Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

Recommendations

Info

The restriction of hypotheses can be implemented by adding a conjunct to the previous hypothesis. The preferential ordering among the hypotheses can be made by a heuristic evaluation function that excludes some objects from the target concept. The hypothesis that is not supported by the excluded class is preferred to the hypothesis that is supported by it . The Candidate Elimination Algorithm The candidate elimination algorithm is employed to reduce the concept (version) space from both general to specific and from specific to general form. It, thus, is a bi-directional search. Positive instances are used for generalization and negative instances are utilized to prevent the algorithm from over-generalization. The learned concept will, therefore, be general enough to include all positive instances and exclude all negative instances. We now present the procedure candidate-elimination [5], [9]. Procedure Candidate-Elimination Begin Initialize G to be the most general concept in the space; Initialize S to be the first positive training instance; For each new positive instance p do Begin Eliminate the members of G that do not match with p; for all s ∈ S, if s does not match with p, replace s with its most specific generalization that match with p; Eliminate from S any hypothesis that is more general than some other in S; Eliminate from S any hypothesis, which is no more specific than some hypothesis in G; End For ; For each negative instance n do Begin Eliminate all members of S that match with n; for each g ∈ G that matches with n, replace g by its most general specialization that does not match with n; Eliminate from G any hypothesis, which is more specific in some other hypothesis in G; Eliminate from G any hypothesis, which is more specific than some other hypothesis in S; End For If G=∅ and S=∅ Then report “no concept supports all positive and refutes all negative instances”;
If G=S and both contain one element, Then report “a single concept, that supports all positive and refutes all negative instances, has been found”. End. Example 13.1: This example illustrates the candidate elimination procedure with the following positive and negative training instances. G: {ob (x.y,z)} S: {φ } G: {ob (x,y,z)} S: {ob(large,blue,ball)} G:{ob (x,y,ball)} S:{ob (x,y,ball) } G:{ob (x, y, ball)} S:{ob (x, y, ball)} G:{ob (x,y,ball)} S: {ob (x,y,ball)} Ob (large, blue, ball) (positive) Fig. 13.1: Illustrating the candidate elimination algorithm. Ob (small, yellow, ball) (positive) Ob (small, blue, cone) (negative) Ob(small,green,cube) (negative)
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 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 782 and 783:
∂E/∂Wsp = ∑r (∂E/∂Outr) (
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 )
show all

Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?