CS 188: Artificial Intelligence Spring 2009

Similar documents
Announcements. CS 188: Artificial Intelligence Spring More Announcements. Today. From Last Time: Reflex Agents.

CS 188: Artificial Intelligence Spring Announcements

CS 188: Artificial Intelligence Fall Announcements

CS 188: Artificial Intelligence Fall Announcements

Announcements. CS 188: Artificial Intelligence Fall Today. Reflex Agents. Goal Based Agents. Search Problems

CS 188: Artificial Intelligence Fall 2011

Announcements. CS 188: Artificial Intelligence Fall Reflex Agents. Today. Goal Based Agents. Search Problems

CS 188: Artificial Intelligence Spring Announcements

CS 188: Artificial Intelligence Spring Announcements

Outline. CSE 473: Artificial Intelligence Spring Types of Agents

CS 188: Artificial Intelligence

Today. CS 232: Ar)ficial Intelligence. Search. Agents that Plan. September 3 rd, 2015 Search Problems. Uninformed Search Methods

Reminder. CS 188: Artificial Intelligence. A reflex agent for pacman. Reflex Agent. A reflex agent for pacman (3) A reflex agent for pacman (2)

CSE 573: Artificial Intelligence Autumn Search thru a. Goal Based Agents 9/28/2012. Agent vs. Environment. Example: N Queens

A search problem. Formalizing a search problem. Our Search Problem. Our Search Problem. Overview

Searching: Deterministic single-agent

Course Logistics Textbook: Artificial Intelligence: A Modern Approach, Russell and Norvig (3 rd ed) Topics

Announcements. CS 188: Artificial Intelligence Fall Office hours, Section. Today. DFS and BFS. Recap: Search. Lecture 3: A* Search 9/3/2009

Announcements. CS 188: Artificial Intelligence. Costs on Actions. Recap: Search. Lecture 3: A* Search

Exam 2 Solutions. Jonathan Turner 4/2/2012. CS 542 Advanced Data Structures and Algorithms

Self-Adjusting Top Trees

10.3 The Quadratic Formula

Search: Cost & Heuristics

Problem solving by search

T h e C S E T I P r o j e c t

Weighted Graphs. Weighted graphs may be either directed or undirected.

4.1 Interval Scheduling. Chapter 4. Greedy Algorithms. Interval Scheduling: Greedy Algorithms. Interval Scheduling. Interval scheduling.

d e c b a d c b a d e c b a a c a d c c e b

Tangram Fractions Overview: Students will analyze standard and nonstandard

(2) If we multiplied a row of B by λ, then the value is also multiplied by λ(here lambda could be 0). namely

1 Introduction to Modulo 7 Arithmetic

/99 $10.00 (c) 1999 IEEE

P a g e 3 6 of R e p o r t P B 4 / 0 9

KEB 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

Series III, TV Breakaway Fail-Safe Connectors Quick-Disconnect with an Axial Pull of Lanyard

CS 103 BFS Alorithm. Mark Redekopp

OpenMx Matrices and Operators

Appendix. In the absence of default risk, the benefit of the tax shield due to debt financing by the firm is 1 C E C

Who is this Great Team? Nickname. Strangest Gift/Friend. Hometown. Best Teacher. Hobby. Travel Destination. 8 G People, Places & Possibilities

Classical Theory of Fourier Series : Demystified and Generalised VIVEK V. RANE. The Institute of Science, 15, Madam Cama Road, Mumbai

Lecture 20: Minimum Spanning Trees (CLRS 23)

COMP108 Algorithmic Foundations

Easy Steps to build a part number... Tri-Start Series III CF P

CSE 373: AVL trees. Warmup: Warmup. Interlude: Exploring the balance invariant. AVL Trees: Invariants. AVL tree invariants review

Module graph.py. 1 Introduction. 2 Graph basics. 3 Module graph.py. 3.1 Objects. CS 231 Naomi Nishimura

Planar Upward Drawings

LWC 434 East First Street 4440 Garwood Place

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

Trade Patterns, Production networks, and Trade and employment in the Asia-US region

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)}

Winnie flies again. Winnie s Song. hat. A big tall hat Ten long toes A black magic wand A long red nose. nose. She s Winnie Winnie the Witch.

Outline. 1 Introduction. 2 Min-Cost Spanning Trees. 4 Example

b. How many ternary words of length 23 with eight 0 s, nine 1 s and six 2 s?

Improving Union. Implementation. Union-by-size Code. Union-by-Size Find Analysis. Path Compression! Improving Find find(e)

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)}

Ash Wednesday. First Introit thing. * Dómi- nos. di- di- nos, tú- ré- spi- Ps. ne. Dó- mi- Sál- vum. intra-vé-runt. Gló- ri-

Paths. Connectivity. Euler and Hamilton Paths. Planar graphs.

Distributed Set Reachability

L...,,...lllM" l)-""" Si_...,...

Multicast routing algorithm based on Extended Simulated Annealing Algorithm

Chapter 6 Perturbation theory

P a g e 5 1 of R e p o r t P B 4 / 0 9

Designing A Uniformly Loaded Arch Or Cable

learning objectives learn what graphs are in mathematical terms learn how to represent graphs in computers learn about typical graph algorithms

Math 166 Week in Review 2 Sections 1.1b, 1.2, 1.3, & 1.4

CSE 373: More on graphs; DFS and BFS. Michael Lee Wednesday, Feb 14, 2018

Three Phase Asymmetrical Load Flow for Four-Wire Distribution Networks

Life After Study Abroad

Gavilan JCCD Trustee Areas Plan Adopted November 10, 2015

Solutions for HW11. Exercise 34. (a) Use the recurrence relation t(g) = t(g e) + t(g/e) to count the number of spanning trees of v 1

Math 61 : Discrete Structures Final Exam Instructor: Ciprian Manolescu. You have 180 minutes.

Planar convex hulls (I)

Spanning Trees. BFS, DFS spanning tree Minimum spanning tree. March 28, 2018 Cinda Heeren / Geoffrey Tien 1

INFLUENCE OF ANTICLIMBING DEVICE ON THE VARIATION OF LOADS ON WHEELS IN DIESEL ELECTRIC 4000 HP

PLAYGROUND SALE Take up to 40% off. Plus FREE equipment * with select purchase DETAILS INSIDE

16.unified Introduction to Computers and Programming. SOLUTIONS to Examination 4/30/04 9:05am - 10:00am

NUCON 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

ENGO 431 Analytical Photogrammetry

EECE 301 Signals & Systems Prof. Mark Fowler

Housing Market Monitor

Stable Matching for Spectrum Market with Guaranteed Minimum Requirement

RUTH. land_of_israel: the *country *which God gave to his people in the *Old_Testament. [*map # 2]

An action with positive kinetic energy term for general relativity. T. Mei

Grade 7/8 Math Circles March 4/5, Graph Theory I- Solutions

Problem 1. Solution: = show that for a constant number of particles: c and V. a) Using the definitions of P

Depth First Search. Yufei Tao. Department of Computer Science and Engineering Chinese University of Hong Kong

Nefertiti. Echoes of. Regal components evoke visions of the past MULTIPLE STITCHES. designed by Helena Tang-Lim

February 12 th December 2018

C-Curves. An alternative to the use of hyperbolic decline curves S E R A F I M. Prepared by: Serafim Ltd. P. +44 (0)

CS 188: Artificial Intelligence Spring 2007

Proc. of the 23rd Intl. Conf. on Parallel Processing, St. Charles, Illinois, August 1994, vol. 3, pp. 227{ Hanan Samet

Language Processors F29LP2, Lecture 5

BACKFILLED 6" MIN TRENCH BOTT OF CONT FTG PROVIDE SLEEVE 1" CLR AROUND PIPE - TYP BOTTOM OF TRENCH PIPE SHALL NOT EXTEND BELOW THIS LINE

BACKFILLED 6" MIN TRENCH BOTT OF CONT FTG PROVIDE SLEEVE 1" CLR AROUND PIPE - TYP BOTTOM OF TRENCH PIPE SHALL NOT EXTEND BELOW THIS LINE

An undirected graph G = (V, E) V a set of vertices E a set of unordered edges (v,w) where v, w in V

Rectangular Waveguides

Beechwood Music Department Staff

Mid Year Examination F.4 Mathematics Module 1 (Calculus & Statistics) Suggested Solutions

Daily Skill Practice

(Minimum) Spanning Trees

Transcription:

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 T l is otionl, ut t ssignmnt is not I you sumit, you won t gt n mil yt Pojt 1: Post tonigt Du in two wks: Wnsy, F 4 tt ly n sk ustions. It s long tn most! 1

Mo Announmnts tion tion stts Mony tion 104 om 5m - 6m will l in 9 Evns Tims n lotions will on t wsit sotly Oi ous My nw oi ous: Tus 3-4 n W 11-1 GI oi ous (o will ) on t wsit Toy Agnts tt Pln A Polms Uninom Mtos (viw o mny) Dt-Fist Bt-Fist Uniom-Cost Huisti Mtos (nw mtil) Gy

Fom Lst Tim: Rlx Agnts Rlx gnts: Coos tion s on unt t n mmoy Do not onsi utu onsuns o ti tions Cn lx gnt tionl? How goo ws ou gnt om lst lss? Rmin: t oo i it ws t; voi gosts Aginst nom gosts: won 31% o t tim On t oiginl Pmn m: 5% win t Aginst lx gosts on smll m: 3% win t Gol Bs Agnts Gol-s gnts: Pln Mk isions s on (yotsiz) onsuns o tions Must v mol o ow t wol volvs in sons to tions [mo: ln st/ ln otiml] 3

Polms A s olm onsists o: A stt s A susso untion N, 1.0 A stt stt n gol tst E, 1.0 A solution is sun o tions ( ln) wi tnsoms t stt stt to gol stt How Big is t tt? Polm: Et ll o t oo Pmn s ositions: 10 x 1 Foo ount: 30 Gost ositions: 1 x 1 Ditions: u, own, lt, igt, sto 4

Ts N, 1.0 E, 1.0 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 tt Gs Fo vy s olm, t s osoning g o t stt s T susso untion is snt y s G W n ly uil tis g in mmoy Lugly tiny s g o tiny s olm 5

Gnl T Dtil suoo is in t ook! T Initiliz t oot no o t s t wit t stt stt Wil t unxn l nos (ing): Coos l no (sttgy) I t no ontins gol stt: tun t osoning solution Els: xn t no n its iln to t t Imotnt is: Fing Exnsion ttgy: wi ing nos to xlo? Exml: T G 6

tts vs. Nos tt s gs v olm stts Rsnt n stt stt o t wol Hv sussos, n gol / non-gol, v multil ssos ts v s nos Rsnt ln (t) wi sults in t no s stt Hv olm stt n on nt, t lngt, t & ost T sm olm stt my in multil s t nos Polm tts Nos Pnt Dt 5 No Ation Dt 6 tt Gs vs Ts G E NODE in in t s t is n nti PATH in t olm g. W lmost lwys onstut ot on mn n w onstut s littl s ossil. G G 7

8 Rviw: Dt Fist G G G ttgy: xn st no ist Imlmnttion: Fing is LIFO stk Rviw: Bt Fist G G G Tis ttgy: xn sllowst no ist Imlmnttion: Fing is FIFO uu

Algoitm Potis Comlt? Gunt to in solution i on xists? Otiml? Gunt to in t lst ost t? Tim omlxity? omlxity? Vils: n Num o stts in t olm 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 DF Algoitm Comlt Otiml Tim DF Dt Fist N N N N O(B Ininit LMAX ) O(LMAX) Ininit TART GOAL Ininit ts mk DF inomlt How n w ix tis? 9

DF Wit yl king, DF is omlt. 1 no nos nos m tis m nos Algoitm Comlt Otiml Tim DF w/ Pt Cking Y N O( m+1 ) O(m) Wn is DF otiml? BF Algoitm Comlt Otiml Tim DF BF w/ Pt Cking Y N O( m+1 ) O(m) Y N* O( s+1 ) O( s ) s tis 1 no nos nos s nos m nos Wn is BF otiml? 10

Ittiv Dning Ittiv ning uss DF s suoutin: 1. Do DF wi only ss o ts o lngt 1 o lss.. I 1 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+1 ) O(m) Y N* O( s+1 ) O( s ) Y N* O( s+1 ) O(s) Costs on Ations TART 3 1 1 3 8 9 8 4 4 15 GOAL 1 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. 11

Uniom Cost Exn st no ist: Fing is ioity uu Cost ontous 4 6 11 3 9 1 13 5 7 8 11 G 10 1 17 11 3 0 G 1 15 8 9 8 1 16 G 1 Pioity Quu Rs 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 1

Uniom Cost Algoitm Comlt Otiml Tim DF BF UC w/ Pt Cking Y N O( m+1 ) O(m) Y N O( s+1 ) O( s ) Y* Y O( C*/ ) O( C*/ ) C*/ tis You n mo out uniom ost s s ilu in t ook, o y sking us 5 Minut Bk A Dn Gillik oiginl 13

Uniom Cost Issus Rmm: xlos insing ost ontous T goo: UC is omlt n otiml! 1 3 T : Exlos otions in vy ition No inomtion out gol lotion tt Gol [mo: us ontous ] Huistis Any stimt o ow los stt is to gol Dsign o tiul s olm Exmls: Mnttn istn, Eulin istn 10 5 11. 14

Bst Fist / Gy ttgy: xn t losst no to t gol G =8 =0 1 8 =5 =4 5 =11 3 =8 9 1 9 =4 1 4 =6 5 =1 15 4 3 =11 =9 =6 [mo: gy] Bst Fist / Gy 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 Lik DF in omltnss (init stts w/ yl king) 15

Gon Wong? Ext Wok? Filu to tt t stts n us xonntilly mo wok (wy?) 16

G In BF, o xml, w souln t ot xning t il nos (wy?) G G G Vy siml ix: nv xn stt ty twi Cn tis wk omltnss? Wy o wy not? How out otimlity? Wy o wy not? 17

om Hints G s is lmost lwys tt tn t s (wn not?) Imlmnt you los list s it o st! Nos ontully ts, ut tt to snt wit stt, ost, lst tion, n n to t nt no 18