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1.5.3 The Modern Period The modern period starts from the latter half of the 1970s to the present day. This period is devoted to solving more complex problems of practical interest. The MYCIN experiments of Stanford University [4], [39] resulted in an expert system that could diagnose and prescribe medicines for infectious bacteriological diseases. The MECHO system for solving problems of Newtonian machines is another expert system that deals with real life problems. It should be added that besides solving real world problems, researchers are also engaged in theoretical research on AI including heuristic search, uncertainty modeling and non-monotonic and spatio-temporal reasoning. To summarize, this period includes research on both theories and practical aspects of AI. 1.6 Characteristic Requirements for the Realization of the Intelligent Systems The AI problems, irrespective of their type, possess a few common characteristics. Identification of these characteristics is required for designing a common framework for handling AI problems. Some of the well-known characteristic requirements for the realization of the intelligent systems are listed below. 1.6.1 Symbolic and Numeric Computation on Common Platform It is clear from the previous sections that a general purpose intelligent machine should be able to perform both symbolic and numeric computations on a common platform. Symbolic computing is required in automated reasoning, recognition, matching and inductive as well as analogy-based learning. The need for symbolic computing was felt since the birth of AI in the early fifties. Recently, the connectionist approach for building intelligent machines with structured models like artificial neural nets is receiving more attention. The ANN based models have successfully been applied in learning, recognition, optimization and also in reasoning problems [29] involved in expert systems. The ANNs have outperformed the classical approach in many applications, including optimization and pattern classification problems. Many AI researchers, thus, are of the opinion that in the long run
the connectionist approach will replace the classical approach in all respects. This, however, is a too optimistic proposition, as the current ANNs require significant evolution to cope with the problems involved in logic programming and non-monotonic reasoning. The symbolic and connectionist approach, therefore, will continue co-existing in intelligent machines until the latter, if ever, could replace the former in the coming years. 1.6.2 Non-Deterministic Computation The AI problems are usually solved by state-space approach, introduced in section 1.3. This approach calls for designing algorithms for reaching one or more goal states from the selected initial state(s). The transition from one state to the next state is carried out by applying appropriate rules, selected from the given knowledge base. In many circumstances, more than one rule is applicable to a given state for yielding different next states. This informally is referred to as non-determinism. Contrary to the case, when only one rule is applicable to a given state, this system is called deterministic. Generally AI problems are non-deterministic. The issues of determinism and nondeterminism are explained here with respect to an illustrative knowledge-based system. For instance, consider a knowledge base consisting of the following production rules and database. Production Rules PR1: IF (A) AND (B) THEN ( C ). PR2: IF ( C ) THEN ( D). PR3: IF ( C ) AND ( E ) THEN (Y). PR4: IF (Y) THEN (Z). Database: A, B, E. The graph representing the transition of states for the above reasoning problem is presented in fig.1.12. Let A and B be starting states and Z be the goal state. It may be noted that both PR2 and PR3 are applicable at state (C) yielding new states. However, the application of PR3 at state (C) can subsequently lead to the goal state Z, which unfortunately remains unknown until PR4 is applied at state Y. This system is a typical example of nondeterminism. The dropping of PR2 from the knowledge base, however, makes the system deterministic. One formal approach for testing determinism / nondeterminism of a reasoning system can be carried out by the following principle:
Page 2 and 3: Artificial Intelligence and Soft Co
Page 4 and 5: Library of Congress Cataloging-in-P
Page 6 and 7: understanding of the subject. The r
Page 8 and 9: 4. Structural organization of the b
Page 10 and 11: ACKNOWLEDGMENT The author gratefull
Page 12 and 13: To my parents, Mr. Sailen Konar and
Page 14 and 15: Chapter 2: The Psychological Perspe
Page 16 and 17: 5.4.2 Syntactic Methods for Theorem
Page 18 and 19: 9.4.4 Realization of Fuzzy Inferenc
Page 20 and 21: Chapter 14: Machine Learning Using
Page 22 and 23: 17.4.2.7 Fusing Multi-sensory Data
Page 24 and 25: 22.4 Parallelism at Knowledge Repre
Page 26 and 27: 1 Introduction to Artificial Intell
Page 28 and 29: instances of decision making [27],
Page 30 and 31: End. Begin state := new-states - ex
Page 32 and 33: (a) Generate and Test Approach: Thi
Page 34 and 35: this problem is useful for the foll
Page 36 and 37: disciplines. As a young discipline
Page 38 and 39: The bird and its attributes here ha
Page 40 and 41: [19] , Kosko [15] and Pedrycz [30]
Page 42 and 43: Reasoning in the presence of imprec
Page 44 and 45: where AI finds extensive applicatio
Page 46 and 47: M1 M2 M3 J2 J 1 8 5 J 2 J 1 2 8 J 2
Page 50 and 51: A B AND C D E AND Y Fig. 1.12: A Pe
Page 52 and 53: improve reliability by realizing fr
Page 54 and 55: in AI problems, it supports massive
Page 56 and 57: [7] Dean, T., Allen, J. and Aloimon
Page 58 and 59: [34] Rajendra, C. and Chaudhury, D.
Page 60 and 61: 2 The Psychological Perspective of
Page 62 and 63: The scope of realization of the pro
Page 64 and 65: 2.2.3 Feature-based Approach for Pa
Page 66 and 67: From Sensory Registers Echoic Reg.
Page 68 and 69: cognitive memory and its utilizatio
Page 70 and 71: Tulving’s model bridges a potenti
Page 72 and 73: A " (a) (b) Fig. 2.6: The character
Page 74 and 75: his friend s facial image in memory
Page 76 and 77: sum need not be 180 degrees. Thus a
Page 78 and 79: same evening. Then he started medit
Page 80 and 81: construction of knowledge and its o
Page 82 and 83: saved in a magnetic media, which he
Page 84 and 85: set-point + error Controller Plant
Page 86 and 87: Cognitive science has emerged as a
Page 88 and 89: Further show that the Euclidean lea
Page 90 and 91: [4] Biswas, B., Mukherjee, A. K. an
Page 92 and 93: [28] Peterson, M. A., Kihlstrom, J.
Page 94 and 95: Production Systems, Logical Calculu
Page 96 and 97: against the data items of the WM. I
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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
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Thus the elements of communicative
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19 Problem Solving by Constraint Sa
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The recognition problem is the pres
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CSP deals with two classical proble
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= 10 10 = 10 = * Fig. 19.2 (b): Pro
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+ _ V1 R1 V 2 V3 I1 I2 I3 V R2 R3 S
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V = Now assuming the values of V, R
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On (A, Ta) ∧ On (B, Ta) ∧On (C,
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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
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The possible labels of the junction
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The main part of CSP is the formula
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20 Acquisition of Knowledge Acquisi
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invite the experts to attend a meet
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B E A= test ?, B = test results, C
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The database in fig. 20.2 is extrac
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20.5 Knowledge Refinement by Hebbia
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* * * n i ( t + 1) = n i ( t ) at t
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L(r,s) L(s,r) Y(r) Y(s) OS(r,s) n1
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The FPN of fig. 20.5 is formed with
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L(a,l) L(l,a) Y(a) Y(l) OS(a,l) n1
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[3] Cooke, N. and Macdonald, J.,
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21 Validation, Verification and Mai
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Here the problem characteristic is
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The chapter will cover various issu
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21.2.1 Qualitative Methods for Perf
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Paired t-test: Suppose xi ∈ X and
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Ancestor P = {p1,p2} Tr = {tr1,tr2,
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21.3.2 Inconsistencies in Knowledge
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q tr1 tr2 p1 p2 …………… pn
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A rule may also include a number of
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21.4 Maintenance of Knowledge Based
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control of data and knowledge is un
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8. Stefik, M., Introduction to Know
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are not suitable for AI application
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Because of non-determinism in the s
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Procedure Partial-Expansion Begin W
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Step: Fig.22.4: A tree expanded by
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Procedure Generous-Sharing Begin Fl
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To evaluate the performance of the
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Definition 22.2: If the antecedents
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a) AND -Parallelism Consider a logi
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However, given sufficient computing
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EPN = { P, Tr, D, f, m, A, a, I, o
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It is to be noted that if- then ope
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After the bindings of the variables
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F(x) ← A(X) , B(Y) (1) A(1) ← (
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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
Page 752 and 753:
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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