Bai Giang Tri Tuen Han Tao

5/28/2018 Bai Giang Tri Tuen Han Tao

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TS. Nguyn nh ThunKhoa Cng nghThng tin

i hc Nha TrangEmail: [email protected]

TR TUNHN TO

Artificial Intelligence

Nha Trang 8-2007


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Slide 2

Noi dung mon hoc

Chng 1: Gii thiu

Mu Lnh vc nghin cu ca AI

ng dng ca AI

Cc vn t ra


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Slide 3

Noi dung mon hoc (tip)

Chng 2:Tm kim trn khng gian trng thi

Bi ton tm kim Gii thut tng qut Depth first search (DFS) Breath first search (BFS)

Chng 3:Tm kim theo Heuristic Gii thiu vHeuristic

Tm kim theo heuristic Gii thut Best first search (BFS), Gii thut AT, AKT, A* Chin lc Minimax, Alpha Beta


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Slide 4

Noi dung mon hoc (tip)

Chng 4:Biu din tri thc

Bba i tng Thuc tnh Gi tr Cc lut dn Mng ngngha Frame

Logic mnh , Logic vt Thut gii Vng Ho, Thut gii Robinson

Chng 5: My hc

Cc hnh thc hc Thut gii Quinland Hc theo bt nh


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Slide 5

Thc hnh &Ti liu tham kho

Thc hnh Prolog / C++ / Pascal

Cc gii thut tm kim Biu din tri thc

Bi tp ln

Ti liu tham kho Bi ging Tr tunhn to TS Nguyn nh Thun

Gio trnh Tr tunhn to - GS Hong KimHQGTPHCM

Tr tunhn toPGS Nguyn Thanh ThyH Bch Khoa HNi Artificial Inteligent George F. Luget & Cilliam A. Stubblefied


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Chng 1: GII THIEU


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Slide 7

1.1 Mu

Tr tul g:

Theo tin Bch khoa ton thWebster: Tr tul khnng:

Phn ng mt cch thch hp li nhng tnh hung

mi thng qua iu chnh hnh vi mt cch thchhp.

Hiu r mi lin hgia cc skin ca thgii bn

ngoi nhm a ra nhng hnh vi ph hp tc mc ch.


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Slide 8

SThng Minh

Khi nim vtnh thng minh ca mt i

tng thng biu hin qua cc hot ng:Shiu bit v nhn thc c tri thc

Sl lun to ra tri thc mi da trn tri thc c

Hnh ng theo kt quca cc l lun

Knng (Skill)

TRI THC ???


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Slide 9

Tri thc (Knowledge)

Tri thc l nhng thng tin cha ng 2 thnh phn Cc khi nim:

Cc khi nim cbn: l cc khi nim mang tnh quy c Cc khi nim pht trin: c hnh thnh tcc khc nim cbn

thnh cc khi nim phc hp phc tp hn.

Cc phng php nhn thc: Cc qui lut, cc thtc Phng php suy din, l lun,..

Tri thc l iu kin tin quyt ca cc hnh xthng minh haySthng minh

Tri thc c c qua sthu thp tri thc v sn sinh tri thc

Qu trnh thu thp v sn sinh tri thc l hai qu trnh song song vni tip vi nhau khng bao gichm dt trong mt thc th

Thng Minh


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Slide 10

Tri thc Thu thp v sn sinh

Thu thp tri thc: Tri thc c thu thp tthng tin, l kt quca mt qu

trnh thu nhn dliu, xl v lu tr. Thng thng qutrnh thu thp tri thc gm cc bc sau: Xc nh lnh vc/phm vi tri thc cn quan tm

Thu thp dliu lin quan di dng cc trng hp cth.

Hthng ha, rt ra nhng thng tin tng qut, i din cho cctrng hp bit Tng qut ha.

Xem xt v gili nhng thng tin lin quan n vn cn quantm , ta c cc tri thc vvn .

Sn sinh tri thc: Tri thc sau khi c thu thp sc a vo mng tri thc c.

Trn cs thc hin cc lin kt, suy din, kim chng sn sinh ra

cc tri thc mi.


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Slide 11

Tri thc Tri thc siu cp

Tr thc siu cp (meta knowledge) hay Tri thc vTri thc L cc tri thc dng :

nh gi tri thc khc

nh gi kt quca qu trnh suy din

Kim chng cc tri thc mi

Phng tin truyn tri thc: ngn ngtnhin


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Slide 12

Hanh x thong minh Ket luan

Hnh xthng minh khng n thun l cc hnh ng nhl ktquca qu trnh thu thp tri thc v suy lun trn tri thc.

Hnh xthng minh cn bao hm Stng tc vi mi trng nhn cc phn hi Stip nhn cc phn hi iu chnh hnh ng - Skill Stip nhn cc phn hi hiu chnh v cp nht tri thc

Tnh cht thng minh ca mt i tng l stng hp ca c3yu t: thu thp tri thc, suy lun v hnh xca i tng trn trithc thu thp c. Chng ha quyn vo nhau thnh mt ththng nht SThng Minh

Khng thnh gi ring lbt kmt kha cnh no ni vtnh thng minh. THNG MINH CN TRI THC


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Slide 13

1.2 i tng nghien cu cua AI

AI l lnh vc ca Cng nghthng tin, c chc nng nghincu v to ra cc chng trnh m phng hot ng tduy ca

con ngi. Tr tunhn to nhm to ra My ngi?

Mc tiu

Xy dng l thuyt vthng minh gii thch cc hot ngthng minh

Tm hiu cchsthng minh ca con ngi Cchlu trtri thc

Cchkhai thc tri thc Xy dng cchhin thc sthng minh

p dng cc hiu bit ny vo cc my mc phc vcon

ngi.


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Slide 14

1.2 i tng nghien cu cua AI(tip)

AI l ngnh nghin cu vcch hnh x thng minh(intellgent behaviour) bao gm: thu thp, lu tr tri

thc, suy lun, hot ng v knng.i tng nghin cu l cc hnh x thng minh

chkhng phi l sthng minh.

Gii quyt bi ton bng AI l tm cch biu din trithc, tm cch vn dng tri thcgii quyt vn v tm cch bsung tri thc bng cch pht hin tri

thc tnhng thng tin sn c (my hc)


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Slide 15

1.3 Lch spht trin ca AI :Giai on cin

Giai on cin (1950 1965)C 2 kthut tm kim cbn:

Kthut generate and test : chtm c 1 p n/ chachc ti u.

Kthut Exhaustive search (vt cn): Tm tt cccnghim, chn la phng n tt nht.

(Bng nthp m(Bng nthp mnn vi m>=10)vi m>=10)


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Slide 16

Lch spht trin ca AI :Giai on vin vng

Giai on vin vng (1965 1975) y l giai on pht trin vi tham vng lm cho my hiu c

con ngi qua ngn ngtnhin. Cc cng trnh nghin cu tp trung vo vic biu din tri thc v

phng thc giao tip gia ngi v my bng ngn ngtnhin.

Kt qukhng my khquan nhng cng tm ra c cc phngthc biu din tri thc vn cn c dng n ngy nay tuy chatht tt nh: Semantic Network (mng ngngha)

Conceptial graph (thkhi nim) Frame (khung)

Script (kch bn)Vp phi trngi vnng lcVp phi trngi vnng lc

ca my tnhca my tnh


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Slide 17

Lch spht trin ca AI :Giai on hin i

Giai on hin i (t1975) Xc nh li mc tiu mang tnh thc tin hn ca AI:

Tm ra li gii tt nht trong khong thi gian chp nhn c. Khng cu ton tm ra li gii ti u

Tinh thn HEURISTIC ra i v c p dng mnh mkhcphc bng nthp.

Khng nh vai tr ca tri thc ng thi xc nh 2 trngi ln lbiu din tri thc v bng nthp.

Nu cao vai tr ca Heuristic nhng cng khng nh tnh kh khn

trong nh gi heuristic.

Better than nothingBetter than nothingPht trin ng dng mnh m: Hchuyn gia,Hchun on,..


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Slide 18

1.4 Cc lnh vc ng dng

Game Playing: Tm kim / Heuristic

Automatic reasoning & Theorem proving: Tm kim / Heuristic Expert System: l hng pht trin mnh mnht v c gi trng

dng cao nht.

Planning & Robotic: cc hthng dbo, tng ha

Machine learning: Trang bkhnng hc tp gii quyt vn khotri thc:

Supervised : Kim sot c tri thc hc c. Khng tm ra ci mi.

UnSupervised:Thc, khng kim sot. C thto ra tri thc minhng cngnguy him v c thhc nhng iu khng mong mun.


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Slide 19

1.4 Cc lnh vc ng dng(tip)

Natural Language Understanding & Semantic modelling:Khngc pht trin mnh do mc phc tp ca biton cvtri thc & khnng suy lun.

Modeling Human perfromance: Nghin cu cchtchctr tuca con ngi p dng cho my.

Language and Environment for AI:Pht trin cng cv mitrng xy dng cc ng dng AI.

Neural network / Parallel Distributed processing: gii quytvn nng lc tnh ton v tc tnh ton bng kthutsong song v m phng mng thn kinh ca con ngi.


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Slide 20

ng dung AI

M hnh ng dng AI hin ti:

AI = Presentation & SearchAI = Presentation & Search Mc d mc tiu ti thng ca ngnh TTNT l xy dng mt chic my c

nng lc tduy tng tnhcon ngi nhng khnng hin ti ca tt ccc sn phm TTNT vn cn rt khim tn so vi mc tiu ra. Tuyvy, ngnh khoa hc mi mny vn ang tin bmi ngy v ang tra

ngy cng hu dng trong mt scng vic i hi tr thng minh ca conngi. Hnh nh sau sgip bn hnh dung c tnh hnh ca ngnh tr tunhn to.


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Slide 21

Cc bi ton

Xt cc bi ton sau:

1. i tin (Vt cn v Heuristic)

2. Tm kim chiu rng v su3. Tic tac toe.

4. ong du.

5. Bi ton TSP6. 8 puzzle.

7. Cvua

8. Ctng9. Ngi nng dn qua sng.

10. Con thv con co

11.

Con khv ni chui


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Chng 2: TM KIM TRN KHNGGIAN TRNG THI

(State Space Search)


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Slide 23

Bi ton tm kim

Tm kim ci g?

Biu din v tm kim l kthut phbin gii cc biton trong lnh vc AI

Cc vn kh khn trong tm kim vi cc bi tonAI c tvn phc tp

Khng gian tm kim ln

c tnh i tng tm kim thay i

p ng thi gian thc Meta knowledge v kt quti u

Kh khn vkthut


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Slide 24

Cu trc chung ca bi ton tm kim

Mt cch chung nht, nhiu vn -bi ton phc tp uc dng "tm ng i trong th" hay ni mt cch

hnh thc hn l "xut pht tmt nh ca mt th,tm ng i hiu qunht n mt nh no ".

Mt pht biu khc thng gp ca dng bi ton ny l:

Cho trc hai trng thi T0 v TG hy xy dng chui trng thiT0, T1, T2, ..., Tn-1, Tn = TG sao cho :

tha mn mt iu kin cho trc (thng l nhnht).


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Slide 25

2.2 Gii thut tng qut

K hiu:s nh xut pht

g:nh chn:nh ang xt

(n): tp cc nh c th i trc tip t nh n

Open: tp cc nh c th xt bc k tipClose: tp cc nh xt


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Slide 26

2.2 Gii thut tng qut (tip)

Begin

Open := {s};

Close := ;While (Open ) do

begin

n:=Retrieve(Open);if (n=g) then Return True;

Open := Open (n); // ((n) Close)

Close := Close {n};

end;

Return False;

End;


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Slide 27

V d:

Xt graph sau:

A

B C D

E F G

H I J

s = A l nh bt ug= G l nh ch


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Slide 28

2.3 Breath First Search V d

Xt graph sau:

A

B C D

E F G

H I J

{A}

{A, B}{A, B, C}

{A, B, C, D}

{A, B, C, D, E}

{A, B, C, D, E, F}

{A}

{B, C, D}

{C, D, E, F}{D, E, F, G}

{E, F, G}

{F, G, H, I}

{G, H, I, J}

{B, C, D}

{E, F}{F, G}

{H, I}

{J}

True

A

BC

D

E

FG

0

1

23

4

5

67

CloseOpen(n)nLn lp


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Slide 29

2.3 Breath First Search V d1

Xt graph sau:A->U

A

B C D

E F G

H I J

{A}

{A, B}{A, B, C}

{A, B, C, D}

{A, B, C, D, E}

{A, B, C, D, E, F}{A, B, C, D, E, F,G}

{A,B,C, D, E, F,G,H}

{A,B,C, D, E,F,G,H,I}

{A,B,C, D, E,F,G,H,I,J}

{A}

{B,C,D}

{C,D, E,F}{D,E, F,G}

{E, F, G}

{F, G, H, I}

{G, H, I, J}{H, I, J}

{I, J}

{J}

{B, C, D}

{E, F}{F, G}

{H, I}

{J}

FALSE

A

BC

D

E

FG

H

I

J

0

1

23

4

5

67

8

9

10

CloseOpen(n)nLn lp


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Slide 30

V d:

Xt graph sau:

A

B C D

E F G

H I J


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Slide 31

2.4 Depth First Search V d

Xt graph sau:

A

B C D

E F G

H I J

{A}

{A, B}

{A, B, E}

{A, B, E, H}

{A, B, E, H, I}

{A, B, E, H, I, F}{A, B, E, H, I, F,J}

{A,B,E,H,I, F,J,C}

{A}

{B, C, D}

{E, F, C, D}

{H, I, F, C, D}

{I, F, C, D}

{F, C, D}

{J, C, D}

{C, D}

{G, D}

{B, C, D}

{E, F}

{H, I}

{J}

{F, G}

True

A

B

E

H

I

F

J

C

G

0

1

2

3

4

5

6

7

8

9

CloseOpen(n)nLn lp


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Slide 32

Breath First vs Depth First

Breath First: openc tchc dng FIFO

Depth First: open c tchc dng LIFO

Hiu qu Breath First lun tm ra nghim c scung nhnht

Depth First thng cho kt qunhanh hn.

Kt qu BFS, DFS chc chn tm ra kt qunu c.

Bng nthp l kh khn ln nht cho cc gii thutny.Gii Php cho bng nthp??


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Slide 33

Tm Kim Rng

1. Open = [A]; closed = []2. Open = [B,C,D];

closed = [A]

2. Open = [C,D,E,F];closed = [B,A]3. Open = [D,E,F,G,H]; closed = [C,B,A]4. Open = [E,F,G,H,I,J]; closed = [D,C,B,A]5. Open = [F,G,H,I,J,K,L];closed = [E,D,C,B,A]

6. Open = [G,H,I,J,K,L,M];(v L c trong open);

closed = [F,E,D,C,B,A]


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Slide 34

Tm kim Su

1. Open = [A]; closed = []2. Open = [B,C,D]; closed = [A]3. Open = [E,F,C,D];closed = [B,A]

4. Open = [K,L,F,C,D];closed = [E,B,A]5. Open = [S,L,F,C,D];

closed = [K,E,B,A]6. Open = [L,F,C,D];

closed = [S,K,E,B,A]7. Open = [T,F,C,D];

closed = [L,S,K,E,B,A]8. Open = [F,C,D];

closed = [T,L,S,K,E,B,A]

9.


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Slide 35

Depth first search c gii hn

Depth first search c khnng lp v tn do cc trngthi con sinh ra lin tc. su tng v tn.

Khc phc bng cch gii hn su ca gii thut. Su bao nhiu th va?

Chin lc gii hn: Cnh mt su MAX, nhcc danh thchi ctnh

trc c snc nht nh

Theo cu hnh resource ca my tnh

Meta knowledge trong vic nh gii hn su.

Gii hn su => co hp khng gian trng thi => cthmt nghim.


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Chng 3: HEURISTIC SEARCH


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Slide 37

3.1 Gii thiu vHeuristic

Heuristic l g? Heuristic l nhng tri thc c rt ta tnhng kinh

nghim, trc gic ca con ngi. Heuristic c thl nhng tri thc ng hay sai.

Heuristic l nhng meta knowledge v thng ng.

Heuristic dng lm g? Trong nhng bi ton tm kim trn khng gian trng thi, c

2 trng hp cn n heuristic: Vn c thkhng c nghim chnh xc do cc mnh khng pht

biu cht chhay thiu dliu khng nh kt qu. Vn c nghim chnh xc nhng ph tn tnh ton tm ra nghim

l qu ln (hquca bng nthp)

Heuristic gip tm kim t kt quvi chi ph thp hn

H i i ( i )


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Slide 38

Heuristic (tip)

Thut gii Heuristic l mt s m rng khinim thut ton. N thhin cch gii bi ton

vi cc c tnh sau: Thng tm c li gii tt (nhng khng chc l

li gii tt nht)

Gii bi ton theo thut gii Heuristic thng ddng v nhanh chng a ra kt quhn so vi giithut ti u, v vy chi ph thp hn.

Thut gii Heuristic thng th hin kh t nhin,gn gi vi cch suy ngh v hnh ng ca conngi.

H i i ( i )


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Slide 39

Heuristic (tip)

C nhiu phng php xy dng mt thut gii Heuristic, trongngi ta thng da vo mt snguyn l cbn nhsau:

Nguyn l vt cn thng minh: Trong mt bi ton tm kim no , khi khnggian tm kim ln, ta thng tm cch gii hn li khng gian tm kim hocthc hin mt kiu d tm c bit da vo c th ca bi ton nhanh chngtm ra mc tiu.

Nguyn l tham lam (Greedy): Ly tiu chun ti u (trn phm vi ton cc)

ca bi ton lm tiu chun chn la hnh ng cho phm vi cc bca tngbc (hay tng giai on) trong qu trnh tm kim li gii.

Nguyn l tht: Thc hin hnh ng da trn mt cu trc ththp l cakhng gian kho st nhm nhanh chng t c mt li gii tt.

Hm Heuristic: Trong vic xy dng cc thut gii Heuristic, ngi ta thngdng cc hm Heuristic. l cc hm nh gi th, gi trca hm phthucvo trng thi hin ti ca bi ton ti mi bc gii. Nhgi trny, ta c thchn c cch hnh ng tng i hp l trong tng bc ca thut gii.

H i ti G d


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Slide 40

Heuristic Greedy

Bi ton i tin: i stin n thnh cc loi tin chotrc sao cho stl t nht

Bi ton hnh trnh ngn nht (TSP): Hy tm mt hnhtrnh cho mt ngi giao hng i qua n im khc nhau,mi im i qua mt ln v trvim xut pht sao cho

tng chiu di on ng cn i l ngn nht. Gisrng c con ng ni trc tip tgia hai im bt k. Vt cn: (n-1)! (Vi n ln ???)

Greedy 1: Mi bc chn i j sao cho j gn i nht trong nhngnh ni vi i cn li

Greedy 2: Mi bc chn i j sao cho i gn j nht trong nhngnh ni vi j cn li

V d TSP i 8


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Slide 41

V d: TSP vi n=8

0660690200900116047010108

66003904605709205403807

69039005209506003004306

2004605200740105050080059005709507400142010408404

11609206001050142007106403

470540300500104071007302

101038043080084064073001

87654321

V d TSP i 8


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Slide 42

V d: TSP vi n=8

*Vi Greedy 1:

1 7 6 2 8 5 4 3 1

Tng chi ph: 4540*Vi Greedy 2:

1 7 4 5 8 2 6 3 1

Tng chi ph: 3900

Bi ton 3: Bi ton t mu bn

Heuristic (tt)


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Slide 43

Heuristic (tt)

Heuristic dng nhthno trong tm kim? Tm kim trn khng gian trng thi theo chiu no? Su

hay rng? Tm theo Heuristic : Heuristic nh hng qu trnh tm

kim theo hng m n cho rng khnng t ti nghiml cao nht. Khng su cng khng rng

Kt quca tm kim vi Heuristic Vic tm kim theo nh hng ca heuristic c kt qutt

hay xu ty theo heuristic ng hay sai.

Heuristic c khnng bst nghim

Heuristic cng tt cng dn n kt qunhanh v tt.

Lm sao tm c Heuristic tt???Lm sao tm c Heuristic tt???

3 2 T ki ti (B t Fi t S h)


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Slide 44

3.2 Tm kim ti u (Best First Search)

OPEN : tp cha cc trng thi c sinh ra nhng cha c xt n (v ta chn mt trng thi khc). Thc ra, OPEN l mt loi hng i u tin(priority queue) m trong , phn tc u tin cao nht l phn ttt nht.

CLOSE : tp cha cc trng thi c xt n. Chng ta cn lu trnhngtrng thi ny trong bnhphng trng hp khi mt trng thi mi cto ra li trng vi mt trng thi m ta xt n trc .

Thut gii BEST-FIRST SEARCH

1.t OPEN cha trng thi khi u.2. Cho n khi tm c trng thi ch hoc khng cn nt no trong OPEN, thchin :

2.a. Chn trng thi tt nht (Tmax) trong OPEN (v xa Tmax khi OPEN)

2.b. Nu Tmax l trng thi kt thc th thot.2.c. Ngc li, to ra cc trng thi ktip Tk c thc ttrng thi Tmax.i vi mi trng thi ktip Tk thc hin : Tnh f(Tk); Thm Tk vo OPEN

3 2 Tm kim ti u (tip)


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Slide 45

3.2 Tm kim ti u (tip)

Thut gii BEST-FIRST SEARCH

Beginopen:={s};

While (open ) dobeginn:= Retrieve(Open) //Chn trng thi tt nht tOpen.if(n=g) then return Trueelse begin

To (n)for mi nt con m ca (n) do

Gn gi trchi ph cho mOpen:=Open{m};

end;Return False;

End;

Begin

Open := {s};

Close := ;

While (Open ) do

begin n:=Retrieve(Open);

if (n=g) then Return True;

Open := Open (n); // ((n) Close)

Close := Close {n};

end; Return False;

End;

3 2 Tm kim ti (tip)


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Slide 46

3.2 Tm kim ti u (tip)

- BFS kh n gin. Tuy vy, trn thc t, cng nh tm kimchiu su v chiu rng, him khi ta dng BFS mt cch trctip. Thng thng, ngi ta thng dng cc phin bn caBFS l AT, AKT v A*Thng tin vqu khv tng lai

-Thng thng, trong cc phng n tm kim theo kiu BFS,

tt fca mt trng thi c tnh da theo 2 hai gi trm tagi l l g v h. h chng ta bit, l mt c lng vchiph t trng thi hin hnh cho n trng thi ch (thng tintng lai). Cn g l "chiu di qung ng" i ttrng thi

ban u cho n trng thi hin ti (thng tin qu kh). Lu rng g l chi ph thc s(khng phi chi ph c lng).

3 3 Thut gii AT


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Slide 47

3.3 Thut gii AT

Phn bit khi nim g v h

3 3 Thut gii AT


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Slide 48

3.3 Thut gii AT

Thut gii AT l mt phng php tm kim theo kiu BFS vitt ca nt l gi trhm g tng chiu di con ng ittrng thi bt u n trng thi hin ti.

Beginopen:={s};While (open ) do

beginn:= Retrieve(Open) //Chn n sao cho g(n) nh nht tOpen.if(n=g) then return Trueelse begin


if (mOpen) thenBegin

g(m):=g(n)+Cost(n,m)Open:=Open{m};

endelse So snh g(m) va gNew (m) v cp nht

end;Return False;

3 3 Thut gii CMS (Cost Minimazation Search)


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Slide 49

3.3 Thut gii CMS (Cost Minimazation Search)

Thut gii CMS l mt phng php tm kim theo kiu BFS vi tt cant l gi trhm g v bsung tp Close: tp nh xt).Begin

open:={s}; close :=

While (open ) dobeginn:= Retrieve(Open) //Chn n sao cho g(n) nh nht tOpen.if(n=g) then return Trueelse begin

To (n)for mi nt con m ca (n) doif (mOpen) and (mClose) then

Beging(m):=g(n)+Cost(n,m)

Open:=Open{m};endelse So snh g(m) va gNew (m) v cp nht

close = close {n}end;

Return False;End

V d:


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Slide 50

V d:

Xt graph sau:

A

B C D

E F G

H I J

s = A l nh bt ug= J l nh ch

3520 30

1545

30

40

25 10

10

20

V d:


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Slide 51

V d:

Xt graph sau:

(E,60),(J,60)(J}F

(C,35), (D,30),(E,60),(F,65){E,F}B

(C,35),(E,60),(F,65)D

(E,60),(F,50),(G,45){F,G}C

(E,60),(F,50),(J,65){J}G

J

{(B,20), (C,35), (D,30)}{B,C,D}A1

{(A,0)}0

Open(n)nLn lp

s = A l nh bt ug= J l nh ch

DA

EBFC

GC

JF

CABA

A*

SauTrc

3 4 Thut gii AKT


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Slide 52

3.4 Thut gii A

(Algorithm for Knowlegeable Tree Search)

Thut gii AKT mrng AT bng cch sdng thm thng tin c lng h. ttca mt trng thi f l tng ca hai hm g v h.

Begin

open:={s};While (open ) do

beginn:= Retrieve(Open) //Chn n sao cho f(n) nh nht tOpen.if(n=g) then return True

else beginTo (n)for mi nt con m ca (n) do

Beging(m):=g(n)+Cost(n,m)

f(m):= g(m)+h(m);Open:=Open{m};

end;end;

Return False;

End;

3 5 Thut gii A*


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Slide 53

3.5 Thut gii A

Thut gii A*A* l mt phin bn c bit ca AKT p dng cho trng hp th.Thut gii A* c s dng thm tp hp CLOSE lu tr nhngtrng hp c xt n. A* mrng AKT bng cch bsung cchgii quyt trng hp khi "m" mt nt m nt ny c sn trongOPEN hoc CLOSE.

3.5 Thut gii A* (tip)


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Slide 54

3.5 Thut gii A (tip)

Beginopen:={s}; close:=;

While (open ) do

beginn:= Retrieve(Open) //sao cho f(n) min.if(n=g) then return path ts n gelse begin


case m ofm Open v m Close:

beginGn gi trheuristic cho mOpen:=Open{m};

end;

m Open:ifn c m bng mt path ngn hnthen Cp nht li m trong Open.

m Closeifn c m bng mt path ngn hnthen begin

Close:=Close-{m}

Open:=Open{m}end;

end; /*end case*/

Close:=Close{n}

end; / while/return false;End;

Hm lng gi Heuristic


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Slide 55

g g eu st c

Hm lng gi Heuristic l hm c lng ph tn i ttrng thihin ti n trng thi goal.

Csxc nh hm lng gi l da vo tri thc/kinh nghim thuthp c.

Hm lng gi cho kt qung (gn thc th) hay sai (xa gi trthc) sdn n kt qutm c tt hay xu.

Khng c chun mc cho vic nh gi mt hm lng giHeuristic. L do:

Khng c cu trc chung cho hm lng gi

Tnh ng/sai thay i lin tc theo tng vn cth Tnh ng/sai thay i theo tng tnh hung cthtrong mt vn

C thdng nhiu hm lng gi khc nhau theo tnh hung cn hm lng gi vcc hm lng gi.

Tr8 hay 15


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Slide 56

y

Trng thi ban u Trng thi ch Tr

15

Tr8

Cn biu din KGTT cho bi ton ny nhthno?

78910

615115141312

4321

567

48

321

38129

1513215610

741411

126

753

82

C 3 Tm kim khng gian trng thi

Thut gii A* V d


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Slide 57

g

l Xt bi ton 8 pusslevi goal l:

567

48

321

Heuristic 1: Tng sming sai vtrHeuristic 2: Tngkhong cch sai v tr

ca tng ming.

65

43

6557

461

382

567

41

382

57

461

382

Vic chn la hm Heuristic l kh khn v c ngha quyt nh i vi tc ca gii thut

Hm lng gi Heuristic Cu trc


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Slide 58

g g

Xt li hot ng ca gii thut Best First Search: Khi c 2 nt cng c gi trkvng t n mc tiu bng nhau th nt c

path tnt bt u n nt ngn hn sc chn trc nhvy nt ny

c gi trHeuristic tt hn. Hay ni cch khc hm lng gi Heuristic cho nt gn start hn l tt hn

nu kvng n goal l bng nhau.

Vy chn nt no nu kvng ca 2 nt khc nhau? Nt kvng tt hn

nhng xa start hay nt kvng xu hn nhng gn rootHm lng gi bao gm c2 v c cu trc:

F(n) := G(n) + H(n)

G(n): ph tn thc troot n nH(n): ph tn c lung heuristic tn n goal.

Thut gii A* V d


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Slide 59

g

l Xt v dl bi ton 8 puzzle vi:

57461

382

Bt u

56748

321

ch

Hm lng gi: F(n) = G(n) + H(n)Vi G(n): sln chuyn vtr thc hin

H(n): Sming nm sai vtrNt X c gi trheuristic tt hn nt Y nu F(x) < F(y).

Ta c hot ng ca gii thut Best First search trn nhhnh sau:

3.5 Thut gii A* (tip)


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Slide 60

Beginopen:={s}; close:=;

While (open ) dobegin

n:= Retrieve(Open) //sao cho f(n) min.if(n=g) then return path ts n gelse begin


case m ofm Open v m Close:

beginGn gi trheuristic cho mOpen:=Open{m};

end;

m Open:ifn c m bng mt path ngn hnthen Cp nht li m trong Open.

m Closeifn c m bng mt path ngn hnthen begin

Close:=Close-{m}

Open:=Open{m}end;

end; /*end case*/

Close:=Close{n}end; / while/return false;

End;

V d


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Slide 61

57

461

3821 State A

F(a) =0+4=4

57

461

382x State B

F(b) =1+5=6 56741

3822 State C

F(c) =1+3=4 57461

382x State D

F(D) =1+5=6

567

41

3823 State E

F(e) =2+3=5 567481

324 State F

F(f) =2+3=5 56741

382x State G

F(g) =2+4=6

567

412

38x State H

F(h) =3+3=6 56417

382x State I

F(i) =3+4=7

57

461

382

567

41

382

V d


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Slide 62

567

481

324 State F

F(f) =2+3=5

567

481

325 State J

F(j) =3+2=5 567481

32x State K

F(k) =3+4=7 56741

382y State Close

567

481

32y Close567

48

3216 State L

F(l) =4+1=5

567

481

32y State Close

567

48

3217 State M

F(m) =5+0=5 56487

321x State N

F(n) =5+1=7

The 8-puzzle searched by a production system with

l d t ti d d th b d 5

C3T


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Tr

chi

8-puzzle

loop detection and depth bound 5Tm

kim

khnggia

ntrngthi

Hot ng theo gii thut A*


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Slide 64

{}

{A4}{A4,C4}

{A4,C4,E5}

{A4,C4,E5,F5}{A4,C4,E5,F5,J5}

{A4,C4,E5,F5,J5,L5}

{A4}

{C4,B6,D6}{E5,F5,G6,B6,D6}

{F5,H6,G6,B6,D6,I7}

{J5,H6,G6,B6,D6,K7,I7}{L5,H6,G6,B6,D6,K7,I7}

{M5,H6,G6,B6,D6,K7,I7,N7}

A4C4

E5

F5J5

l5

m5m5

0

12

3

45

6

7

CloseOpennLn

nh gi gii thut Heuristic


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Slide 65

Admissibility Tnh chp nhn Mt gii thut Best first search vi hm nh gi

F(n) = G(n) + H(n) vi N : Trng thi bt k

G(n) : Ph tn i tnt bt u n nt n

H(n) : Ph tn c lng heuristic i tnt nn goal

c gi l gii thut A Mt gii thut tm kim c xem l admissible nu i

vi mt thbt kn lun dng path nghim tt nht

(nu c). Gii thut A*: L gii thut A vi hm heuristic H(n)lun

lun gi trthc i tn n goal.

Gii thut A* l admissible

nh gi gii thut Heuristic


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Slide 66

Monotonicity n iu Mt hm heuristic H(n)c gi lmonotone (n iu) nu:

ni, nj : nj l nt con chu ca ni ta cH(ni)-H(nj) ph tn tht i t ni n nj

nh gi heuristic ca ch l 0 : H(goal) = 0.

Gii thut A c hm H(n) monotone l gii thut A* vAdmissible

Informedness

Xt 2 hm heuristic H1(n) v H2(n) nu ta c H1(n)H2(n) vi mi trng thi n th H2(n)c cho linformed hn H1(n).

Heuristic trong tr chi i khng


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Slide 67

g g Gii thut minimax:

Hai u thtrong tr chi c gi l MIN v MAX.

Mi nt l c gi tr: 1 nu l MAX thng,

0 nu l MIN thng.

Minimax struyn cc gi trny ln cao dn trn th, qua cc

nt cha mktip theo cc lut sau: Nu trng thi cha ml MAX, gn cho n gi trln nht c trong cc trng

thi con.

Nu trng thi b, ml MIN, gn cho n gi trnhnht c trong cc trng

thi con.

C 4 Tm kim Heuristic

Hy p dng GT Minimax vo Tr Chi NIM


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Slide 68C 4 Tm kim Heuristic

Minimax vi su lp cnh


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Slide 69

Minimax i vi mt KGTT ginh.

Cc nt l c gn cc gi trheuristic

Cn gi trti cc nt trong l cc gi trnhn c da trngii thut Minimax


Heuristic trong tr chi tic-tac-toe


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Slide 70

Hm Heuristic

: E(n) = M(n) O(n)Trong : M(n) l tng sng thng c thca tiO(n) l tng sng thng c thca i thE(n) l trsnh gi tng cng cho trng thi n


Minimax 2 lp trong tic-tac-toe


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Slide 71

Trch tNilsson (1971).C 4 Tm kim Heuristic

Gii thut ct ta -


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Slide 72

Tm kim theo kiu depth-first.

Nt MAX c 1 gi tr (lun tng)

Nt MIN c 1 gi tr (lun gim) TK c thkt thc di bt k:

Nt MIN no c ca bt k nt cha MAX no.

Nt MAX no c ca bt k nt cha MIN no.

Gii thut ct ta - th hin mi quan h gia ccnt lp n v n+2, m ti ton b cy c gc ti

lp n+1 c th ct b.


Ct ta


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Slide 73

S

A Z

MAX

MIN

= z

=

z

- cut

=


Ct ta


74/113

Slide 74

S

A Z

MIN

MAX

= z

=

z

- cut

=


GT Ct Ta - p dng cho KGTT gi nh


75/113

Slide 75C 4 Tm kim Heuristic

Cc nt khng c gi trlcc nt khng c duytqua


76/113

TS. Nguyn nh ThunKhoa Cng nghThng tini hc Nha TrangEmail: [email protected]

Chng 4: Biu din v suy lun tri thc


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Slide 77

4.1. Mu tri thc, lnh vc v biu din tri thc.

4.2. Cc loi tri thc: c chia thnh 5 loi1. Tri thc th tc: m t cch thc gii quyt mt vn. Loi

tri thc nya ra gii php thc hin mt cng vic no. Cc dng tri thc th tc tiu biu thng l cc lut,chin lc, lch trnh v th tc.

2. Tri thc khai bo: cho bit mt vn c thy nh th no.Loi tri thc ny bao gm cc pht biun gin, di dngcc khngnh logic ng hoc sai. Tri thc khai bo cng cth l mt danh sch cc khngnh nhm m t y hnv i tng hay mt khi nim no.

4.2. Cc loi tri thc (tip)


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Slide 78

3. Siu tri thc: m t tri thc v tri thc. Loi tri thc ny gip lachn tri thc thch hp nht trong s cc tri thc khi gii quyt mt vn.Cc chuyn gia s dng tri thc ny iu chnh hiu qu gii quyt vn bng cch hng cc lp lun v min tri thcckh nng hn c.

4. Tri thc heuristic: m t cc "mo" dn dt tin trnh lp lun.Tri thc heuristic l tri thc khng bmm hon ton 100% chnh xc vkt qu gii quyt vn. Cc chuyn gia thng dng cc tri thc khoahc nh s kin, lut, sau chuyn chng thnh cc tri thc heuristic thun tin hn trong vic gii quyt mt s bi ton.

5. Tri thc c cu trc: m t tri thc theo cu trc. Loi tri thc nym t m hnh tng quan h thng theo quanim ca chuyn gia, baogm khi nim, khi nim con, v cci tng; din t chc nn g v milin h gia cc tri thc da theo cu trc xcnh.

V d: Hy phn loi cc tri thc sau


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Slide 79

1. Nha Trang l thnh ph p.

2. Bn Lan thchc sch.

3. Thut ton tm kim BFS, DFS4. Thut gii Greedy

5. Mt scch chiu tng trong vic chi ctng.

6. Hthng cc khi nim trong hnh hc.

7. Cch tp vit chp.

8. Tm tt quyn sch vTr tunhn to.

9. Chn loi cphiu mua cphiu.

4.3. CC KTHUT BIU DIN TRI THC


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Slide 80

4.3.1 B bai tng-Thuc tnh-Gi tr

4.3.2 Cc lut dn

4.3.3 Mng ng ngha4.3.4 Frames

4.3.5 Logic

4.3.1 B bai tng-Thuc tnh-Gi tr


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Slide 81

Mt s kin c th c dng xc nhn gi tr ca mt thuc tnh xcnh ca mt vii tng. V d, mnh "qu bng mu" xc nhn"" l gi tr thuc tnh "mu" cai tng "qu bng". Kiu s kin ny

c gi l b bai tng-Thuc tnh-Gi tr (O-A-V Object-Attribute-Value).

Hnh 2.1. Biu din tri thc theo bba O-A-V

4.3.1 B bai tng-Thuc tnh-Gi tr (tip)


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Slide 82

Trong cc s kin O-A-V, mti tng c th c nhiu thuc tnhvi cc kiu gi tr khc nhau. Hn na mt thuc tnh cng c thc mt hay nhiu gi tr. Chng c gi l cc s kin n tr

(single-valued) hoc a tr (multi-valued). iu ny cho php cch tri thc linhng trong vic biu din cc tri thc cn thit.

Cc s kin khng phi lc no cng bom lng hay sai vi chc chn hon ton. V th, khi xem xt cc s kin, ngi ta

cn s dng thm mt khi nim l tin cy. Phng php truynthng qun l thng tin khng chc chn l s dng nhn t chcchn CF (certainly factor). Khi nim ny bt u t h thngMYCIN (khong nm 1975), dng tr li cho cc thng tin suylun. Khi, trong s kin O-A-V s c thm mt gi tr xcnh tin cy ca n l CF.

4.3.2 Cc lut dn


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Slide 83

Lut l cu trc tri thc dng lin kt thng tin bit vi cc thng tin khc gipa ra cc suy lun,

kt lun t nhng thng tin bit. Trong h thng da trn cc lut, ngi ta thu thp cc

tri thc lnh vc trong mt tp v lu chng trong c

s tri thc ca h thng. H thng dng cc lut nycng vi cc thng tin trong b nh gii bi ton.Vic x l cc lut trong h thng da trn cc lutc qun l bng mt module gi l b suy din.

4.3.2 Cc lut dn(tip)


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Slide 84

Cc dng lut cbn: 7 dng1. Quan h:

IF Bnhin hngTHEN Xe s khng khingc

2. Li khuyn:

IF Xe khng khingcTHENi b

3. Hng dnIF Xe khng khingc AND H thng nhin liu tt

THEN Kimtrah thngin

4.3.2 Cc lut dn(tip)


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Slide 85

4. Chin lcIF Xe khng khingcTHEN u tin hy kim tra h thng nhin liu, sau kim tra hthngin

5. Din giiIFXen AND ting ginTHENng chotng bnh thng

6. ChnonIF St cao AND hay ho AND HngTHEN Vim hng

7. Thit kIFLnAND Da sng

THEN Nn chn Xe Spacy AND Chn mu sng

4.3.3 Mng ng ngha


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Slide 86

Mng ngngha l mt phng php biu din tri thc dng th trong nt biu din i tng v cung biu dinquan h gia cci tng.

Hnh 2.3. "Sl Chim" thhin trn mng ngngha

4.3.3 Mng ng ngha(tip)


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Slide 87

Hnh 4.4. Pht trin mng ngngha

V d: Gii bi ton tam gic tng qut


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Slide 88

C 22 yu tca tam gic. Nhvy c C322 -1 cch xy dng hayxc nh mt tam gic.Theo thng k, c khong 200 cng thc lin quan n cnh v gc 1tam gic.gii bi ton ny bng cng cmng ngngha, sdng khong200 nh cha cng thc v khong 22 nh cha cc yu tcatam gic. Mng ngngha cho bi ton ny c cu trc nhsau :

nh ca thbao gm hai loi :

nh cha cng thc (k hiu bng hnh chnht)nh cha yu tca tam gic (k hiu bng hnh trn)

Cung : chni tnh hnh trn n nh hnh chnht cho bit yuttam gic xut hin trong cng thc no

* L

u : trong mt cng th

c lin h

gi

a n yu t

ca tam gic, ta gi

nh rng nu bit gi trca n-1 yu t th s tnh c gi trca

yu tcn li. Chng hn nhtrong cng thc tng 3 gc ca tam gicbng 1800 th khi bit c hai gc, ta stnh c gc cn li.

V d: Gii bt tam gic tng qut (tt)


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Slide 89

B1 : Kch hot nhng nh hnh trn cho banu (nhng yu t c gi tr)B2 : Lp li bc sau cho n khi kch hot ctt c nhng nh ng vi nhng yu t cn tnhhoc khng th kch hot c bt k nh nona.

Nu mt nh hnh chnht c cung ni vi nnhhnh trn m n-1nh hnh trn c kch hotth kch hot nh hnh trn cn li (v tnh gi tr

nh cn li ny thng qua cng thc nh hnhchnht).

V d: Gii bt tam gic tng qut (tt)


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Slide 90

V d: "Cho hai gc , v chiu di cnh a ca tamgic. Tnh chiu di ng cao hC".

p

p=(a+b+c)/2

4.3.4 Frame


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Slide 91

Hnh 2.6. Cu trc frame

Hnh 2.7. Nhiu mc ca frame m tquan hphc tp hn

4.3.5 Logic


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Slide 92

1. Logic mnhIF Xe khng khingc (A)

AND Khong cch t nhn ch lm l xa (B)

THEN S tr gilm (C)

Lut trn c th biu din li nh sau:AB C

2. Logic v t Logic v t, cng ging nh logic mnh, dng cc k hiu

th hin tri thc. Nhng k hiun ygm hng s, v t, binv hm.

4.4 SUY DIN DLIU


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Slide 93

1. Modus ponens1. E1

2. E1 E2

3. E2

Nu c tin khc, c dng E2 E3 th E3c a vo danh sch.

2. Modus tollens1. E2

2. E1 E2

3. E1

4.5 Chng minh mnh


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Slide 94

Mt trong nhng vn kh quan trng ca logic mnh l chng minhtnh ng n ca php suy din (a b). y cng chnh l bi ton chng

minh thng gp trong ton hc. Vi hai php suy lun cbn ca logic mnh (Modus Ponens, Modus

Tollens) cng vi cc php bin i hnh thc, ta cng c thchng minhc php suy din. Tuy nhin, thao tc bin i hnh thc l rt kh ci tc trn my tnh. Thm ch iu ny cn kh khn vi ccon ngi!

Vi cng cmy tnh, c thcho rng ta sddng chng minh c mibi ton bng mt phng php bit l lp bng chn tr. Tuy vlthuyt, phng php lp bng chn trlun cho c kt qucui cngnhng phc tp ca phng php ny l qu ln, O(2n) vi n l sbin

mnh . Sau y chng ta snghin cu hai phng php chng minhmnh vi phc tp chc O(n).

4.5 Chng minh mnh


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Slide 95

Mt trong nhng vn kh quan trng ca logic mnh l chng minhtnh ng n ca php suy din (a b). y cng chnh l bi ton chng

minh thng gp trong ton hc. Vi hai php suy lun cbn ca logic mnh (Modus Ponens, Modus

Tollens) cng vi cc php bin i hnh thc, ta cng c thchng minhc php suy din. Tuy nhin, thao tc bin i hnh thc l rt kh ci tc trn my tnh. Thm ch iu ny cn kh khn vi ccon ngi!

Vi cng cmy tnh, c thcho rng ta sddng chng minh c mibi ton bng mt phng php bit l lp bng chn tr. Tuy vlthuyt, phng php lp bng chn trlun cho c kt qucui cngnhng phc tp ca phng php ny l qu ln, O(2n) vi n l sbin

mnh . Sau y chng ta snghin cu hai phng php chng minhmnh vi phc tp chc O(n).

4.5.1 Thut gii Vng Ho

B1 Ph bi l i i hi k l h d h


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Slide 96

B1 : Pht biu li githit v kt lun ca vn theo dng chun sau :GT1, GT2, ..., GTn KL1, KL2, ..., KLm

Trong cc GTi v KLi l cc mnh c xy dng tcc bin mnh v 3php ni cbn : , ,

B2 : Chuyn vcc GTi v KLi c dng phnh.V d:

p q, (r s), g, p r s, p p q, p r , p (r s), g, s

B3 : Nu GTi c php th thay thphp bng du ","Nu KLi c php th thay thphp bng du ","V d:

p q , r ( p s) q, s p, q, r, p s q, s

4.5.1 Thut gii Vng Ho

B4 N GT h th t h th h h i d


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Slide 97

B4 : Nu GTi c php th tch thnh hai dng con.Nu KLi c php th tch thnh hai dng con.V d:

p, p q q

p, p qv p, q q

B5 : Mt dng c chng minh nu tn ti chung mt mnh chai pha.V d:

p, q qc chng minhp, p q p p, qB6 :a) Nu mt dng khng cn php ni v php ni chai vv 2 v

khng c chung mt bin mnh th dng khng c chng minh.

b) Mt vn c chng minh nu tt cdng dn xut tdng chun ban uu c chng minh.V d: i) p ( p q) q

ii) (p q) ( p r) q r

4.5.2 Thut gii Robinson


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Slide 98

Thut gii ny hot ng da trn phng php chng minhphn chng v php hp gii Robinson.

Phng php chng minh phn chng: Chng minh php suy lun (a b) l ng (vi a l githit, b l kt

lun).

Phn chng : gisb sai suy ra b l ng.

Php hp gii Robinson:i) p ( p q) q

ii) (p q) ( p r) q r

Bi ton c chng minh nu a ng v b ng sinh ra mt muthun.

4.5.2 Thut gii Robinson (tip)

1 h bi l i i hi k l d i d h


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Slide 99

B1 : Pht biu li githit v kt lun ca vn di dng chunnhsau :

GT1, GT2, ..., GTn KL1, KL2, ..., KLm

Trong : GTi v KLjc xy dng tcc bin mnh v ccphp ton : , , B2 : Nu GTi c php th thay bng du ","

Nu KLi c php th thay bng du ","

B3 : Bin i dng chun B1 vthnh danh sch mnh nhsau :{ GT1, GT2, ..., GTn , KL1, KL2, ..., KLm }

B4 : Nu trong danh sch mnh bc 2 c 2 mnh i ngunhau th bi ton c chng minh. Ngc li th chuyn sangB5. (a v a gi l hai mnh i ngu nhau)

4.5.2 Thut gii Robinson (tip)


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Slide 100

B6 : p dng php hp gii

i) p ( p q) q

ii) (p q) ( p r) q rB7 : Nu khng xy dng c thm mt mnh mi no v

trong danh sch mnh khng c 2 mnh no i ngunhau th vn khng c chng minh.

V d: Chng minh rng( p q) ( q r) ( r s) ( u s) p u

Chng 5 My hc

5 1 M U


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Slide 101

5.1 MU Cc chng trc tho lun v biu din v suy lun tri

thc. Trong trng hp ny gi nh c sn tri thc v c t h

biu din tng minh tri thc. Tuy vy trong nhiu tinh hung, s khng c sn tri thc nh:

K s tri thc cn thu nhn tri thc t chuyn gia lnh vc. Cn bit cc lut m t lnh vc c th.

Bi ton khngc biu din tng minh theo lut, s kin hay ccquan h.

C hai tip cn c h o h thng hc: Hc t k hiu: bao gm vic hnh thc ha, sa cha cc lut tng

minh, s kin v cc quan h.

Hc t d liu s: c p dng cho nhng h thngc m hnh didng s lin quann cc k thut nhm tiu cc tham s. Hc theodng s bao gm mng Neural nhn to, thut gii di truyn, bi ton tiu truyn thng. Cc k thut hc theo s khng to ra CSTT tngminh.

5.2 CC HNH THC HC

1 Hc vt: H tip nhn cc khng nh ca cc quyt nh


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Slide 102

1. Hc vt: H tip nhn cc khngnh ca cc quytnhng. Khi h to r a mt quytnh khngng, h s a racc lut hay quan h ng m h s dng. Hnh thc hc

vt nhm cho php chuyn gia cung cp tri thc theo kiutng tc.

2. Hc bng cch ch dn: Thay va r a mt lut c th cnp dng vo tnh hung cho trc, h thng s c cungcp bng cc ch dn tng qut. V d: "gas hu nh b thotra t van thay v thot ra t ng dn". H thng phi t mnh ra cch bini t tru tngn cc lut kh dng.

3. Hc bng qui np: H thngc cung cp mt tp cc v d v kt lunc rt ra t tng v d. H lin tc lc cclut v quan h nhm x l tn g v d mi.

5.2 CC HNH THC HC (Tip)

4. Hc bng tng t: H thngc cung cppngng cho cc tc


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Slide103

g g g g p p g gv tng t nhng khng ging nhau. H thng cn lm thchngpng trc nhm to ra mt lut mi c kh nng p dng cho tnhhung mi.

5. Hc da trn gii thch: H thng phn tch tp cc li gii v d (v ktqu) nhmnnh kh nngng hoc sai v to ra cc gii thch dng hng dn cch gii bi ton trong tng lai.

6. Hc da trn tnh hung: Bt k tnh hung noc h thng lp lunuc lu tr cng vi kt qu cho dng hay sai. Khi gp tnh

hng mi, h thng s lm thch nghi hnh vi lu tr vi tnh hungmi.

7. Khm ph hay hc khng gim st: Thay v c mc tiu tng minh, hkhm ph lin tc tm kim cc mu v quan h trong d liu nhp. Ccv d v hc khng gim st bao gm gom cm d liu, hc nhndng ccc tnh cbn nh cnh t ccimnh.

V d v CC HNH THC HC


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Slide 104

V d:

- H MYCIN- Mng Neural nhn to

- Thut ton hc Quinland

- Bi ton nhn dng- My chi ccar, ctng

5.3 THUT GII Quinlan

L thut ton hc theo quy np dng lut a mc tiu


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Slide 105

- L thut ton hc theo quy np dng lut, a mc tiu.

- Do Quinlan a r a nm 1979.

- tng: Chn thuc tnh quan trng nht to cyquytnh.

- Thuc tnh quan trng nht l thuc tnh phn loi

Bng quan st thnh cc bng con sao cho t mi bngcon ny d phn tch tm quy lut chung.

5.3.1 THUT GII A. Quinlan

ConclusionFamilyNationalitySizeSTT


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Slide 106

ASingleGermanLarge3

BMarriedGermanSmall8

BMarriedItalianLarge7

BSingleItalianLarge6

BMarriedGermanLarge5

BSingleItalianSmall4

ASingleFrenchLarge2

ASingleGermanSmall1

yy

Vi mi thuc tnh ca bng quan st:

Xt vector V: c s chiu bng s phn loi


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Slide 107

V(Size=Small) = (ASmall, BSmall)

ASmall=S quan st A c Size l Small / Tng s quan st c Size=Small

BSmall= S quan st B c Size l Small / Tng s quan st c Size=SmallV(Size=Small) = (1/3 , 2/3)

V(Size=Large) = (2/5 , 3/5)

Vi thuc tnh NationalityV(Nat = German)= (2/4 , 2/4)

V(Nat = French) = (1 , 0)V(Nat = Italian) = (0 , 1)

Thuc tnh Family:V(Family=Single) = (3/5 ,2/5)

V(Family = Married)

= (0, 1)

Vi mi thuc tnh ca bng quan st:

Ch cn xt German ConclusionFamilySizeSTT


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Slide 108

Thuc tnh Size:V(Size=Small) = (1/2 , 1/2)V(Size=Large) = (1/2 , 1/2)

Thuc tnh Family:V(Family=Single) = (1, 0)V(Family=Married) = (0,1)

ConclusionFamilySizeSTT

BMarriedSmall4

BMarriedLarge3

ASingleLarge2

ASingleSmall1

Nationality

Italian French German

Single Married

Vi mi thuc tnh ca bng quan st(tip)


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Slide 109

Nationality

Italian French German

Single Married

Rule 1: If (Nationality IS Italian) then (Conclusion IS B)

Rule 2: If (Nationality IS French) then (Conclusion IS A)

Rule 3: If (Nationality IS German) AND (Family IS Single)

then (Conclusion IS A)

Rule 4: If (Nationality IS German) AND (Family IS Married)then (Conclusion IS B)

5.3.2 Thut gii Hc theo btnh

ProfitTypeCompetitionAgeStt


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Slide 110

DownHardwareYesOld10

DownHardwareYesMidle9

UpSoftwareYesNew8

UpSoftwareNoMidle7

UpSoftwareNoNew6

UpHardwareNoNew5

DownHardwareNoOld4

UpHardwareNoMidle3

DownSoftwareYesMidle2

DownSoftwareNoOld1

yppg

Hc theo btnh(tip)

b h X k


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Slide 111

btnh ca X:

Tnh Entropy cho mi thuc tnh v chn thuc tnh c Entropynh nht.

=

=k

1i2log-)( ii ppXE

=+=

==

==

=

=

=

=

8752.0811.0*4.0918.0*6.0)/(

811.043log

43-

41log

41-)/(

918.06

2log

6

2-

6

4log

6

4-)/(

),(log),(-)/(

22

22

k

1i

2

nCompetitioCE

CE

CE

acpacpACE

YesnCompetitio

NonCompetitio

iiii

Hc theo btnh(tip)

Tng t:


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Slide 112

E(C/Age) = 0.4

E(C/Type) = 1

Age cho nhiu thng tinnht

ProfitTypeCompetitionSTT

DownHardwareYes4

UpSoftwareNo3

UpHardwareNo2

DownSoftwareYes1

Age

Old Milde New

Down Competition Up

No Yes

Up Down

Hc theo btnh(tip)

Age


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Slide 113

Age

Old Milde New

Down Competition Up

No Yes

Up Down

Rule 1: If (Age IS Old) then (Profit IS Down)

Rule 2: If (Age IS New) then (Profit IS Up)

Rule 3: If (Age IS Midle) And (Competition IS No)

then (Profit IS Up)Rule 4: If (Age IS Midle) And (Competition IS Yes)

then (Profit IS Down)

Bai Giang Tri Tuen Han Tao

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