Spheres Iteratial Jural f Mder Studies i Mechaical Egieerig (IJMSME) Vlume 4, Issue 1, 018, PP 4-8 ISSN 454-9711 (Olie) DOI: http://dx.di.rg/10.0431/454-9711.0401004 www.arcjurals.rg The Mlecular Diffusi f Heat

More information