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[34] Rajendra, C. and Chaudhury, D., “An efficient heuristic approach to the scheduling of jobs in a flowshop,” European Journal of Operational Research, vol. 61, pp. 318-325, 1991. [35] Rich, E. and Knight, K., Artificial Intelligence, McGraw-Hill, New York, 1996. [36] Russel, S. and Norvig, P., Artificial Intelligence: A Modern Approach, Prentice-Hall, Englewood Cliffs, NJ, 1995. [37] Schalkoff, J., Culberson, J., Treloar, N. and Knight, B., “A world championship caliber checkers program,” Artificial Intelligence, vol. 53, no. 2-3, pp. 273-289, 1992. [38] Shobam, Y., Artificial Intelligence Techniques in PROLOG, Morgan Kaufmann, San Mateo, CA, 1994. [39] Shortliffe, E. H., Computer-based Medical Consultations: MYCIN, Elsevier, New York, 1976. [40] Spivey, M., An Introduction to Logic Programming through PROLOG, Prentice-Hall, Englewood Cliffs, NJ, 1996. [41] Sympson, P., Artificial Neural Nets: Concepts, Paradigms and Applications, Pergamon Press, Oxford, 1988. [42] Szats, A., Temporal logic of programs: standard approach, In Time & Logic, Leonard, B. and Andrzej, S., Eds., UCL Press, London, pp. 1-50, 1995. [43] Takagi, T.and Sugeno, M., "Fuzzy identification of systems and its application to modeling and control,” IEEE Trans. on Systems, Man and Cybernetics, pp. 116-132, 1985. [44] Tanaka, K., “Stability and Stabilizability of Fuzzy-Neural-Linear Control Systems,” IEEE Trans. on Fuzzy Systems, vol. 3, no. 4, 1995. [45] Wah, B.W. and Li, G.J., “A survey on the design of multiprocessing systems for artificial intelligence,” IEEE Trans. On Systems, Man and Cybernetics, vol. 19, no.4, July/Aug. 1989. [46] Winograd, T., “Understanding natural language,” Cognitive Psychology, vol. 1, 1972.
[47] Winston, P. H., Artificial Intelligence, Addison-Wesley, 2 nd ed., Reading, MA, 1994. [48] Yager, R.R., “Some relationships between possibility, truth and certainty,” Fuzzy Sets and Systems, Elsevier, North Holland, vol. 11, pp. 151-156, 1983. [49] Zadeh. L. A., “ The role of fuzzy logic in the management of uncertainty in Expert Systems,” Fuzzy Sets and Systems, Elsevier, North Holland, vol. 11, pp. 199-227, 1983. [50] Zimmermann, H. J., Fuzzy Set Theory and its Applications, Kluwer Academic, Dordrecht, The Netherlands, 1991.
Page 2 and 3:
Artificial Intelligence and Soft Co
Page 4 and 5:
Library of Congress Cataloging-in-P
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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 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
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p q r s denotes joint premises t w
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3.11.1 Isolation of Knowledge and C
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state, as the predecessor states of
Page 116 and 117:
[3] Buchanan, B. G. and Shortliffe,
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issues: first ‘what to search’
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We will now present two typical alg
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Further, the first state within the
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n1 n2 n3 n8 (a) (b) (c) (d) (e) (f
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The iterative deepening search thus
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legal next states will also lie on
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End. Then push those children into
Page 132 and 133:
Procedure Best-First-Search Begin 1
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End. have multiple parents; Under t
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node. Under this circumstance, the
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6. Pn- Γ the set of paths from nod
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Therefore, f * (n’) = g* (n’) +
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0 +10 B 1+3 C +4 D +5 E F G 2+2 3+1
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The above algorithm considers two d
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Step 1: X (7) Step 2: Step 3: U X (
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(iv) If all of the nodes connected
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If the values are propagated up to
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ii) the alpha value of any MAX node
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αmin =2 βmax =1 C αmin =1 e(n) =
Page 156 and 157:
eductions. Constraint satisfaction
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5 The Logic of Propositions and Pre
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5.2 Formal Definitions The followin
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Definition 5.7: A propositional for
Page 164 and 165:
5.4.1 Semantic Method for Theorem P
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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
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p v q ¬ q v r p v r ¬ p v s r v s
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Let AS = {A1, A2,, …..,An} be the
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4. If P is a WFF and X is not a qua
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original expression: ( ¬ P11 ∨
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S: = S ∪ {variable or constant /
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¬ Loves (john, mary) ¬Child (X)
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which implies that if q is true the
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ii) C is a ground instance of C [9]
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) p→ (q→ r) ⇔ (p Λ q) → r
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[6] Leinweber, D., “Knowledge-bas
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of a ‘classroom scene’ interpre
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Sits-on (Y, bench, classhour), Pers
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Sits-on (mita, bench, classhour). 3
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Teacher (X) Sub-goal fails Person (
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Definition 6.5: A definite goal is
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Example 6.5, presented below, illus
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6.6.1 Risk of Using CUT It is to be
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In the SLD tree for the Taxpayer pr
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6.9 Fixed Points in Non-Horn Clause
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6.11 Conclusions The main advantage
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preferred X50 >= 2, default X100
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7.1 Introduction Predicate Logic is
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proposed this logic and called it n
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We now present a few modal properti
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Expert Reasoning System New data Dy
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PR5: ¬Visits (Wife, market, Monday
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IN (n1) + IN (n2) + (n11) retracts
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may hold (and not must hold). An ex
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The resulting stable consequences t
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In this example, we call ψ = Bird
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important factor in non-monotonic r
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a) Boy(X) ∧ ¬ L ¬ Likes (X, cho
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8 Structured Approach to Knowledge
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predicate boy (jim). This can be re
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Consequently, we represent the abov
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Is-a Is-a Is-a Protozoa Bacteria Is
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Example 8.2: This example illustrat
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A common question that may be raise
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∴ q is true. 2) Defeasible deriva
Page 250 and 251:
Examination Hall Seat Arrangement I
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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
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techniques, we first introduce the
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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
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2. It might happen that none of the
Page 274 and 275:
One interesting property of Bayesia
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We now compute the messages that no
Page 278 and 279:
The main steps [8], [12] of the bel
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Updating a node X thus involves upd
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j ↓ i→ round oval-shaped and MB
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Formally, the set of all possible o
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The orthogonal summation of belief
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The orthogonal summation operations
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integers, then we can definitely sa
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where a / b in fast-runner subset r
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where the “o” denotes a fuzzy A
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This may be formally written as µ
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cumulative maximum of the two succe
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“If you can manage to evaluate th
Page 302 and 303:
[7] Shenoy, P. P. and Shafer, G.,
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10.1 Introduction Databases associa
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a finite set of places, D= {d1, d2
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µfast runner(x) 0.9 0.6 0.2 0.1 5
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present in the consequent part; Aug
Page 312 and 313:
Procedure Cycle-detection (P, Q, m,
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Procedure Put-transition (Newlistk,
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The trace of the algorithm cycle- d
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p1 p2 pm n1 n2 nm Fig. 10.4: A tran
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where the elements qw / ∈ {0, 1}
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Q' m and Rm matrices. It may be not
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Definition 10.9: An arc is called p
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The procedure forward reasoning is
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In the above example, A, B, and C a
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n V (q k j ∧r j k ) to be close t
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Let us assume for the sake of simpl
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It is evident from the above distri
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= R b m o ((P ' f m) T o Nf c (t+1)
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Since such choice of Rfm and Rbm sa
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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
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[24] Looney, C. G., "Fuzzy Petri ne
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11 Reasoning with Space and Time Th
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The other one is an extension by ne
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touches another, the common points
Page 354 and 355:
Procedure Move-optimal (R , Startin
Page 356 and 357:
at Jadavpur University verified thi
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µ above(θ) =sin 2 θ , when -∏
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c c a b q r a b (b) (a) d d p q r p
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11.5 Temporal Reasoning by Situatio
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Holds( now, rain-ceased) Axiom (3)
Page 366 and 367:
Interpretation I Interpretation II
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Example 11.3: Prove that Ā(p) ≡
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11.6.3 Some Elementary Proofs in PT
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The second rule is generally called
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11.9 Conclusions The chapter demons
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12 Intelligent Planning This chapte
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sets opposite to the TV. So they oc
Page 380 and 381:
where On (X, Y) means the object X
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this, let us consider the last prob
Page 384 and 385:
(B), we generate the old state 3, w
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12.3 Least Commitment Planning The
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To start solving the problem, we fi
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We now have three partially ordered
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12.4 Hierarchical Task Network Plan
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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
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job k from time 0. For example, the
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3. Design a hierarchical plan for t
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trainer, who supplies the input-out
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Definition 13.1: An object is an en
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The restriction of hypotheses can b
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Positive instances: 1. Object (larg
Page 410 and 411:
To illustrate how LEX performs inte
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alive dead Z=A/W 2 1 0 0 1 2 Living
Page 414 and 415:
where Pi denotes the proportion of
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3. Apply Mapping Function: Apply th
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determine the animals because it do
Page 420 and 421:
simplest form of reinforcement lear
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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
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know f ? These questions can be ans
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|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
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transverse diameter, etc. as the in
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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
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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
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ANTECEDENT 1: Predicate: END conclu
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Rules properly arranged MAIN MENU:
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Level POSITION_FROM_LEFT NODE 1 1 H
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calculating membership values enter
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C > File currently in progress = ci
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When the goal is within concave obs
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path selected 160,160 and 240,240 T
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(a) Robot entering the room (b) Aft
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where tr and Outr denote the target
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∂E/∂Wsp = ∑r (∂E/∂Outr) (
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Proof of theorem10.2: For an oscill
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Thus, we get analogously , and 1 0
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= Q 'f m T o Rf m o ( Q ' f m o I )
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Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

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