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Examination Hall Seat Arrangement Invigilators Examinees Question Paper : 1Qu/ Candidate Answer Scripts: 1script/candidate A Preferential seat arrangement of ETCE dept. of J.U. Euclidean Distance between candidate of same group : to be maximized . Exam. Hall of Jadavpur Univ . (J.U) Seat arrang.: Invigi : at least 2/ room. Examinee : 2 group of examinees/room . Question paper : 1 Question / Candidate. Answer script: 1 answer Script/ Candi. / hall Fig. 8.11: A frame describing an examination hall. B D Seat arrangement of ETCE dept. at J.U. No. of candidate / bench : 2 Candidate in the same bench should be :of different class/ year. Preferential Arrangement. C
Can Is-a Bird Has-wings: 2 Body covered with feathers Fly Altitude: high Tweety Has- wings: 2 Broken -wings: 1 Birds, having lost aeronautical prowess Has-wings: 2 Is -a This is resolved here through a principle called “follow minimal inferential distance path” [11]. Since ‘’ type of implication is more specific than the default ‘--->’ type of implication , we prefer Tweety Birds, having lost aeronautical prowess with respect to Tweety ---> Birds. The former rule being predominant, the reasoning goes in favor of Birds having lost aeronautical power ----/--> Fly. So, finally we have Tweety --/--> Fly as the conclusion. 8.8 Petri Nets Fig. 8.12: Illustrating reasoning in a frame. Another structured model that is gaining popularity currently is Petri nets. A Petri net is a directed bipartite graph consisting of places and transitions. It is getting popular for its capability of reasoning in a parallel and distributed manner. In this section we limit our discussion on FOL based reasoning with Can
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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 (
Page 200 and 201: Definition 6.5: A definite goal is
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
Page 252 and 253: Petri nets [5]. It, however, does n
Page 254 and 255: Since p1 and p 2 contain tokens and
Page 256 and 257: For building dependency structures,
Page 258 and 259: The most significant drawback of CD
Page 260 and 261: Once a script structure for a given
Page 262 and 263: [6] Nilsson, N. J., Principles of A
Page 264 and 265: techniques, we first introduce the
Page 266 and 267: of precision of data and certainty
Page 268 and 269: where D and S stand for disease and
Page 270 and 271: To illustrate the reasoning process
Page 272 and 273: 2. It might happen that none of the
Page 274 and 275: One interesting property of Bayesia
Page 276 and 277: We now compute the messages that no
Page 278 and 279: The main steps [8], [12] of the bel
Page 280 and 281: Updating a node X thus involves upd
Page 282 and 283: j ↓ i→ round oval-shaped and MB
Page 284 and 285: Formally, the set of all possible o
Page 286 and 287: The orthogonal summation of belief
Page 288 and 289: The orthogonal summation operations
Page 290 and 291: integers, then we can definitely sa
Page 292 and 293: where a / b in fast-runner subset r
Page 294 and 295: where the “o” denotes a fuzzy A
Page 296 and 297: This may be formally written as µ
Page 298 and 299: 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
Page 494 and 495:
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+
Page 508 and 509:
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
Page 522 and 523:
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
Page 542 and 543:
Sample Execution: This is a program
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U t11 t12 t13 t14 x V = t21 t22 t23
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obtained after linearizing measurem
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co-ordinate systems. The correspond
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18 Linguistic Perception Building p
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psychological aspects of understand
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S NP VP ART N VP the man VP V NP ki
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( S ( NP ( ( ART the ) (N man ) ) (
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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
Page 584 and 585:
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
Page 596 and 597:
The possible labels of the junction
Page 598 and 599:
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
Page 606 and 607:
The database in fig. 20.2 is extrac
Page 608 and 609:
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
Page 614 and 615:
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
Page 624 and 625:
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
Page 630 and 631:
Ancestor P = {p1,p2} Tr = {tr1,tr2,
Page 632 and 633:
21.3.2 Inconsistencies in Knowledge
Page 634 and 635:
q tr1 tr2 p1 p2 …………… pn
Page 636 and 637:
A rule may also include a number of
Page 638 and 639:
21.4 Maintenance of Knowledge Based
Page 640 and 641:
control of data and knowledge is un
Page 642 and 643:
8. Stefik, M., Introduction to Know
Page 644 and 645:
are not suitable for AI application
Page 646 and 647:
Because of non-determinism in the s
Page 648 and 649:
Procedure Partial-Expansion Begin W
Page 650 and 651:
Step: Fig.22.4: A tree expanded by
Page 652 and 653:
Procedure Generous-Sharing Begin Fl
Page 654 and 655:
To evaluate the performance of the
Page 656 and 657:
Definition 22.2: If the antecedents
Page 658 and 659:
a) AND -Parallelism Consider a logi
Page 660 and 661:
However, given sufficient computing
Page 662 and 663:
EPN = { P, Tr, D, f, m, A, a, I, o
Page 664 and 665:
It is to be noted that if- then ope
Page 666 and 667:
After the bindings of the variables
Page 668 and 669:
F(x) ← A(X) , B(Y) (1) A(1) ← (
Page 670 and 671:
the result produced by clause (2) m
Page 672 and 673:
Else Flag:= true; End; Until no-of
Page 674 and 675:
transition for the PTVVM. The PTVVM
Page 676 and 677:
For the sake of convenience, the pi
Page 678 and 679:
22.6 Conclusions The chapter starte
Page 680 and 681:
Rule 3: S(X, Y) → Z (Y, X) Rule 4
Page 682 and 683:
23 Case Study I: Building a System
Page 684 and 685:
features in human faces and their r
Page 686 and 687:
Definition 23.4: A linear edge segm
Page 688 and 689:
lock containing edge. The productio
Page 690 and 691:
Definition 23.13: The measure of di
Page 692 and 693:
Partitioning Fe into Blocks Feature
Page 694 and 695:
The fuzzy membership functions used
Page 696 and 697:
∆ • Fig. 23.4: The delta and th
Page 698 and 699:
Thus we have altogether 6 different
Page 700 and 701:
cross-section of the vocal tract, w
Page 702 and 703:
23.4.2 Training a Multi-layered Neu
Page 704 and 705:
information with their normalized v
Page 706 and 707:
SR (Y, X) OR (M (X) ∧ M (Y) ∧ L
Page 708 and 709:
procedure, however, no attempts hav
Page 710 and 711:
23.5.5 Belief Revision and Limitcyc
Page 712 and 713:
that the inferences will be minimal
Page 714 and 715:
the formation of the FPN. The FPN o
Page 716 and 717:
is then initiated for detecting the
Page 718 and 719:
initial fuzzy beliefs at all places
Page 720 and 721:
at all feasible x, y. Then estimate
Page 722 and 723:
[13] Malmberg, B., Manual of Phonet
Page 724 and 725:
24.1 Mobile Robots Mobile robots ar
Page 726 and 727:
about its world. There exists ample
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the process, else it attempts to fi
Page 730 and 731:
Depending on the type of planning p
Page 732 and 733:
WEST NORTH SOUTH Fig. 24.5: Represe
Page 734 and 735:
NW child B NE child 'C' SW child 'D
Page 736 and 737:
As observed from the node status, t
Page 738 and 739:
In their evolutionary planner algor
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tuning of the path, such as avoidin
Page 742 and 743:
S S= starting point, G= Goal point,
Page 744 and 745:
Once the training is over, the syst
Page 746 and 747:
N o Table 24.2: Training pattern sa
Page 748 and 749:
24.9.1 Finite State Machine Finite
Page 750 and 751:
occurrence of a state, but also in
Page 752 and 753:
24.10 An Application in a Soccer Pl
Page 754 and 755:
Exercises 1. Devise an algorithm fo
Page 756 and 757:
[15] Meng, H. and Picton, P. D.,
Page 758 and 759:
24 + The Expectations from the Read
Page 760 and 761:
Appendix A How to Run the Sample Pr
Page 762 and 763:
Predicate: LSR Argument1: S Argumen
Page 764 and 765:
ANTECEDENT 1: Predicate: END conclu
Page 766 and 767:
Rules properly arranged MAIN MENU:
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Level POSITION_FROM_LEFT NODE 1 1 H
Page 770 and 771:
calculating membership values enter
Page 772 and 773:
C > File currently in progress = ci
Page 774 and 775:
When the goal is within concave obs
Page 776 and 777:
path selected 160,160 and 240,240 T
Page 778 and 779:
(a) Robot entering the room (b) Aft
Page 780 and 781:
where tr and Outr denote the target
Page 782 and 783:
∂E/∂Wsp = ∑r (∂E/∂Outr) (
Page 784 and 785:
Proof of theorem10.2: For an oscill
Page 786 and 787:
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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