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Here the problem characteristic is to determine current through a component or the voltage drop across a component and the specifications for the problem include voltage drop across or current through a different component. Once the (input) specifications and the (output) requirements of the problems are known, we have to search concepts that can represent the problems. In case of electrical circuits, Kirchoff’s laws, Ohm’s laws, Thevenin’s theorem, reciprocity theorem, superposition theorem, etc. correspond to concepts that may be used to represent the problem. The next task is to design structures to organize knowledge. In electrical circuit problems, ‘finding structures’ means determining the appropriate rules (laws/theorems) to represent the problem. For instance, in a simple loop, when the current through a passive component is known, and the value of the component is also known, we can employ Ohm’s law to find the drop across it. When the currents through some of the components and /or voltage drops across one or more components are known and there exist a number of loops, we could employ Kirchoff’s law or Maxwell’s loop equation. Thus the structures in the present context could be of the following type. IF & & THEN use . Here, the input specification and the output requirement correspond to voltage and currents supplied and to be evaluated respectively. The ‘configuration’ in the present context may refer to number of loops (one or more). A look at fig. 21.2 will be useful to understand the structure used to formulate concepts. 1. IF & & THEN apply . 2. IF & & THEN apply . 3. IF & & THEN apply . . . . Fig. 21.2: Instances of the structure representing a concept. It may be added here that the structure describing the concepts need not be always IF-THEN statements. Structure and pointers, filler and slots or any other AI techniques commonly used to represent knowledge could equally represent it. After the construction of the structure is over, its component,
here, for instance, the THEN part of each rule has to be encoded by some pieces of knowledge. Ohm’s law, for example, can be formalized as follows: IF V= (voltage across a component) & Z= (impedance of the component) & I= (current through the component) THEN V:= I * Z. Kirchoff’s voltage law (KVL) in a loop can be represented as follows: IF (no. of circuit component in a loop = n) & (voltage across and current through Z1 are V1 and I1 ) & (voltage across and current through Z2 are V2 and I2) & . . . . (voltage across and current through Zn are Vn and In) & (total driving emf in a loop = V) THEN ( V = V1 + V2 + …….+ Vn ) OR (V = I1 * Z1 + I2 * Z2 + …+ In*Zn). After the construction of the knowledge base, the performance of the system in fig. 21.1 is evaluated through verification and validation of the system. A question that then naturally arises is how to compare the performance of the proposed system with an ‘ideal’ one. One way to solve the problem is to call the expert, based on whose knowledge the system is built. Suppose the expert selects superposition theorem to solve a problem but the expert system built with his expertise cannot identify ‘superposition’ theorem as the best choice for solving the problem. Obviously, there is a problem with the system design. Most likely there exist some loopholes in the identification of problem characteristics. So, reformulation of the problem characteristics through validation of the system could overcome this problem. In case reformulation of the new problem characteristics cannot be inferred by the validation procedure, then the structures used to represent concepts or the knowledge base could be re-designed. This is done by invoking a verification procedure. Apart from validation and verification, another significant issue in improving the performance of an expert system lies with its maintenance. The knowledge base of an expert system is gradually enriched with new pieces of knowledge. Unless there is some formalization of entering new rules, inconsistencies in various forms may creep into the system. Further, there must be some sort of partitioning in the knowledge base; otherwise, the system will suffer from inefficient pattern matching cycle.
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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
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x 1 x 2 x n Penultimate layer ∑+F
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Drawbacks of back-propagation algor
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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,
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ni 1 = '1' output ( firing ). Each
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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
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circuit,” IEEE Trans. on Circuits
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their joint usage in many problems,
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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
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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,
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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)
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[4] Chakraborty, U. K. and Muehlenb
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16 Realizing Cognition Using Fuzzy
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Cognitive maps are generally used f
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Theorem 16.1: With initial values o
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Here W T is a transposed weight mat
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example, for moving one’s arm, th
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and the weight adaptation equation
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Xk+1 ∆Wk = α Ek οXk+1 T / ((Xk+
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d5 d9 d1 d6 d7 d8 d10 d11 d1=Front
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16.10 Conclusions and Future Direct
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[6] Kosko, B., “Fuzzy cognitive m
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however, have a narrow spectrum of
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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
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It may be noted that segmenting an
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machine has to narrate the game onl
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schematic diagram, briefly outlinin
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same process for all clusters, we g
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Level 1 Level 2 Level 0 Neuron Fig.
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I (u, v) YI Fig. 17.12: Camera mode
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epipolar planes meet the image plan
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(a, b, 1) Fig. 17.15: A 3-D represe
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case III: Planes not parallel to th
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yi = -fi (xi,* ai - 1*) + ( fi / a
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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 ) ) (
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Sentence: NP VP 1 6 NP: ART N 2 ART
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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
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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
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 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
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Else Flag:= true; End; Until no-of
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transition for the PTVVM. The PTVVM
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For the sake of convenience, the pi
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22.6 Conclusions The chapter starte
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Rule 3: S(X, Y) → Z (Y, X) Rule 4
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23 Case Study I: Building a System
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features in human faces and their r
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Definition 23.4: A linear edge segm
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lock containing edge. The productio
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Definition 23.13: The measure of di
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Partitioning Fe into Blocks Feature
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The fuzzy membership functions used
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∆ • Fig. 23.4: The delta and th
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Thus we have altogether 6 different
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cross-section of the vocal tract, w
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23.4.2 Training a Multi-layered Neu
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information with their normalized v
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SR (Y, X) OR (M (X) ∧ M (Y) ∧ L
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procedure, however, no attempts hav
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23.5.5 Belief Revision and Limitcyc
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that the inferences will be minimal
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the formation of the FPN. The FPN o
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is then initiated for detecting the
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initial fuzzy beliefs at all places
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at all feasible x, y. Then estimate
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[13] Malmberg, B., Manual of Phonet
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24.1 Mobile Robots Mobile robots ar
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about its world. There exists ample
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the process, else it attempts to fi
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Depending on the type of planning p
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WEST NORTH SOUTH Fig. 24.5: Represe
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NW child B NE child 'C' SW child 'D
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As observed from the node status, t
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In their evolutionary planner algor
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tuning of the path, such as avoidin
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S S= starting point, G= Goal point,
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Once the training is over, the syst
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N o Table 24.2: Training pattern sa
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24.9.1 Finite State Machine Finite
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occurrence of a state, but also in
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24.10 An Application in a Soccer Pl
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Exercises 1. Devise an algorithm fo
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[15] Meng, H. and Picton, P. D.,
Page 758 and 759:
24 + The Expectations from the Read
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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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