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[3] Cooke, N. and Macdonald, J., “A formal methodology for acquiring and representing expert knowledge,” Proc. of the IEEE, vol. 74, no. 10, pp. 1422-1430, 1986. [4] Degreef, P. and Breuker, J., “A case study in structured knowledge acquisition,” Proc. of IJCAI, pp. 390-392, 1985. [5] Gaschnig, J., Klahr, P., Popple, H., Shortliffe, E. and Terry, A., “Evaluation of expert systems: Issues and case studies,” Building Expert Systems, Hayes-Roth, F., Waterman, D. A. and Lenat, D. B., Eds., Addison–Wesley, Reading, MA, 1983. [6] Hart, A., Knowledge Acquisition for Expert Systems, McGraw-Hill, New York, 1986. [7] Hayes-Roth, F., Waterman, D. and Leant, D., Building Expert Systems, Addison-Wesley, Reading, MA,1983. [8] Konar, A. and Mandal, A. K., “ Uncertainty management in expert systems using fuzzy Petri nets,” IEEE Trans. on Knowledge and Data Engg., vol. 8, no. 1, pp. 96-105, 1996. [9] Konar, A. and Pal, S., Modeling cognition with fuzzy neural nets, In Fuzzy Systems Theory: Techniques and Applications, Leondes, C. T., Ed., Academic Press, New York, 1999. [10] Kosko, B., “Fuzzy associative memory systems,” In Fuzzy Expert Systems, Kandel, A., Ed., Addison-Wesley, Reading, MA, Dec. 1986. [11] Looney, C. G., “Fuzzy Petri nets for rule based decision making,”IEEE Trans. on Systems, Man and Cybernetics, vol. SMC- 18, no. 1, Jan./ Feb., 1988. [12] McGraw, K. and Seale, M., “Knowledge elicitation with multiple experts: Considerations and techniques,” Artificial Intelligence Review, vol. 2, no. 1, Jan. / Feb., 1988. [13] McGraw, K. L. and Harbison-Briggs, K., Knowledge Acquisition: Principles and Guidelines, Prentice-Hall, Englewood Cliffs, NJ, pp. 280-321,1989. [14] Mittal, S. and Dym, C., “Knowledge acquisition from multiple experts,” The AI Magazine, vol. 7, no. 2, pp. 32-37, 1985.
[15] Paul, B., Konar, A. and Mandal, A. K., “Estimation of certainty factor of rules by Associative Memory realised with Fuzzy Petri Nets,” Proc. of IEEE Int. Conf. on Automation, Robotics and Computer Vision, Singapore, 1996. [16] Prerau, D., “ Knowledge acquisition in the development of a large expert system,” AI Magazine, vol. 8, no. 2, pp. 43-52, 1987. [17] Rush, R. and Wallace, W. A., “ Elicitation of knowledge from multiple experts using network inference, IEEE Trans. on Knowledge and Data Engg., vol. 9, no. 5, pp. 688-696, 1997.
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Artificial Intelligence and Soft Co
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Library of Congress Cataloging-in-P
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understanding of the subject. The r
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4. Structural organization of the b
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ACKNOWLEDGMENT The author gratefull
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To my parents, Mr. Sailen Konar and
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Chapter 2: The Psychological Perspe
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5.4.2 Syntactic Methods for Theorem
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9.4.4 Realization of Fuzzy Inferenc
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Chapter 14: Machine Learning Using
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17.4.2.7 Fusing Multi-sensory Data
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22.4 Parallelism at Knowledge Repre
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1 Introduction to Artificial Intell
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instances of decision making [27],
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End. Begin state := new-states - ex
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(a) Generate and Test Approach: Thi
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this problem is useful for the foll
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disciplines. As a young discipline
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The bird and its attributes here ha
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[19] , Kosko [15] and Pedrycz [30]
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Reasoning in the presence of imprec
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where AI finds extensive applicatio
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M1 M2 M3 J2 J 1 8 5 J 2 J 1 2 8 J 2
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1.5.3 The Modern Period The modern
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A B AND C D E AND Y Fig. 1.12: A Pe
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improve reliability by realizing fr
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in AI problems, it supports massive
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[7] Dean, T., Allen, J. and Aloimon
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[34] Rajendra, C. and Chaudhury, D.
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2 The Psychological Perspective of
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The scope of realization of the pro
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2.2.3 Feature-based Approach for Pa
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From Sensory Registers Echoic Reg.
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cognitive memory and its utilizatio
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Tulving’s model bridges a potenti
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A " (a) (b) Fig. 2.6: The character
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his friend s facial image in memory
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sum need not be 180 degrees. Thus a
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same evening. Then he started medit
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construction of knowledge and its o
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saved in a magnetic media, which he
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set-point + error Controller Plant
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Cognitive science has emerged as a
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Further show that the Euclidean lea
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[4] Biswas, B., Mukherjee, A. K. an
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[28] Peterson, M. A., Kihlstrom, J.
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Production Systems, Logical Calculu
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against the data items of the WM. I
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Suppose the WM contains the data Bi
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To demonstrate the working principl
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i) Prefer rules for firing, where u
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In fig. 3.3, the antecedent clauses
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possible offspring states are gener
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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
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[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
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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) =
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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
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5.4.1 Semantic Method for Theorem P
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8. p \/ (q Λ ¬ q) ⇔ p 9. p Λ (
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) AND, OR Removal: If the L.H.S. co
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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
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Examination Hall Seat Arrangement I
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Petri nets [5]. It, however, does n
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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
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where D and S stand for disease and
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To illustrate the reasoning process
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2. It might happen that none of the
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One interesting property of Bayesia
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We now compute the messages that no
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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
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[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
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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
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10.9 Conclusions The chapter presen
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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
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Procedure Move-optimal (R , Startin
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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)
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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
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where On (X, Y) means the object X
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this, let us consider the last prob
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(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
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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
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To illustrate how LEX performs inte
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alive dead Z=A/W 2 1 0 0 1 2 Living
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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
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simplest form of reinforcement lear
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Let us now assume that the game is
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How can we compute the utility valu
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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
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[7] Michalski, R. S., “A theory a
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However, most biologists are of the
Page 436 and 437:
The most common non-linear function
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(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
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Drawbacks of back-propagation algor
Page 446 and 447:
x1 x2 xn 14.6 Widrow-Hoff's Multi-l
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x 1 x 2 x 1 x 2 The training proces
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• 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
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− ~ w X k, l ∼ = ⎡ ⎤ ⎢
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V x ∑ w O j = Sgn ( j+ ji i + A
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neural nets in the fields mentioned
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where the first summation is over a
Page 466 and 467:
circuit,” IEEE Trans. on Circuits
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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
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15.2 Deterministic Explanation of H
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Thus, in a total of N selections wi
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and population size = 2, which mean
Page 478 and 479:
The behavior of GA without mutation
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a = ∑ Lt (π 0 P n ) i i=1 n→
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Eθ GA Program Z1, Z2, Z3, Z4, X1,
Page 484 and 485:
e [W1 W2 W3 W4 W5 W6] T and [ G1 G2
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function. So, GA in each evolution
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f(x) = Sin (x) + [x * x + y] 1/2 ca
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(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
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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
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1. Move the mask over the image, so
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∇ 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
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schematic diagram, briefly outlinin
Page 528 and 529:
same process for all clusters, we g
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Level 1 Level 2 Level 0 Neuron Fig.
Page 532 and 533:
I (u, v) YI Fig. 17.12: Camera mode
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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
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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
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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 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
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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
Page 788:
= Q 'f m T o Rf m o ( Q ' f m o I )
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