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Definition 6.5: A definite goal is a clause of the form ← B1, B2, ..., Bn i.e., a clause with an empty consequent. Definition 6.6: SLD resolution stands for SL resolution for definite clauses, where SL stands for resolution with linear selection function. Example 6.3: This example illustrates the linear resolution. Consider the following OR clauses, represented by a set-like notation. Let S = {A1, A2, A3, A4}, A1 = {P(X), Q (X)}, A2= { P(X), ¬ Q(X)}, A3 = {¬ P(X), Q(X)}, A4 = {¬ P(X), ¬ Q(X)} and Goal = ¬ P(X). A 1 A 2 {P ( X)} A 3 {Q (X)} A 4 {¬ P(X)} { P (X)} ϕ Fig. 6.3: The linear selection of clauses in the resolution tree.
The resolution tree for linear selection is presented in fig. 6.3. It is clear that two clauses from the set S1 = S ∪ { ¬ Goal} are first used for resolution and the resolvent is next used for resolution with a third clause from the same set S1. The process is continued until a null clause is generated. In the linear selection process, one clause, however, can be used more than once for resolution. An alternative way to represent the resolution process in a tree with linear selection is presented below. Such trees are generally referred to as SLD trees. Let us now consider the following Logic program and draw the SLD tree for the program. Example 6.4: The Logic program built with definite clauses and the goal are presented below. 1. P (X, Z) ← Q (X, Y) , P (Y, Z) 2. P (X, X) ← 3. Q (a, b) ← Goal : ← P (X, b) For the construction of the SLD tree, we would match the head of a clause with the same consequent clause in another's body, during the process of resolution of two clauses. This, however, in no way imposes restriction in the general notion of resolution. Rather, it helps the beginners to mechanically realize the resolution process. The SLD tree of a Logic Program becomes infinite, if one uses the same rule many times. For instance using rule 1 many times, we find an infinite SLD tree like fig. 6.4 for the current example. 6.6 Controlling Backtracking by CUT Since a PROLOG compiler searches for the solution using a “depth first search” strategy, the leftmost sub-tree in fig. 6.4 being infinite does not yield any solution of the system. Such a problem, however, can be taken care of by appropriately positioning “CUT” statements in the Logic Program. Since depth first search in PROLOG programs are realized by stack, an infinitely large SLD tree results in a stack overflow.
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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
Page 150 and 151: If the values are propagated up to
Page 152 and 153: ii) the alpha value of any MAX node
Page 154 and 155: αmin =2 βmax =1 C αmin =1 e(n) =
Page 156 and 157: eductions. Constraint satisfaction
Page 158 and 159: 5 The Logic of Propositions and Pre
Page 160 and 161: 5.2 Formal Definitions The followin
Page 162 and 163: Definition 5.7: A propositional for
Page 164 and 165: 5.4.1 Semantic Method for Theorem P
Page 166 and 167: 8. p \/ (q Λ ¬ q) ⇔ p 9. p Λ (
Page 168 and 169: ) AND, OR Removal: If the L.H.S. co
Page 170 and 171: Since all the terminals of the tree
Page 172 and 173: p v q ¬ q v r p v r ¬ p v s r v s
Page 174 and 175: Let AS = {A1, A2,, …..,An} be the
Page 176 and 177: 4. If P is a WFF and X is not a qua
Page 178 and 179: original expression: ( ¬ P11 ∨
Page 180 and 181: S: = S ∪ {variable or constant /
Page 182 and 183: ¬ Loves (john, mary) ¬Child (X)
Page 184 and 185: which implies that if q is true the
Page 186 and 187: ii) C is a ground instance of C [9]
Page 188 and 189: ) p→ (q→ r) ⇔ (p Λ q) → r
Page 190 and 191: [6] Leinweber, D., “Knowledge-bas
Page 192 and 193: of a ‘classroom scene’ interpre
Page 194 and 195: Sits-on (Y, bench, classhour), Pers
Page 196 and 197: Sits-on (mita, bench, classhour). 3
Page 198 and 199: Teacher (X) Sub-goal fails Person (
Page 202 and 203: Example 6.5, presented below, illus
Page 204 and 205: 6.6.1 Risk of Using CUT It is to be
Page 206 and 207: In the SLD tree for the Taxpayer pr
Page 208 and 209: 6.9 Fixed Points in Non-Horn Clause
Page 210 and 211: 6.11 Conclusions The main advantage
Page 212 and 213: preferred X50 >= 2, default X100
Page 214 and 215: 7.1 Introduction Predicate Logic is
Page 216 and 217: proposed this logic and called it n
Page 218 and 219: We now present a few modal properti
Page 220 and 221: Expert Reasoning System New data Dy
Page 222 and 223: PR5: ¬Visits (Wife, market, Monday
Page 224 and 225: IN (n1) + IN (n2) + (n11) retracts
Page 226 and 227: may hold (and not must hold). An ex
Page 228 and 229: The resulting stable consequences t
Page 230 and 231: In this example, we call ψ = Bird
Page 232 and 233: important factor in non-monotonic r
Page 234 and 235: a) Boy(X) ∧ ¬ L ¬ Likes (X, cho
Page 236 and 237: 8 Structured Approach to Knowledge
Page 238 and 239: predicate boy (jim). This can be re
Page 240 and 241: Consequently, we represent the abov
Page 242 and 243: Is-a Is-a Is-a Protozoa Bacteria Is
Page 244 and 245: Example 8.2: This example illustrat
Page 246 and 247: A common question that may be raise
Page 248 and 249: ∴ 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
Page 646 and 647:
Because of non-determinism in the s
Page 648 and 649:
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
Page 656 and 657:
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
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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.,
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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
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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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