Announcements. CS 188: Artificial Intelligence Fall Reflex Agents. Today. Goal Based Agents. Search Problems
|
|
- Allyson Townsend
- 6 years ago
- Views:
Transcription
1 Announmnts Pojt 0: Pyton Tutoil Du tomoow! T is l Wnsy om 3m-5m in o 75 T l tim is otionl, ut P0 itsl is not On sumit, you soul gt mil om t utog Pojt : On t w toy tt ly n sk ustions. It s long tn most! l-dignosti on w tions: n go to ny, ut v ioity in you own C 88: Atiiil Intllign Fll 0 Ltu : Quu-Bs 8/30/0 Dn Klin UC Bkly Multil slis om tut Russll, Anw Moo Toy Rlx Agnts Agnts tt Pln A Polms Uninom Mtos (t viw o som) Dt-Fist Bt-Fist Uniom-Cost Huisti Mtos (nw o ll) y Rlx gnts: Coos tion s on unt t (n my mmoy) My v mmoy o mol o t wol s unt stt Do not onsi t utu onsuns o ti tions Consi ow t wol I Cn lx gnt tionl? [mo: lx otiml / loo ] ol Bs Agnts ol-s gnts: Pln Ask wt i Disions s on (yotsiz) onsuns o tions Must v mol o ow t wol volvs in sons to tions Consi ow t wol WOULD BE [mo: ln st / slow ] Polms A s olm onsists o: A stt s A susso untion (wit tions, osts) A stt stt n gol tst N,.0 E,.0 A solution is sun o tions ( ln) wi tnsoms t stt stt to gol stt
2 Exml: Romni tt s tt s: Citis usso untion: Ros: o to j ity wit ost = ist tt stt: A ol tst: Is stt == Bust? olution? tt s g: A mtmtil snttion o s olm Fo vy s olm, t s osoning stt s g T susso untion is snt y s W n ly uil tis g in mmoy (so w on t) Riiulously tiny s g o tiny s olm Wt s in tt? T wol stt siis vy lst til o t nvionmnt A s stt ks only t tils n (sttion) Polm: Pting tts: (x,y) lotion Ations: NEW usso: ut lotion only ol tst: is (x,y)=end Polm: Et-All-Dots tts: {(x,y), ot oolns} Ations: NEW usso: ut lotion n ossily ot ooln ol tst: ots ll ls Wol stt: Agnt ositions: 0 Foo ount: 30 ost ositions: Agnt ing: NEW tt izs? How mny Wol stts? 0x( 30 )x( )x4 tts o ting? 0 tts o t-ll-ots? 0x( 30 ) Ts Anot T N,.0 E,.0 A s t: Tis is wt i t o lns n outoms tt stt t t oot no Ciln oson to sussos Nos ontin stts, oson to PLAN to tos stts Fo most olms, w n nv tully uil t wol t : Exn out ossil lns Mintin ing o unxn lns Ty to xn s w t nos s ossil
3 nl T Exml: T Imotnt is: Fing Exnsion Exlotion sttgy Dtil suoo is in t ook! Min ustion: wi ing nos to xlo? tt s vs. Ts Rviw: Dt Fist W onstut ot on mn n w onstut s littl s ossil. E NODE in in t s t is n nti PATH in t olm g. ttgy: xn st no ist Imlmnttion: Fing is LIFO stk Rviw: Bt Fist Algoitm Potis ttgy: xn sllowst no ist Imlmnttion: Fing is FIFO uu Tis Comlt? unt to in solution i on xists? Otiml? unt to in t lst ost t? Tim omlxity? omlxity? Vils: n Num o stts in t olm (ug) T vg ning to B (t vg num o sussos) C* Cost o lst ost solution s m Dt o t sllowst solution Mx t o t s t 3
4 DF Algoitm Comlt Otiml Tim DF Dt Fist N N N N O(B Ininit LMAX ) O(LMAX) Ininit DF Wit yl king, DF is omlt.* m tis no nos nos TART OAL m nos Ininit ts mk DF inomlt How n w ix tis? Algoitm Comlt Otiml Tim DF w/ Pt Cking Y N O( m+ ) O(m) Wn is DF otiml? * O g s nxt ltu. BF Algoitm Comlt Otiml Tim DF BF w/ Pt Cking Y N O( m+ ) O(m) Y N* O( s+ ) O( s ) Comisons Wn will BF outom DF? s tis no nos nos Wn will DF outom BF? s nos m nos Wn is BF otiml? Ittiv Dning Ittiv ning: BF using DF s suoutin:. Do DF wi only ss o ts o lngt o lss.. I il, o DF wi only ss ts o lngt o lss. 3. I il, o DF wi only ss ts o lngt 3 o lss..n so on. Algoitm Comlt Otiml Tim DF BF ID w/ Pt Cking Y N O( m+ ) O(m) Y N* O( s+ ) O( s ) Y N* O( s+ ) O(s) TART 3 Costs on Ations 5 8 OAL Noti tt BF ins t sotst t in tms o num o tnsitions. It os not in t lst-ost t. W will uikly ov n lgoitm wi os in t lst-ost t
5 Uniom Cost Pioity Quu Rs Exn st no ist: Fing is ioity uu (ioity: umultiv ost) Cost ontous A ioity uu is t stutu in wi you n inst n tiv (ky, vlu) is wit t ollowing otions:.us(ky, vlu).o() insts (ky, vlu) into t uu. tuns t ky wit t lowst vlu, n movs it om t uu. You n s ky s ioity y using it gin Unlik gul uu, instions n t onstnt tim, usully O(log n) W ll n ioity uus o ost-snsitiv s mtos Uniom Cost Uniom Cost Issus Algoitm Comlt Otiml Tim DF BF UC w/ Pt Cking Y N O( m+ ) O(m) Y N O( s+ ) O( s ) Y* Y O( C*/ε ) O( C*/ε ) Rmm: xlos insing ost ontous T goo: UC is omlt n otiml! 3 C*/ε tis * UC n il i tions n gt itily T : Exlos otions in vy ition No inomtion out gol lotion tt ol [mo: s mo mty] Huistis Huistis Any stimt o ow los stt is to gol Dsign o tiul s olm Exmls: Mnttn istn, Eulin istn
6 Bst Fist / y Bst Fist / y Exn t no tt sms losst A ommon s: Bst-ist tks you stigt to t (wong) gol Wost-s: lik lygui DF in t wost s Cn xlo vyting Cn gt stuk in loos i no yl king Wt n go wong? [mo: gy] Lik DF in omltnss (init stts w/ yl king) on Wong? 6
CS 188: Artificial Intelligence Fall 2011
Announmnts Pojt 0: Pyton Tutoil Du tomoow! T is l Wnsy om 3m-5m in o 275 T l tim is otionl, ut P0 itsl is not On sumit, you soul gt mil om t utog Pojt 1: On t w toy tt ly n sk ustions. It s long tn most!
More informationAnnouncements. CS 188: Artificial Intelligence Fall Today. Reflex Agents. Goal Based Agents. Search Problems
C 88: Atiiil Intllign Fll 009 Ltu : Quu-Bs 9//009 Dn Klin UC Bkly Multil slis om tut Russll, Anw Moo Announmnts Pojt 0: Pyton Tutoil Du tomoow! T is l tomoow om m-3m in o 75 T l tim is otionl, ut P0 itsl
More informationCS 188: Artificial Intelligence Fall Announcements
C 188: Atiiil Intllign Fll 2009 Ltu 2: Quu-Bs 9/1/2009 Dn Klin UC Bkly Multil slis om tut Russll, Anw Moo Announmnts Pojt 0: Pyton Tutoil Du tomoow! T is l tomoow om 1m-3m in o 275 T l tim is otionl, ut
More informationCS 188: Artificial Intelligence Fall Announcements
C 188: Atiiil Intllign Fll 007 Ltu : Quu-Bs 8/31/007 Dn Klin UC Bkly Mny slis om it tut Russll o Anw Moo Announmnts Nxt wk Nw oom is 105 Not Gt, stts Tusy Ck w g o stions (nw oming) L Fiy 10m to 5m in
More informationCS 188: Artificial Intelligence Spring Announcements
C 188: Atiiil Intllign ing 2006 Ltu 2: Quu-Bs 8/31/2006 Dn Klin UC Bkly Mny slis om it tut Russll o Anw Moo Announmnts L Fiy 1-5m in o 275 Ln Pyton tt on Pojt 1.1: Mzwol Com o wtv tims you lik No stions
More informationCS 188: Artificial Intelligence Spring Announcements
C 188: Atiiil Intllign ing 2010 Ltu 2: Quu-Bs 1/21/2010 Pit Al UC Bkly Mny slis om Dn Klin Announmnts Pojt 0: Pyton Tutoil Out toy. Du nxt wk Tusy. L sssions in 271 o: Mony 2-3m Wnsy 4-5m T l tim is otionl,
More informationCS 188: Artificial Intelligence Spring Announcements
CS 188: Atiiil Intllign Sing 2011 Ltu 2: Quu-Bs S 1/24/2010 Pit Al UC Bkly Mny slis om Dn Klin Announmnts Pojt 0: Pyton Tutoil Du Fiy 5m. L sssion Wnsy 3-5m in 271 So T l tim is otionl, ut P0 itsl is not
More informationAnnouncements. CS 188: Artificial Intelligence Spring More Announcements. Today. From Last Time: Reflex Agents.
C 88: Atiiil Intllign ing 009 Ltu : Quu-Bs //008 Jon DNo UC Bkly Mny slis om Dn Klin, tut Russll o Anw Moo Announmnts Pojt 0: Pyton Tutoil Post onlin now Du nxt Wnsy, Jn 8 T is l toy om m-3m in o 75 T
More informationCS 188: Artificial Intelligence Spring 2009
C 188: Atiiil Intllign ing 009 Ltu : Quu-Bs 1//008 Jon DNo UC Bkly Mny slis om Dn Klin, tut Russll o Anw Moo Announmnts Pojt 0: Pyton Tutoil Post onlin now Du nxt Wnsy, Jn 8 T is l toy om 1m-3m in o 75
More informationOutline. CSE 473: Artificial Intelligence Spring Types of Agents
9/9/7 CE 7: Atiiil Intllign ing 07 Polms Outlin Polm s & Dit Fox Uninom Mtos Dt-Fist Bt-Fist Uniom-Cost Wit slis om Dn Wl, Pit Al, Dn Klin, tut Russll, Anw Moo, Luk Zttlmoy Agnt vs. Envionmnt Tys o Agnts
More informationCS 188: Artificial Intelligence
C 188: Atiiil Intllign Toy Agnts tt Pln A Polms Instuto: Mo Alvz Univsity o Ro Isln (Ts slis w t/moii y Dn Klin, Pit Al, An Dgn o C188 t UC Bkly) Uninom Mtos Dt-Fist Bt-Fist Uniom-Cost Agnts tt Pln Rlx
More informationToday. CS 232: Ar)ficial Intelligence. Search. Agents that Plan. September 3 rd, 2015 Search Problems. Uninformed Search Methods
1 C 232: A)iil Intllign Toy tm 3, 2015 Agnts tt Pln A Polms Uninom Mtos Dt- Fist Bt- Fist Uniom- Cost [Ts slis w t y Dn Klin n Pit Al o C188 Into to AI t UC Bkly. All C188 mtils vill t M://i.kly.u.] Agnts
More informationReminder. CS 188: Artificial Intelligence. A reflex agent for pacman. Reflex Agent. A reflex agent for pacman (3) A reflex agent for pacman (2)
C 88: Atiiil Intllign Ltus n : Rmin Only vy smll tion o AI is out mking omuts ly gms intlligntly Rll: omut vision, ntul lngug, ootis, min lning, omuttionl iology, t. Pit Al UC Bkly Mny slis om Dn Klin
More informationCSE 573: Artificial Intelligence Autumn Search thru a. Goal Based Agents 9/28/2012. Agent vs. Environment. Example: N Queens
CE 573: Atiiil Intllign Autumn 0 Intoution & Dn Wl Wit slis om Dn Klin, tut Russll, Anw Moo, Luk Zttlmoy Agnt vs. Envionmnt An gnt is n ntity tt ivs n ts. A tionl gnt slts tions tt mximiz its utility untion.
More informationSearching: Deterministic single-agent
Sing: Dtministi singl-gnt Anw W. Moo Posso Sool o Comut Sin Cngi Mllon Univsity www.s.mu.u/~wm wm@s.mu.u -68-7 ot to ot ts n uss o ts slis. Anw woul ligt i you oun tis sou mtil usul in giving you own ltus.
More informationA search problem. Formalizing a search problem. Our Search Problem. Our Search Problem. Overview
Sing: Dtministi singl-gnt Atully, tis is otimiztion ov tim wit ist vils Anw W. Moo Posso Sool o Comut Sin Cngi Mllon Univsity www.s.mu.u/~wm wm@s.mu.u -6-7 ot to ot ts n uss o ts slis. Anw woul ligt i
More informationCourse Logistics Textbook: Artificial Intelligence: A Modern Approach, Russell and Norvig (3 rd ed) Topics
CE 573: Atiiil Intllign Autumn 0 Intoution & Dn Wl Wit slis om Dn Klin, tut Russll, Anw Moo, Luk Zttlmoy Cous Logistis Txtook: Atiiil Intllign: A Mon Ao, Russll n Novig (3 ) Puisits: Dt tutus (CE 36 o
More informationAnnouncements. CS 188: Artificial Intelligence Fall Office hours, Section. Today. DFS and BFS. Recap: Search. Lecture 3: A* Search 9/3/2009
C 88: Atiicil Intllignc Fll 009 Lctu : A* ch 9//009 Pit Al UC Bkly Mny slids om Dn Klin Announcmnts Assignmnts: Pojct 0 (Python tutoil): du Thusdy /8 Wittn (ch): du Thusdy /8 Pojct (ch): to lsd tody, du
More informationAnnouncements. CS 188: Artificial Intelligence. Costs on Actions. Recap: Search. Lecture 3: A* Search
Announcmnts Pojcts: Looking fo ojct tns? --- Com to font ft lctu. Ty i ogmming, not divid-nd-conu Account foms vill u font duing k nd ft lctu Assignmnts Looking fo studnts to wok on ssignmnts with? ---
More informationSelf-Adjusting Top Trees
Th Polm Sl-jsting Top Ts ynmi ts: ol: mintin n n-tx ost tht hngs o tim. link(,w): ts n g twn tis n w. t(,w): lts g (,w). pplition-spii t ssoit with gs n/o tis. ont xmpls: in minimm-wight g in th pth twn
More informationExam 2 Solutions. Jonathan Turner 4/2/2012. CS 542 Advanced Data Structures and Algorithms
CS 542 Avn Dt Stutu n Alotm Exm 2 Soluton Jontn Tun 4/2/202. (5 ont) Con n oton on t tton t tutu n w t n t 2 no. Wt t mllt num o no tt t tton t tutu oul ontn. Exln you nw. Sn n mut n you o u t n t, t n
More informationSearch: Cost & Heuristics
9/9/ CSE 7: Artiiil Intelligene Autumn 0 Pizz? Announements Serh: Cost & Heuristis Dn Wel Projet : Serh online tomorrow ue Mony 0/ ue We / Strt! With slies rom Dn Klein, Sturt Russell, Anrew Moore, Luke
More informationProblem solving by search
Prolm solving y srh Tomáš voo Dprtmnt o Cyrntis, Vision or Roots n Autonomous ystms Mrh 5, 208 / 3 Outlin rh prolm. tt sp grphs. rh trs. trtgis, whih tr rnhs to hoos? trtgy/algorithm proprtis? Progrmming
More informationOpenMx Matrices and Operators
OpnMx Mtris n Oprtors Sr Mln Mtris: t uilin loks Mny typs? Dnots r lmnt mxmtrix( typ= Zro", nrow=, nol=, nm="" ) mxmtrix( typ= Unit", nrow=, nol=, nm="" ) mxmtrix( typ= Int", nrow=, nol=, nm="" ) mxmtrix(
More informationWeighted Graphs. Weighted graphs may be either directed or undirected.
1 In mny ppltons, o rp s n ssot numrl vlu, ll wt. Usully, t wts r nonntv ntrs. Wt rps my tr rt or unrt. T wt o n s otn rrr to s t "ost" o t. In ppltons, t wt my msur o t lnt o rout, t pty o ln, t nry rqur
More informationTangram Fractions Overview: Students will analyze standard and nonstandard
ACTIVITY 1 Mtrils: Stunt opis o tnrm mstrs trnsprnis o tnrm mstrs sissors PROCEDURE Skills: Dsriin n nmin polyons Stuyin onrun Comprin rtions Tnrm Frtions Ovrviw: Stunts will nlyz stnr n nonstnr tnrms
More informationd e c b a d c b a d e c b a a c a d c c e b
FLAT PEYOTE STITCH Bin y mkin stoppr -- sw trou n pull it lon t tr until it is out 6 rom t n. Sw trou t in witout splittin t tr. You soul l to sli it up n own t tr ut it will sty in pl wn lt lon. Evn-Count
More information10.3 The Quadratic Formula
. Te Qudti Fomul We mentioned in te lst setion tt ompleting te sque n e used to solve ny qudti eqution. So we n use it to solve 0. We poeed s follows 0 0 Te lst line of tis we ll te qudti fomul. Te Qudti
More informationLecture 20: Minimum Spanning Trees (CLRS 23)
Ltur 0: Mnmum Spnnn Trs (CLRS 3) Jun, 00 Grps Lst tm w n (wt) rps (unrt/rt) n ntrou s rp voulry (vrtx,, r, pt, onnt omponnts,... ) W lso suss jny lst n jny mtrx rprsntton W wll us jny lst rprsntton unlss
More information4.1 Interval Scheduling. Chapter 4. Greedy Algorithms. Interval Scheduling: Greedy Algorithms. Interval Scheduling. Interval scheduling.
Cptr 4 4 Intrvl Suln Gry Alortms Sls y Kvn Wyn Copyrt 005 Prson-Ason Wsly All rts rsrv Intrvl Suln Intrvl Suln: Gry Alortms Intrvl suln! Jo strts t s n nss t! Two os omptl ty on't ovrlp! Gol: n mxmum sust
More information(2) If we multiplied a row of B by λ, then the value is also multiplied by λ(here lambda could be 0). namely
. DETERMINANT.. Dtrminnt. Introution:I you think row vtor o mtrix s oorint o vtors in sp, thn th gomtri mning o th rnk o th mtrix is th imnsion o th prlllppi spnn y thm. But w r not only r out th imnsion,
More informationP a g e 3 6 of R e p o r t P B 4 / 0 9
P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J
More informationEasy Steps to build a part number... Tri-Start Series III CF P
ulti-l i Oti iul ( oto) ow to O ol os sy ts to uil t u... i-tt is 1. 2 3 4. 5. 6. oto y til iis ll tyl ll iz- st t ott y & y/ ywy ositio 50 9 0 17-08 ol ulti-l i oti otos o us wit ulti-o sil o tii o y
More informationCOMP108 Algorithmic Foundations
Grdy mthods Prudn Wong http://www.s.liv..uk/~pwong/thing/omp108/01617 Coin Chng Prolm Suppos w hv 3 typs of oins 10p 0p 50p Minimum numr of oins to mk 0.8, 1.0, 1.? Grdy mthod Lrning outoms Undrstnd wht
More information1 Introduction to Modulo 7 Arithmetic
1 Introution to Moulo 7 Arithmti Bor w try our hn t solvin som hr Moulr KnKns, lt s tk los look t on moulr rithmti, mo 7 rithmti. You ll s in this sminr tht rithmti moulo prim is quit irnt rom th ons w
More informationT h e C S E T I P r o j e c t
T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T
More informationKEB INVERTER L1 L2 L3 FLC - RELAY 1 COMMON I1 - APPROACH CLOSE 0V - DIGITAL COMMON FLA - RELAY 1 N.O. AN1+ - ANALOG 1 (+) CRF - +10V OUTPUT
XT SSMLY MOL 00 (O FS) 00 (I- PT) 00 (SIGL SLI) WG O 0 0-0 0-0-0 0.0. 0 0-0 0-0-0 0 0-0 0-0-0 VOLTG F.L...0..0..0.0..0 IIG POW FOM US SUPPLI ISOT (S TL) US OP OUTOS T T 0 O HIGH H IUIT POTTIO OT: H IUIT
More informationMulticast routing algorithm based on Extended Simulated Annealing Algorithm
7t WSEAS Int. Con. on MATHEMATICAL METHODS n COMPUTATIONAL TECHNIQUES IN ELECTRICAL ENGINEERING, Soi, 27-29/10/05 (pp129-133) Multist outing lgoitm s on Extn Simult Annling Algoitm Jin-Ku Jong*, Sung-Ok
More informationlearning objectives learn what graphs are in mathematical terms learn how to represent graphs in computers learn about typical graph algorithms
rp loritms lrnin ojtivs loritms your sotwr systm sotwr rwr lrn wt rps r in mtmtil trms lrn ow to rprsnt rps in omputrs lrn out typil rp loritms wy rps? intuitivly, rp is orm y vrtis n s twn vrtis rps r
More informationSpanning Trees. BFS, DFS spanning tree Minimum spanning tree. March 28, 2018 Cinda Heeren / Geoffrey Tien 1
Spnnn Trs BFS, DFS spnnn tr Mnmum spnnn tr Mr 28, 2018 Cn Hrn / Gory Tn 1 Dpt-rst sr Vsts vrts lon snl pt s r s t n o, n tn ktrks to t rst junton n rsums own notr pt Mr 28, 2018 Cn Hrn / Gory Tn 2 Dpt-rst
More informationCS 188: Artificial Intelligence Spring 2007
CS 188: Artificil Intelligence Spring 2007 Lecture 3: Queue-Bsed Serch 1/23/2007 Srini Nrynn UC Berkeley Mny slides over the course dpted from Dn Klein, Sturt Russell or Andrew Moore Announcements Assignment
More informationProblem 1. Solution: = show that for a constant number of particles: c and V. a) Using the definitions of P
rol. Using t dfinitions of nd nd t first lw of trodynis nd t driv t gnrl rltion: wr nd r t sifi t itis t onstnt rssur nd volu rstivly nd nd r t intrnl nrgy nd volu of ol. first lw rlts d dq d t onstnt
More informationCOMP 250. Lecture 29. graph traversal. Nov. 15/16, 2017
COMP 250 Ltur 29 rp trvrsl Nov. 15/16, 2017 1 Toy Rursv rp trvrsl pt rst Non-rursv rp trvrsl pt rst rt rst 2 Hs up! Tr wr w mstks n t sls or S. 001 or toy s ltur. So you r ollown t ltur rorns n usn ts
More informationc. What is the average rate of change of f on the interval [, ]? Answer: d. What is a local minimum value of f? Answer: 5 e. On what interval(s) is f
Essential Skills Chapter f ( x + h) f ( x ). Simplifying the difference quotient Section. h f ( x + h) f ( x ) Example: For f ( x) = 4x 4 x, find and simplify completely. h Answer: 4 8x 4 h. Finding the
More informationPLAYGROUND SALE Take up to 40% off. Plus FREE equipment * with select purchase DETAILS INSIDE
PLYROUND SL Tk up t 40% ff Plu FR quipnt * with lct puch DTILS INSID T BONUS QUIPMNT FR! T BONUS QUIPMNT FR * Mk qulifing $10K, $0K $30K puch f thi ORDR $10K ORDR $0K ORDR $30K T ON FR* T TO FR* T THR
More informationGrade 7/8 Math Circles March 4/5, Graph Theory I- Solutions
ulty o Mtmtis Wtrloo, Ontrio N ntr or ution in Mtmtis n omputin r / Mt irls Mr /, 0 rp Tory - Solutions * inits lln qustion. Tr t ollowin wlks on t rp low. or on, stt wtr it is pt? ow o you know? () n
More informationPaths. Connectivity. Euler and Hamilton Paths. Planar graphs.
Pths.. Eulr n Hmilton Pths.. Pth D. A pth rom s to t is squn o gs {x 0, x 1 }, {x 1, x 2 },... {x n 1, x n }, whr x 0 = s, n x n = t. D. Th lngth o pth is th numr o gs in it. {, } {, } {, } {, } {, } {,
More informationDATA Search I 魏忠钰. 复旦大学大数据学院 School of Data Science, Fudan University. March 7 th, 2018
DATA620006 魏忠钰 Serch I Mrch 7 th, 2018 Outline Serch Problems Uninformed Serch Depth-First Serch Bredth-First Serch Uniform-Cost Serch Rel world tsk - Pc-mn Serch problems A serch problem consists of:
More informationOutline. 1 Introduction. 2 Min-Cost Spanning Trees. 4 Example
Outlin Computr Sin 33 Computtion o Minimum-Cost Spnnin Trs Prim's Alorithm Introution Mik Joson Dprtmnt o Computr Sin Univrsity o Clry Ltur #33 3 Alorithm Gnrl Constrution Mik Joson (Univrsity o Clry)
More informationGavilan JCCD Trustee Areas Plan Adopted November 10, 2015
Gvil JCCD Tust A Pl Aopt Novmb, S Jos US p Ls Pl Aopt // Cit/Csus Dsigt Plc ighw Cit Aom ollist igm S Jos Ts Pios c Ps 4 ut S Bito ut ils Aom ollist igm Ts Pios S Bito ut Lpoff & Goblt Dmogphic sch, Ic.
More information176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s
A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps
More informationDistributed Set Reachability
Dstt St Rty S Gj Mt T Mx-P Isttt Its, Usty U Gy SIGMOD 2016, S Fs, USA Dstt St Rty Dstt St Rty (DSR) s zt ty xt t sts stt stt Dstt St Rty 2 Dstt St Rty Dstt St Rty (DSR) s zt ty xt t sts stt stt Dstt St
More informationAn undirected graph G = (V, E) V a set of vertices E a set of unordered edges (v,w) where v, w in V
Unirt Grphs An unirt grph G = (V, E) V st o vrtis E st o unorr gs (v,w) whr v, w in V USE: to mol symmtri rltionships twn ntitis vrtis v n w r jnt i thr is n g (v,w) [or (w,v)] th g (v,w) is inint upon
More informationCS 241 Analysis of Algorithms
CS 241 Anlysis o Algorithms Prossor Eri Aron Ltur T Th 9:00m Ltur Mting Lotion: OLB 205 Businss HW6 u lry HW7 out tr Thnksgiving Ring: Ch. 22.1-22.3 1 Grphs (S S. B.4) Grphs ommonly rprsnt onntions mong
More information16.unified Introduction to Computers and Programming. SOLUTIONS to Examination 4/30/04 9:05am - 10:00am
16.unii Introution to Computrs n Prormmin SOLUTIONS to Exmintion /30/0 9:05m - 10:00m Pro. I. Kristin Lunqvist Sprin 00 Grin Stion: Qustion 1 (5) Qustion (15) Qustion 3 (10) Qustion (35) Qustion 5 (10)
More informationIn which direction do compass needles always align? Why?
AQA Trloy Unt 6.7 Mntsm n Eltromntsm - Hr 1 Complt t p ll: Mnt or s typ o or n t s stronst t t o t mnt. Tr r two typs o mnt pol: n. Wrt wt woul ppn twn t pols n o t mnt ntrtons low: Drw t mnt l lns on
More informationAppendix. In the absence of default risk, the benefit of the tax shield due to debt financing by the firm is 1 C E C
nx. Dvon o h n wh In h sn o ul sk h n o h x shl u o nnng y h m s s h ol ouon s h num o ssus s h oo nom x s h sonl nom x n s h v x on quy whh s wgh vg o vn n l gns x s. In hs s h o sonl nom xs on h x shl
More informationF102 1/4 AMP +240 VDC SEE FIGURE 5-14 FILAMENT AND OVEN CKTS BLU J811 BREAK-IN TB103 TO S103 TRANSMITTER ASSOCIATED CAL OFF FUNCTION NOTE 2 STANDBY
OWR OR F0 M NOT S0 RT OF FUNTI FL0 T0 OWR SULY SUSSIS T0 T0 WIR FOR 0 V OWR SULY SUSSIS T0 WIR FOR V 0 0 RT V0 RT V0. V RT V0 RT V0 NOT. V. V NOT +0 V 0 +0 V. V 0 FUNTI NOT L +0 V S FIUR - FILMNT N OVN
More informationClassical Theory of Fourier Series : Demystified and Generalised VIVEK V. RANE. The Institute of Science, 15, Madam Cama Road, Mumbai
Clssil Thoy o Foi Sis : Dmystii Glis VIVEK V RANE Th Istitt o Si 5 Mm Cm Ro Mmbi-4 3 -mil ss : v_v_@yhoooi Abstt : Fo Rim itgbl tio o itvl o poit thi w i Foi Sis t th poit o th itvl big ot how wh th tio
More information(Minimum) Spanning Trees
(Mnmum) Spnnn Trs Spnnn trs Kruskl's lortm Novmr 23, 2017 Cn Hrn / Gory Tn 1 Spnnn trs Gvn G = V, E, spnnn tr o G s onnt surp o G wt xtly V 1 s mnml sust o s tt onnts ll t vrts o G G = Spnnn trs Novmr
More informationChapter 6 Perturbation theory
Ct 6 Ptutio to 6. Ti-iddt odgt tutio to i o tutio sst is giv to fid solutios of λ ' ; : iltoi of si stt : igvlus of : otool igfutios of ; δ ii Rlig-Södig tutio to ' λ..6. ; : gl iltoi ': tutio λ : sll
More informationCS 103 BFS Alorithm. Mark Redekopp
CS 3 BFS Aloritm Mrk Rkopp Brt-First Sr (BFS) HIGHLIGHTED ALGORITHM 3 Pt Plnnin W'v sn BFS in t ontxt o inin t sortst pt trou mz? S?? 4 Pt Plnnin W xplor t 4 niors s on irtion 3 3 3 S 3 3 3 3 3 F I you
More informationMath 166 Week in Review 2 Sections 1.1b, 1.2, 1.3, & 1.4
Mt 166 WIR, Sprin 2012, Bnjmin urisp Mt 166 Wk in Rviw 2 Stions 1.1, 1.2, 1.3, & 1.4 1. S t pproprit rions in Vnn irm tt orrspon to o t ollowin sts. () (B ) B () ( ) B B () (B ) B 1 Mt 166 WIR, Sprin 2012,
More informationWho is this Great Team? Nickname. Strangest Gift/Friend. Hometown. Best Teacher. Hobby. Travel Destination. 8 G People, Places & Possibilities
Who i thi Gt Tm? Exi Sh th foowing i of infomtion bot of with o tb o tm mt. Yo o not hv to wit n of it own. Yo wi b givn on 5 mint to omih thi tk. Stngt Gift/Fin Niknm Homtown Bt Th Hobb Tv Dtintion Robt
More information7 ACM FOR FRAME 2SET 6 FRAME 2SET 5 ACM FOR MAIN FRAME 2SET 4 MAIN FRAME 2SET 3 POLE ASSLY 1 2 CROWN STRUCTURE ASSLY 1 1 CROWN ASSLY 1
7 M OR RM 2ST 6 RM 2ST 5 M OR MIN RM 2ST 4 MIN RM 2ST 3 POL SSLY 1 2 ROWN STRUTUR SSLY 1 1 ROWN SSLY 1 SR.NO. SRIPTION QTY. a LL IMNSIONS R IN mm I N MT Pi IOLMI 1'NTION LT. Tm: XPLO VIW OR POL MOUNT MLM
More informationG-001 CHATHAM HARBOR AUNT LYDIA'S COVE CHATHAM ATLANTIC OCEAN INDEX OF NAVIGATION AIDS GENERAL NOTES: GENERAL PLAN A6 SCALE: 1" = 500' CANADA
TR ISL ROR UST 8 O. R-2,4-3 R-4 IX O VITIO IS STT PL ORPI OORITS POSITIO 27698 4-39'-" 88 69-6'-4."W 278248 4-4'-" 8968 69-6'-4"W 27973 4-4'-2" 88 69-6'-"W W MPSIR OOR UUST PORTL MI OR 27 8-OOT OR L -
More informationCSE 373. Graphs 1: Concepts, Depth/Breadth-First Search reading: Weiss Ch. 9. slides created by Marty Stepp
CSE 373 Grphs 1: Conpts, Dpth/Brth-First Srh ring: Wiss Ch. 9 slis rt y Mrty Stpp http://www.s.wshington.u/373/ Univrsity o Wshington, ll rights rsrv. 1 Wht is grph? 56 Tokyo Sttl Soul 128 16 30 181 140
More informationThe local orthonormal basis set (r,θ,φ) is related to the Cartesian system by:
TIS in Sica Cooinats As not in t ast ct, an of t otntias tat w wi a wit a cnta otntias, aning tat t a jst fnctions of t istanc btwn a atic an so oint of oigin. In tis cas tn, (,, z as a t Coob otntia an
More informationRUTH. land_of_israel: the *country *which God gave to his people in the *Old_Testament. [*map # 2]
RUTH 1 Elimlk g ln M 1-2 I in im n ln Irl i n *king. Tr r lr rul ln. Ty r ug. Tr n r l in Ju u r g min. Elimlk mn y in n Blm in Ju. H i nm Nmi. S n Elimlk 2 *n. Tir nm r Mln n Kilin. Ty r ll rm Er mily.
More informationDepth First Search. Yufei Tao. Department of Computer Science and Engineering Chinese University of Hong Kong
Dprtmnt o Computr Sn n Ennrn Cns Unvrsty o Hon Kon W v lry lrn rt rst sr (BFS). Toy, w wll suss ts sstr vrson : t pt rst sr (DFS) lortm. Our susson wll on n ous on rt rps, us t xtnson to unrt rps s strtorwr.
More informationStable Matching for Spectrum Market with Guaranteed Minimum Requirement
Sl g Spum Gun mum Rqumn Yno n T S Ky Sw ngg ompu Sool Wun Uny nyno@wuun Yuxun Xong T S Ky Sw ngg ompu Sool Wun Uny xongyx@mlluun Qn Wng ompu Sool Wun Uny qnwng@wuun STRT Xoyn Y Sool mon Tlogy ow Uny X
More informationCSE 373: More on graphs; DFS and BFS. Michael Lee Wednesday, Feb 14, 2018
CSE 373: Mor on grphs; DFS n BFS Mihl L Wnsy, F 14, 2018 1 Wrmup Wrmup: Disuss with your nighor: Rmin your nighor: wht is simpl grph? Suppos w hv simpl, irt grph with x nos. Wht is th mximum numr of gs
More informationDivided. diamonds. Mimic the look of facets in a bracelet that s deceptively deep RIGHT-ANGLE WEAVE. designed by Peggy Brinkman Matteliano
RIGHT-ANGLE WEAVE Dv mons Mm t look o ts n rlt tt s ptvly p sn y Py Brnkmn Mttlno Dv your mons nto trnls o two or our olors. FCT-SCON0216_BNB66 2012 Klm Pulsn Co. Ts mtrl my not rprou n ny orm wtout prmsson
More informationTrade Patterns, Production networks, and Trade and employment in the Asia-US region
Trade Patterns, Production networks, and Trade and employment in the Asia-U region atoshi Inomata Institute of Developing Economies ETRO Development of cross-national production linkages, 1985-2005 1985
More informationDesigning A Uniformly Loaded Arch Or Cable
Dsinin A Unirmy Ar Or C T pr wit tis ssn, i n t Nxt uttn r r t t tp ny p. Wn yu r n wit tis ssn, i n t Cntnts uttn r r t t tp ny p t rturn t t ist ssns. Tis is t Mx Eyt Bri in Stuttrt, Grmny, sin y Si
More informationFunctions and Graphs 1. (a) (b) (c) (f) (e) (d) 2. (a) (b) (c) (d)
Functions nd Grps. () () (c) - - - O - - - O - - - O - - - - (d) () (f) - - O - 7 6 - - O - -7-6 - - - - - O. () () (c) (d) - - - O - O - O - - O - -. () G() f() + f( ), G(-) f( ) + f(), G() G( ) nd G()
More informationCSE 373: AVL trees. Warmup: Warmup. Interlude: Exploring the balance invariant. AVL Trees: Invariants. AVL tree invariants review
rmup CSE 7: AVL trs rmup: ht is n invrint? Mihl L Friy, Jn 9, 0 ht r th AVL tr invrints, xtly? Disuss with your nighor. AVL Trs: Invrints Intrlu: Exploring th ln invrint Cor i: xtr invrint to BSTs tht
More informationNefertiti. Echoes of. Regal components evoke visions of the past MULTIPLE STITCHES. designed by Helena Tang-Lim
MULTIPLE STITCHES Nrtiti Ehos o Rgl omponnts vok visions o th pst sign y Hln Tng-Lim Us vrity o stiths to rt this rgl yt wrl sign. Prt sping llows squr s to mk roun omponnts tht rp utiully. FCT-SC-030617-07
More informationCMPS 2200 Fall Graphs. Carola Wenk. Slides courtesy of Charles Leiserson with changes and additions by Carola Wenk
CMPS 2200 Fll 2017 Grps Crol Wnk Sls ourtsy o Crls Lsrson wt ns n tons y Crol Wnk 10/23/17 CMPS 2200 Intro. to Alortms 1 Grps Dnton. A rt rp (rp) G = (V, E) s n orr pr onsstn o st V o vrts (snulr: vrtx),
More informationI M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o
I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o u l d a l w a y s b e t a k e n, i n c l u d f o l
More informationL...,,...lllM" l)-""" Si_...,...
> 1 122005 14:8 S BF 0tt n FC DRE RE FOR C YER 2004 80?8 P01/ Rc t > uc s cttm tsus H D11) Rqc(tdk ;) wm1111t 4 (d m D m jud: US
More informationPlanar Upward Drawings
C.S. 252 Pro. Rorto Tmssi Computtionl Gomtry Sm. II, 1992 1993 Dt: My 3, 1993 Sri: Shmsi Moussvi Plnr Upwr Drwings 1 Thorm: G is yli i n only i it hs upwr rwing. Proo: 1. An upwr rwing is yli. Follow th
More informationBeechwood Music Department Staff
Beechwood Music Department Staff MRS SARAH KERSHAW - HEAD OF MUSIC S a ra h K e rs h a w t r a i n e d a t t h e R oy a l We ls h C o l le g e of M u s i c a n d D ra m a w h e re s h e ob t a i n e d
More informationDaily Skill Practice
G CD-0 Dily Skill Pti 00 Wkk ## W i t it Eh. gh y w m y il A ll? + = 8 Dy 8= 0. =. Nm. C h l lit tl k ty i g. I h hi ty w ig h, m y hw hi g w ig h?. W Wkk ##00 A A = t, >, = W it < t t m t m k t. Dy Dy
More informationNUCON NRNON CONRNC ON CURRN RN N CHNOOGY, 011 oo uul o w ul x ol volv y y oll. y ov,., - o lo ll vy ul o Mo l u v ul (G) v Gl vlu oll. u 3- [11]. 000
NU O HMB NRM UNVRY, HNOOGY, C 8 0 81, 8 3-1 01 CMBR, 0 1 1 l oll oll ov ll lvly lu ul uu oll ul. w o lo u uol u z. ul l u oll ul. quk, oll, vl l, lk lo, - ul o u v (G) v Gl o oll. ul l u vlu oll ul uj
More informationINFLUENCE OF ANTICLIMBING DEVICE ON THE VARIATION OF LOADS ON WHEELS IN DIESEL ELECTRIC 4000 HP
U..B. Si. Bull., Si D, Vol.,., SS 454-5 UEE O AMBG DEVE O HE VARAO O OADS O WHEES DESE EER 4 H onl ătălin OESU Dipozitiul nt intodu ini uplimnt p oţil oiilo loomotilo. n lu pzint iti to ini, unţi d dtl
More informationClass Diagrams. CSC 440/540: Software Engineering Slide #1
Class Diagrams CSC 440/540: Software Engineering Slide # Topics. Design class diagrams (DCDs) 2. DCD development process 3. Associations and Attributes 4. Dependencies 5. Composition and Constraints 6.
More informationImproving Union. Implementation. Union-by-size Code. Union-by-Size Find Analysis. Path Compression! Improving Find find(e)
POW CSE 36: Dt Struturs Top #10 T Dynm (Equvln) Duo: Unon-y-Sz & Pt Comprsson Wk!! Luk MDowll Summr Qurtr 003 M! ZING Wt s Goo Mz? Mz Construton lortm Gvn: ollton o rooms V Conntons twn t rooms (ntlly
More informationP a g e 5 1 of R e p o r t P B 4 / 0 9
P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e
More information/99 $10.00 (c) 1999 IEEE
P t Hw Itt C Syt S 999 P t Hw Itt C Syt S - 999 A Nw Atv C At At Cu M Syt Y ZHANG Ittut Py P S, Uvty Tuu, I 0-87, J Att I t, w tv t t u yt x wt y tty, t wt tv w (LBSB) t. T w t t x t tty t uy ; tt, t x
More informationBACKFILLED 6" MIN TRENCH BOTT OF CONT FTG PROVIDE SLEEVE 1" CLR AROUND PIPE - TYP BOTTOM OF TRENCH PIPE SHALL NOT EXTEND BELOW THIS LINE
TT T I I. VTS T T, SPIS. 0 MSY ITS MS /" = '-0" +' - " T /" d ' - 0" I SIGTI d d w/ " M I S IS M I d 0 T d T d w/ å" 0 K W TT I IS ITPT SM SIZ x 0'-0" I T T W PSSI d /" TY I SIGTI Y ' - 0" '-0" '-0" P
More informationBACKFILLED 6" MIN TRENCH BOTT OF CONT FTG PROVIDE SLEEVE 1" CLR AROUND PIPE - TYP BOTTOM OF TRENCH PIPE SHALL NOT EXTEND BELOW THIS LINE
TT T I I. VTS T T, SPIS. 0 MSY ITS MS /" = '-0" +' - " T /" d ' - 0" I SIGTI d d w/ " M I S IS M I d 0 MI T d T d w/ å" 0 K W TT I IS ITPT SM SIZ x 0'-0" MI I T T W PSSI d /" TY I SIGTI Y ' - 0" MI '-0"
More informationRectangular Waveguides
Rtgulr Wvguids Wvguids tt://www.tllguid.o/wvguidlirit.tl Uss To rdu ttutio loss ig rquis ig owr C ort ol ov rti rquis Ats s ig-ss iltr Norll irulr or rtgulr W will ssu losslss rtgulr tt://www..surr..u/prsol/d.jris/wguid.tl
More informationModule graph.py. 1 Introduction. 2 Graph basics. 3 Module graph.py. 3.1 Objects. CS 231 Naomi Nishimura
Moul grph.py CS 231 Nomi Nishimur 1 Introution Just lik th Python list n th Python itionry provi wys of storing, ssing, n moifying t, grph n viw s wy of storing, ssing, n moifying t. Bus Python os not
More informationAsh Wednesday. First Introit thing. * Dómi- nos. di- di- nos, tú- ré- spi- Ps. ne. Dó- mi- Sál- vum. intra-vé-runt. Gló- ri-
sh Wdsdy 7 gn mult- tú- st Frst Intrt thng X-áud m. ns ní- m-sr-cór- Ps. -qu Ptr - m- Sál- vum m * usqu 1 d fc á-rum sp- m-sr-t- ó- num Gló- r- Fí- l- Sp-rí- : quó-n- m ntr-vé-runt á- n-mm c * m- quó-n-
More informationDUET WITH DIAMONDS COLOR SHIFTING BRACELET By Leslie Rogalski
Dut with Dimons Brlt DUET WITH DIAMONDS COLOR SHIFTING BRACELET By Lsli Roglski Photo y Anrw Wirth Supruo DUETS TM from BSmith rt olor shifting fft tht mks your work tk on lif of its own s you mov! This
More informationA TYP A-602 A-304 A-602 A-302 GRADE BEAM SEE 95% COMPACTED STRUCTURAL FILL A '-0"
W W/TITI -0 X U I I X TITI TY S W TYS TIS X W S SU XISTI -0-0 -0-0 -0-0 ' - " ' - " ' - " ' - " ' - " ' - /" ' - /" ' - " -STUTU I -0 ' - ' - " " ' - " " 0' - " ' - U I S STUT W'S TY UTI W S STUT W'S TY
More informationAdrian Sfarti University of California, 387 Soda Hall, UC Berkeley, California, USA
Innionl Jonl of Phoonis n Oil Thnolo Vol. 3 Iss. : 36-4 Jn 7 Rliisi Dnis n lonis in Unifol l n in Unifol Roin s-th Gnl ssions fo h loni 4-Vo Ponil in Sfi Unisi of Clifoni 387 So Hll UC Bkl Clifoni US s@ll.n
More informationMath 61 : Discrete Structures Final Exam Instructor: Ciprian Manolescu. You have 180 minutes.
Nm: UCA ID Numr: Stion lttr: th 61 : Disrt Struturs Finl Exm Instrutor: Ciprin nolsu You hv 180 minuts. No ooks, nots or lultors r llow. Do not us your own srth ppr. 1. (2 points h) Tru/Fls: Cirl th right
More informationSeries III, TV Breakaway Fail-Safe Connectors Quick-Disconnect with an Axial Pull of Lanyard
is, wy il- otos Qui-isot wit xil ull o y ulo ss quo mol i-tt wy il- otos ovi uqul om i viomts quii istt ismt. wy il- oto mily os wi o ltil mil tus: stt ouli m stio omltly itmtl wit st tls (/20 /2) vtoy
More information