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[4] Chakraborty, U. K. and Muehlenbein, H., “Linkage equilibrium and genetic algorithms,” Proc. 4 th IEEE Int. Conf. On Evolutionary Computation, Indianapolis, pp. 25-29, 1997. [5] Chakraborty, U. K. and Dastidar, D. G., “Using reliability analysis to estimate the number of generations to convergence in genetic algorithm,” Information Processing Letters, vol. 46, pp. 199-209, 1993. [6] Davis, T. E. and Principa, J. C., “ A Markov chain framework for the simple genetic algorithm,” Evolutionary Computation, vol. 1, no. 3, pp. 269-288, 1993. [7] De Jong, K. A., An analysis of behavior of a class of genetic adaptive systems, Doctoral dissertation, University of Michigan, 1975. [8] Fogel, D. B., Evolutionary Computation, IEEE Press, Piscataway, NJ, 1995. [9] Filho, J. L. R. and Treleven, P. C., Genetic Algorithm Programming Environment, IEEE Computer Society Press, pp. 28-43, June 1994. [10] Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989. [11] Gupta, B., “Bandwidth enhancement of Microstrip antenna through optimal feed using GA,” Seminar on Seekers and Aerospace Sensors, Hyderabad, India, 1999. [12] Holland, J. H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975. [13] Koza, J. R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992. [14] Mc Donell, J. R., “Control” in Handbook of Evolutionary Computation, Back, T., Fogel, D. B. and Michalewicz, Z., Eds., IOP and Oxford University Press, New York, 1998. [15] Mitchell, M., An Introduction to Genetic Algorithms, MIT Press, Cambridge, MA, 1996.
[16] Muehlenbein, H. and Chakraborty, U. K., “Gene pool recombination genetic algorithm and the onemax function,” Journal of Computing and Information Technology, vol. 5, no. 3, pp. 167-182, 1997. [17] Michalewicz, Z, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin, 1992. [18] Srinivas, M. and Patnaik, L. M., “Genetic search: Analysis using fitness moments,” IEEE Trans. on Knowledge and Data Engg., vol. 8, no. 1, pp. 120-133, 1996. [19] Vose, M. D. and Liepins, G. E., “Punctuated equilibrium in genetic search,” Complex Systems, vol. 5, pp. 31-44, 1991.
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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
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For building dependency structures,
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The most significant drawback of CD
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Once a script structure for a given
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[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
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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
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 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
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U t11 t12 t13 t14 x V = t21 t22 t23
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obtained after linearizing measurem
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co-ordinate systems. The correspond
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18 Linguistic Perception Building p
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psychological aspects of understand
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S NP VP ART N VP the man VP V NP ki
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( 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
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Generally, we start the sentence S
Page 566 and 567:
VP: V NP S0 S1 Tagged procedure V:=
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Laugh agent animal enjoyer entity p
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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
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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
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Step 1: (x
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such variable xj , xk, etc., attach
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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:
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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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