- Page 2 and 3:
Artificial Intelligence and Soft Co
- Page 4 and 5:
Library of Congress Cataloging-in-P
- Page 6 and 7:
understanding of the subject. The r
- Page 8 and 9:
4. Structural organization of the b
- Page 10 and 11:
ACKNOWLEDGMENT The author gratefull
- Page 12 and 13:
To my parents, Mr. Sailen Konar and
- Page 14 and 15:
Chapter 2: The Psychological Perspe
- Page 16 and 17:
5.4.2 Syntactic Methods for Theorem
- Page 18 and 19:
9.4.4 Realization of Fuzzy Inferenc
- Page 20 and 21:
Chapter 14: Machine Learning Using
- Page 22 and 23:
17.4.2.7 Fusing Multi-sensory Data
- Page 24 and 25:
22.4 Parallelism at Knowledge Repre
- Page 26 and 27:
1 Introduction to Artificial Intell
- Page 28 and 29:
instances of decision making [27],
- Page 30 and 31:
End. Begin state := new-states - ex
- Page 32 and 33:
(a) Generate and Test Approach: Thi
- Page 34 and 35:
this problem is useful for the foll
- Page 36 and 37:
disciplines. As a young discipline
- Page 38 and 39:
The bird and its attributes here ha
- Page 40 and 41:
[19] , Kosko [15] and Pedrycz [30]
- Page 42 and 43:
Reasoning in the presence of imprec
- Page 44 and 45:
where AI finds extensive applicatio
- Page 46 and 47:
M1 M2 M3 J2 J 1 8 5 J 2 J 1 2 8 J 2
- Page 48 and 49:
1.5.3 The Modern Period The modern
- Page 50 and 51:
A B AND C D E AND Y Fig. 1.12: A Pe
- Page 52 and 53:
improve reliability by realizing fr
- Page 54 and 55:
in AI problems, it supports massive
- Page 56 and 57:
[7] Dean, T., Allen, J. and Aloimon
- Page 58 and 59:
[34] Rajendra, C. and Chaudhury, D.
- Page 60 and 61:
2 The Psychological Perspective of
- Page 62 and 63:
The scope of realization of the pro
- Page 64 and 65:
2.2.3 Feature-based Approach for Pa
- Page 66 and 67:
From Sensory Registers Echoic Reg.
- Page 68 and 69:
cognitive memory and its utilizatio
- Page 70 and 71:
Tulving’s model bridges a potenti
- Page 72 and 73:
A " (a) (b) Fig. 2.6: The character
- Page 74 and 75:
his friend s facial image in memory
- Page 76 and 77:
sum need not be 180 degrees. Thus a
- Page 78 and 79:
same evening. Then he started medit
- Page 80 and 81:
construction of knowledge and its o
- Page 82 and 83:
saved in a magnetic media, which he
- Page 84 and 85:
set-point + error Controller Plant
- Page 86 and 87:
Cognitive science has emerged as a
- Page 88 and 89:
Further show that the Euclidean lea
- Page 90 and 91:
[4] Biswas, B., Mukherjee, A. K. an
- Page 92 and 93:
[28] Peterson, M. A., Kihlstrom, J.
- Page 94 and 95:
Production Systems, Logical Calculu
- Page 96 and 97:
against the data items of the WM. I
- Page 98 and 99:
Suppose the WM contains the data Bi
- Page 100 and 101:
To demonstrate the working principl
- Page 102 and 103:
i) Prefer rules for firing, where u
- Page 104 and 105:
In fig. 3.3, the antecedent clauses
- Page 106 and 107:
possible offspring states are gener
- Page 108 and 109:
Backward Reasoning: The backward re
- Page 110 and 111:
p q r s denotes joint premises t w
- Page 112 and 113:
3.11.1 Isolation of Knowledge and C
- Page 114 and 115:
state, as the predecessor states of
- Page 116 and 117:
[3] Buchanan, B. G. and Shortliffe,
- Page 118 and 119:
issues: first ‘what to search’
- Page 120 and 121:
We will now present two typical alg
- Page 122 and 123:
Further, the first state within the
- Page 124 and 125:
n1 n2 n3 n8 (a) (b) (c) (d) (e) (f
- Page 126 and 127:
The iterative deepening search thus
- Page 128 and 129:
legal next states will also lie on
- Page 130 and 131:
End. Then push those children into
- Page 132 and 133:
Procedure Best-First-Search Begin 1
- Page 134 and 135:
End. have multiple parents; Under t
- Page 136 and 137:
node. Under this circumstance, the
- Page 138 and 139:
6. Pn- Γ the set of paths from nod
- Page 140 and 141:
Therefore, f * (n’) = g* (n’) +
- Page 142 and 143:
0 +10 B 1+3 C +4 D +5 E F G 2+2 3+1
- Page 144 and 145:
The above algorithm considers two d
- Page 146 and 147:
Step 1: X (7) Step 2: Step 3: U X (
- Page 148 and 149:
(iv) If all of the nodes connected
- Page 150 and 151:
If the values are propagated up to
- Page 152 and 153:
ii) the alpha value of any MAX node
- Page 154 and 155:
αmin =2 βmax =1 C αmin =1 e(n) =
- Page 156 and 157:
eductions. Constraint satisfaction
- Page 158 and 159:
5 The Logic of Propositions and Pre
- Page 160 and 161:
5.2 Formal Definitions The followin
- Page 162 and 163:
Definition 5.7: A propositional for
- Page 164 and 165:
5.4.1 Semantic Method for Theorem P
- Page 166 and 167:
8. p \/ (q Λ ¬ q) ⇔ p 9. p Λ (
- Page 168 and 169:
) AND, OR Removal: If the L.H.S. co
- Page 170 and 171:
Since all the terminals of the tree
- Page 172 and 173:
p v q ¬ q v r p v r ¬ p v s r v s
- Page 174 and 175:
Let AS = {A1, A2,, …..,An} be the
- Page 176 and 177:
4. If P is a WFF and X is not a qua
- Page 178 and 179:
original expression: ( ¬ P11 ∨
- Page 180 and 181:
S: = S ∪ {variable or constant /
- Page 182 and 183:
¬ Loves (john, mary) ¬Child (X)
- Page 184 and 185:
which implies that if q is true the
- Page 186 and 187:
ii) C is a ground instance of C [9]
- Page 188 and 189:
) p→ (q→ r) ⇔ (p Λ q) → r
- Page 190 and 191:
[6] Leinweber, D., “Knowledge-bas
- Page 192 and 193:
of a ‘classroom scene’ interpre
- Page 194 and 195:
Sits-on (Y, bench, classhour), Pers
- Page 196 and 197:
Sits-on (mita, bench, classhour). 3
- Page 198 and 199:
Teacher (X) Sub-goal fails Person (
- Page 200 and 201:
Definition 6.5: A definite goal is
- Page 202 and 203:
Example 6.5, presented below, illus
- Page 204 and 205:
6.6.1 Risk of Using CUT It is to be
- Page 206 and 207:
In the SLD tree for the Taxpayer pr
- Page 208 and 209:
6.9 Fixed Points in Non-Horn Clause
- Page 210 and 211:
6.11 Conclusions The main advantage
- Page 212 and 213:
preferred X50 >= 2, default X100
- Page 214 and 215:
7.1 Introduction Predicate Logic is
- Page 216 and 217:
proposed this logic and called it n
- Page 218 and 219:
We now present a few modal properti
- Page 220 and 221:
Expert Reasoning System New data Dy
- Page 222 and 223:
PR5: ¬Visits (Wife, market, Monday
- Page 224 and 225:
IN (n1) + IN (n2) + (n11) retracts
- Page 226 and 227:
may hold (and not must hold). An ex
- Page 228 and 229:
The resulting stable consequences t
- Page 230 and 231:
In this example, we call ψ = Bird
- Page 232 and 233:
important factor in non-monotonic r
- Page 234 and 235:
a) Boy(X) ∧ ¬ L ¬ Likes (X, cho
- Page 236 and 237:
8 Structured Approach to Knowledge
- Page 238 and 239:
predicate boy (jim). This can be re
- Page 240 and 241:
Consequently, we represent the abov
- Page 242 and 243:
Is-a Is-a Is-a Protozoa Bacteria Is
- Page 244 and 245:
Example 8.2: This example illustrat
- Page 246 and 247:
A common question that may be raise
- Page 248 and 249:
∴ q is true. 2) Defeasible deriva
- Page 250 and 251:
Examination Hall Seat Arrangement I
- Page 252 and 253:
Petri nets [5]. It, however, does n
- Page 254 and 255:
Since p1 and p 2 contain tokens and
- Page 256 and 257:
For building dependency structures,
- Page 258 and 259:
The most significant drawback of CD
- Page 260 and 261:
Once a script structure for a given
- Page 262 and 263:
[6] Nilsson, N. J., Principles of A
- Page 264 and 265:
techniques, we first introduce the
- Page 266 and 267:
of precision of data and certainty
- Page 268 and 269:
where D and S stand for disease and
- Page 270 and 271:
To illustrate the reasoning process
- Page 272 and 273:
2. It might happen that none of the
- Page 274 and 275:
One interesting property of Bayesia
- Page 276 and 277:
We now compute the messages that no
- Page 278 and 279:
The main steps [8], [12] of the bel
- Page 280 and 281:
Updating a node X thus involves upd
- Page 282 and 283:
j ↓ i→ round oval-shaped and MB
- Page 284 and 285:
Formally, the set of all possible o
- Page 286 and 287:
The orthogonal summation of belief
- Page 288 and 289:
The orthogonal summation operations
- Page 290 and 291:
integers, then we can definitely sa
- Page 292 and 293:
where a / b in fast-runner subset r
- Page 294 and 295:
where the “o” denotes a fuzzy A
- Page 296 and 297:
This may be formally written as µ
- Page 298 and 299:
cumulative maximum of the two succe
- Page 300 and 301:
“If you can manage to evaluate th
- Page 302 and 303:
[7] Shenoy, P. P. and Shafer, G.,
- Page 304 and 305:
10.1 Introduction Databases associa
- Page 306 and 307:
a finite set of places, D= {d1, d2
- Page 308 and 309:
µfast runner(x) 0.9 0.6 0.2 0.1 5
- Page 310 and 311:
present in the consequent part; Aug
- Page 312 and 313:
Procedure Cycle-detection (P, Q, m,
- Page 314 and 315:
Procedure Put-transition (Newlistk,
- Page 316 and 317:
The trace of the algorithm cycle- d
- Page 318 and 319:
p1 p2 pm n1 n2 nm Fig. 10.4: A tran
- Page 320 and 321:
where the elements qw / ∈ {0, 1}
- Page 322 and 323:
Q' m and Rm matrices. It may be not
- Page 324 and 325:
Definition 10.9: An arc is called p
- Page 326 and 327:
The procedure forward reasoning is
- Page 328 and 329:
In the above example, A, B, and C a
- Page 330 and 331:
n V (q k j ∧r j k ) to be close t
- Page 332 and 333:
Let us assume for the sake of simpl
- Page 334 and 335:
It is evident from the above distri
- Page 336 and 337:
= R b m o ((P ' f m) T o Nf c (t+1)
- Page 338 and 339:
Since such choice of Rfm and Rbm sa
- Page 340 and 341:
one step of belief revision in the
- Page 342 and 343:
10.9 Conclusions The chapter presen
- Page 344 and 345:
References [1] Bugarin, A. J. and B
- Page 346 and 347:
[24] Looney, C. G., "Fuzzy Petri ne
- Page 348 and 349:
11 Reasoning with Space and Time Th
- Page 350 and 351:
The other one is an extension by ne
- Page 352 and 353:
touches another, the common points
- Page 354 and 355:
Procedure Move-optimal (R , Startin
- Page 356 and 357:
at Jadavpur University verified thi
- Page 358 and 359:
µ above(θ) =sin 2 θ , when -∏
- Page 360 and 361:
c c a b q r a b (b) (a) d d p q r p
- Page 362 and 363:
11.5 Temporal Reasoning by Situatio
- Page 364 and 365:
Holds( now, rain-ceased) Axiom (3)
- Page 366 and 367:
Interpretation I Interpretation II
- Page 368 and 369:
Example 11.3: Prove that Ā(p) ≡
- Page 370 and 371:
11.6.3 Some Elementary Proofs in PT
- Page 372 and 373:
The second rule is generally called
- Page 374 and 375:
11.9 Conclusions The chapter demons
- Page 376 and 377:
12 Intelligent Planning This chapte
- Page 378 and 379:
sets opposite to the TV. So they oc
- Page 380 and 381:
where On (X, Y) means the object X
- Page 382 and 383:
this, let us consider the last prob
- Page 384 and 385:
(B), we generate the old state 3, w
- Page 386 and 387:
12.3 Least Commitment Planning The
- Page 388 and 389:
To start solving the problem, we fi
- Page 390 and 391:
We now have three partially ordered
- Page 392 and 393:
12.4 Hierarchical Task Network Plan
- Page 394 and 395:
d=0 d=1 d=2 d=3 Fig. 12.16: A hiera
- Page 396 and 397:
J1 J2 M1 M2 M3 or Job schedules J2
- Page 398 and 399:
job k from time 0. For example, the
- Page 400 and 401:
3. Design a hierarchical plan for t
- Page 402 and 403:
trainer, who supplies the input-out
- Page 404 and 405:
Definition 13.1: An object is an en
- Page 406 and 407:
The restriction of hypotheses can b
- Page 408 and 409:
Positive instances: 1. Object (larg
- Page 410 and 411:
To illustrate how LEX performs inte
- Page 412 and 413:
alive dead Z=A/W 2 1 0 0 1 2 Living
- Page 414 and 415:
where Pi denotes the proportion of
- Page 416 and 417:
3. Apply Mapping Function: Apply th
- Page 418 and 419:
determine the animals because it do
- Page 420 and 421:
simplest form of reinforcement lear
- Page 422 and 423:
Let us now assume that the game is
- Page 424 and 425:
How can we compute the utility valu
- Page 426 and 427:
13.5 Learning by Inductive Logic Pr
- Page 428 and 429:
know f ? These questions can be ans
- Page 430 and 431:
|H| (1-ε) m ≤ δ ⇒ m ≥ (1/ε
- Page 432 and 433:
[7] Michalski, R. S., “A theory a
- Page 434 and 435:
However, most biologists are of the
- Page 436 and 437:
The most common non-linear function
- Page 438 and 439:
(d) b1 a1 (e) c1 cj cq bi ah Fig.14
- Page 440 and 441:
transverse diameter, etc. as the in
- Page 442 and 443:
x 1 x 2 x n Penultimate layer ∑+F
- Page 444 and 445:
Drawbacks of back-propagation algor
- Page 446 and 447:
x1 x2 xn 14.6 Widrow-Hoff's Multi-l
- Page 448 and 449:
x 1 x 2 x 1 x 2 The training proces
- Page 450 and 451:
• If not, select another ADALINE,
- Page 452 and 453:
ni 1 = '1' output ( firing ). Each
- Page 454 and 455:
designed the weights (W) of a singl
- Page 456 and 457:
AND z1 s11 OR w11 w12 w 1n v 11 v12
- Page 458 and 459:
− ~ w X k, l ∼ = ⎡ ⎤ ⎢
- Page 460 and 461:
V x ∑ w O j = Sgn ( j+ ji i + A
- Page 462 and 463:
neural nets in the fields mentioned
- Page 464 and 465:
where the first summation is over a
- Page 466 and 467:
circuit,” IEEE Trans. on Circuits
- Page 468 and 469:
their joint usage in many problems,
- Page 470 and 471:
1 0 1 0 11 0 1 0 1 0 1 1 0 10 1 1 1
- Page 472 and 473:
15.2 Deterministic Explanation of H
- Page 474 and 475:
Thus, in a total of N selections wi
- Page 476 and 477:
and population size = 2, which mean
- Page 478 and 479:
The behavior of GA without mutation
- Page 480 and 481:
a = ∑ Lt (π 0 P n ) i i=1 n→
- Page 482 and 483:
Eθ GA Program Z1, Z2, Z3, Z4, X1,
- Page 484 and 485:
e [W1 W2 W3 W4 W5 W6] T and [ G1 G2
- Page 486 and 487:
function. So, GA in each evolution
- Page 488 and 489:
f(x) = Sin (x) + [x * x + y] 1/2 ca
- Page 490 and 491:
(EQ (DU(MT CS) (NOT CS)) (DU(MS NN)
- Page 492 and 493:
[4] Chakraborty, U. K. and Muehlenb
- Page 494 and 495:
16 Realizing Cognition Using Fuzzy
- Page 496 and 497:
Cognitive maps are generally used f
- Page 498 and 499:
Theorem 16.1: With initial values o
- Page 500 and 501:
Here W T is a transposed weight mat
- Page 502 and 503:
example, for moving one’s arm, th
- Page 504 and 505:
and the weight adaptation equation
- Page 506 and 507:
Xk+1 ∆Wk = α Ek οXk+1 T / ((Xk+
- Page 508 and 509:
d5 d9 d1 d6 d7 d8 d10 d11 d1=Front
- Page 510 and 511:
16.10 Conclusions and Future Direct
- Page 512 and 513:
[6] Kosko, B., “Fuzzy cognitive m
- Page 514 and 515:
however, have a narrow spectrum of
- Page 516 and 517:
contaminated with it. However, for
- Page 518 and 519:
1. Move the mask over the image, so
- Page 520 and 521:
∇ 2 (g * f ) = ( ∇ 2 g ) * f. W
- Page 522 and 523:
It may be noted that segmenting an
- Page 524 and 525:
machine has to narrate the game onl
- Page 526 and 527:
schematic diagram, briefly outlinin
- Page 528 and 529:
same process for all clusters, we g
- Page 530 and 531:
Level 1 Level 2 Level 0 Neuron Fig.
- Page 532 and 533:
I (u, v) YI Fig. 17.12: Camera mode
- Page 534 and 535:
epipolar planes meet the image plan
- Page 536 and 537:
(a, b, 1) Fig. 17.15: A 3-D represe
- Page 538 and 539:
case III: Planes not parallel to th
- Page 540 and 541:
yi = -fi (xi,* ai - 1*) + ( fi / a
- Page 542 and 543:
Sample Execution: This is a program
- Page 544 and 545:
U t11 t12 t13 t14 x V = t21 t22 t23
- Page 546 and 547:
obtained after linearizing measurem
- Page 548 and 549:
co-ordinate systems. The correspond
- Page 550 and 551:
18 Linguistic Perception Building p
- Page 552 and 553:
psychological aspects of understand
- Page 554 and 555:
S NP VP ART N VP the man VP V NP ki
- Page 556 and 557:
( S ( NP ( ( ART the ) (N man ) ) (
- Page 558 and 559:
Sentence: NP VP 1 6 NP: ART N 2 ART
- Page 560 and 561:
18.2.3.1 Learning For adaptation of
- Page 562 and 563:
context sensitive grammar is thus m
- Page 564 and 565:
Generally, we start the sentence S
- Page 566 and 567:
VP: V NP S0 S1 Tagged procedure V:=
- Page 568 and 569:
Laugh agent animal enjoyer entity p
- Page 570 and 571:
18.5 Discourse and Pragmatic Analys
- Page 572 and 573:
Thus the elements of communicative
- Page 574 and 575:
19 Problem Solving by Constraint Sa
- Page 576 and 577:
The recognition problem is the pres
- Page 578 and 579:
CSP deals with two classical proble
- Page 580 and 581:
= 10 10 = 10 = * Fig. 19.2 (b): Pro
- Page 582 and 583:
+ _ V1 R1 V 2 V3 I1 I2 I3 V R2 R3 S
- Page 584 and 585:
V = Now assuming the values of V, R
- Page 586 and 587:
On (A, Ta) ∧ On (B, Ta) ∧On (C,
- Page 588 and 589:
We now represent the neighboring re
- Page 590 and 591:
Step 1: (x
- Page 592 and 593:
such variable xj , xk, etc., attach
- Page 594 and 595:
of C3. If the constraints are liste
- Page 596 and 597:
The possible labels of the junction
- Page 598 and 599:
The main part of CSP is the formula
- Page 600 and 601:
20 Acquisition of Knowledge Acquisi
- Page 602 and 603:
invite the experts to attend a meet
- Page 604 and 605:
B E A= test ?, B = test results, C
- Page 606 and 607:
The database in fig. 20.2 is extrac
- Page 608 and 609:
20.5 Knowledge Refinement by Hebbia
- Page 610 and 611:
* * * n i ( t + 1) = n i ( t ) at t
- Page 612 and 613:
L(r,s) L(s,r) Y(r) Y(s) OS(r,s) n1
- Page 614 and 615:
The FPN of fig. 20.5 is formed with
- Page 616 and 617:
L(a,l) L(l,a) Y(a) Y(l) OS(a,l) n1
- Page 618 and 619:
[3] Cooke, N. and Macdonald, J.,
- Page 620 and 621:
21 Validation, Verification and Mai
- Page 622 and 623:
Here the problem characteristic is
- Page 624 and 625:
The chapter will cover various issu
- Page 626 and 627:
21.2.1 Qualitative Methods for Perf
- Page 628 and 629:
Paired t-test: Suppose xi ∈ X and
- Page 630 and 631:
Ancestor P = {p1,p2} Tr = {tr1,tr2,
- Page 632 and 633:
21.3.2 Inconsistencies in Knowledge
- Page 634 and 635:
q tr1 tr2 p1 p2 …………… pn
- Page 636 and 637:
A rule may also include a number of
- Page 638 and 639:
21.4 Maintenance of Knowledge Based
- Page 640 and 641:
control of data and knowledge is un
- Page 642 and 643:
8. Stefik, M., Introduction to Know
- Page 644 and 645:
are not suitable for AI application
- Page 646 and 647:
Because of non-determinism in the s
- Page 648 and 649:
Procedure Partial-Expansion Begin W
- Page 650 and 651:
Step: Fig.22.4: A tree expanded by
- Page 652 and 653:
Procedure Generous-Sharing Begin Fl
- Page 654 and 655:
To evaluate the performance of the
- Page 656 and 657:
Definition 22.2: If the antecedents
- Page 658 and 659:
a) AND -Parallelism Consider a logi
- Page 660 and 661:
However, given sufficient computing
- Page 662 and 663:
EPN = { P, Tr, D, f, m, A, a, I, o
- Page 664 and 665:
It is to be noted that if- then ope
- Page 666 and 667:
After the bindings of the variables
- Page 668 and 669:
F(x) ← A(X) , B(Y) (1) A(1) ← (
- Page 670 and 671:
the result produced by clause (2) m
- Page 672 and 673:
Else Flag:= true; End; Until no-of
- Page 674 and 675:
transition for the PTVVM. The PTVVM
- Page 676 and 677:
For the sake of convenience, the pi
- Page 678 and 679:
22.6 Conclusions The chapter starte
- Page 680 and 681:
Rule 3: S(X, Y) → Z (Y, X) Rule 4
- Page 682 and 683:
23 Case Study I: Building a System
- Page 684 and 685:
features in human faces and their r
- Page 686 and 687:
Definition 23.4: A linear edge segm
- Page 688 and 689:
lock containing edge. The productio
- Page 690 and 691:
Definition 23.13: The measure of di
- Page 692 and 693:
Partitioning Fe into Blocks Feature
- Page 694 and 695:
The fuzzy membership functions used
- Page 696 and 697:
∆ • Fig. 23.4: The delta and th
- Page 698 and 699:
Thus we have altogether 6 different
- Page 700 and 701:
cross-section of the vocal tract, w
- Page 702 and 703:
23.4.2 Training a Multi-layered Neu
- Page 704 and 705:
information with their normalized v
- Page 706 and 707:
SR (Y, X) OR (M (X) ∧ M (Y) ∧ L
- Page 708 and 709:
procedure, however, no attempts hav
- Page 710 and 711:
23.5.5 Belief Revision and Limitcyc
- Page 712 and 713:
that the inferences will be minimal
- Page 714 and 715:
the formation of the FPN. The FPN o
- Page 716 and 717:
is then initiated for detecting the
- Page 718 and 719:
initial fuzzy beliefs at all places
- Page 720 and 721:
at all feasible x, y. Then estimate
- Page 722 and 723:
[13] Malmberg, B., Manual of Phonet
- Page 724 and 725:
24.1 Mobile Robots Mobile robots ar
- Page 726 and 727:
about its world. There exists ample
- Page 728 and 729:
the process, else it attempts to fi
- Page 730 and 731:
Depending on the type of planning p
- Page 732 and 733: WEST NORTH SOUTH Fig. 24.5: Represe
- Page 734 and 735: NW child B NE child 'C' SW child 'D
- Page 736 and 737: As observed from the node status, t
- Page 738 and 739: In their evolutionary planner algor
- Page 740 and 741: tuning of the path, such as avoidin
- Page 742 and 743: S S= starting point, G= Goal point,
- Page 744 and 745: Once the training is over, the syst
- Page 746 and 747: N o Table 24.2: Training pattern sa
- Page 748 and 749: 24.9.1 Finite State Machine Finite
- Page 750 and 751: occurrence of a state, but also in
- Page 752 and 753: 24.10 An Application in a Soccer Pl
- Page 754 and 755: Exercises 1. Devise an algorithm fo
- Page 756 and 757: [15] Meng, H. and Picton, P. D.,
- Page 758 and 759: 24 + The Expectations from the Read
- Page 760 and 761: Appendix A How to Run the Sample Pr
- Page 762 and 763: Predicate: LSR Argument1: S Argumen
- Page 764 and 765: ANTECEDENT 1: Predicate: END conclu
- Page 766 and 767: Rules properly arranged MAIN MENU:
- Page 768 and 769: Level POSITION_FROM_LEFT NODE 1 1 H
- Page 770 and 771: calculating membership values enter
- Page 772 and 773: C > File currently in progress = ci
- Page 774 and 775: When the goal is within concave obs
- Page 776 and 777: path selected 160,160 and 240,240 T
- Page 778 and 779: (a) Robot entering the room (b) Aft
- Page 780 and 781: where tr and Outr denote the target
- Page 784 and 785: Proof of theorem10.2: For an oscill
- Page 786 and 787: Thus, we get analogously , and 1 0
- Page 788: = Q 'f m T o Rf m o ( Q ' f m o I )