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21.2.1 Qualitative Methods for Performance Evaluation Validation of an expert system is carried out by qualitative means. Qualitative Validation employs subjective comparison of performances [4]. This section reviews a few methods for qualitative validation for expert systems. Among the qualitative validation schemes, the most popular is Turing test [7] and sensitivity analysis. Turing Test: This test is named after Alan Turing, a pioneering AI researcher. Turing’s view on intelligence can be stated as follows. A problem solving machine is intelligent, if a person trying to assess the intelligence of the machine cannot determine its identity, while working with a human problem solver and a computing machine. This method of testing intelligence of a machine is called Turing test. Turing’s test has successfully been applied to evaluate performance of expert systems like MYCIN and ONCOCIN [4]. For assessing the performance of an expert system by Turing’s test, the expert based on whose expertise the system is built and the expert system are assigned common problems. The assessment of the system performance is then compared with that of a human expert by another expert, without disclosing the performer’s identity. If assessments can be measured objectively, then statistical techniques may be employed to measure the variations or consistency between the performance of the expert and the system. Examples of objective measurement of performance include number of correct inferences or the level of performance in grade points like excellent, good, fair, poor, etc. Turing’s test, however, is not applicable to systems, where finding objective measurement of performance itself is a complex problem and thus is not amenable. Input variables v1 Prob.i v3 p1 p2 p3 Expert System Expert inferences inferences Fig. 21.5: Validating an expert system performance through sensitivity analysis. Performance Evaluation
Sensitivity Analysis: ‘Sensitivity’ in electrical engineering means change in response of a system, when there is even a small change in its input excitation. Thus an electrical system M1 is more sensitive than the system M2, if M1 when compared to M2 responds to smaller changes in the input. Sensitivity in expert systems, however, has a wider meaning. It corresponds to a change in inferences for a change in input variables or parameters of the expert system. Fig. 21.5 describes the scheme for validating an expert system performance through sensitivity analysis. Let the value of the input variables supplied by the problem be v1 = v 1, v2 = v2 and v3 = v3 and the internal parameters (like certainty factors, conditional probabilities, etc.) of the expert system be p1 = p1, p2 = p2, p3 =p3. Suppose, the inferences derived by the expert system match the inferences drawn by the expert for the above settings of input variables and parameters. The performance evaluator now adjusts each variable/parameter one by one by a small amount over their current value and observes the change in response of the system and the expert. If the responses are closer (w.r.t. some evaluator), then the design of the expert system is satisfactory; otherwise the system has to be updated with new knowledge or its inference engine has to be re-modeled. Sensitivity analysis, though a most powerful qualitative method for performance evaluation, has not unfortunately been exploited to any commercial expert systems. We hope that in the coming decade, it will play a significant role in performance evaluation of commercial systems. 21.2.2 Quantitative Methods for Performance Evaluation When the responses of an expert system can be quantified (numerically), we may employ quantitative (statistical) tools for its performance evaluation. Generally, for quantitative evaluation of performance, a confidence interval for one or more measure is considered, and the performance of the expert system w.r.t. the expert is ascertained for a given level of confidence (95% or 99%). If the confidence interval obtained, for an expert system for a given level of confidence, is satisfactory the system requires no adjustment; otherwise we need to retune the knowledge base and the inference engine of the system. An alternative way of formulation is to define a hypothesis H0 and test whether we would accept or reject the hypothesis w.r.t. a predefined performance range. The hypothesis H0 may be of the following form. H0: The expert system is valid for the acceptable performance range under the prescribed input domain. Among the quantitative methods, the most common are paired t-test [4] and Hotelling’s one sample T 2 test.
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Artificial Intelligence and Soft Co
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Library of Congress Cataloging-in-P
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understanding of the subject. The r
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4. Structural organization of the b
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ACKNOWLEDGMENT The author gratefull
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To my parents, Mr. Sailen Konar and
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Chapter 2: The Psychological Perspe
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5.4.2 Syntactic Methods for Theorem
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9.4.4 Realization of Fuzzy Inferenc
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Chapter 14: Machine Learning Using
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17.4.2.7 Fusing Multi-sensory Data
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22.4 Parallelism at Knowledge Repre
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1 Introduction to Artificial Intell
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instances of decision making [27],
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End. Begin state := new-states - ex
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(a) Generate and Test Approach: Thi
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this problem is useful for the foll
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disciplines. As a young discipline
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The bird and its attributes here ha
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[19] , Kosko [15] and Pedrycz [30]
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Reasoning in the presence of imprec
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where AI finds extensive applicatio
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M1 M2 M3 J2 J 1 8 5 J 2 J 1 2 8 J 2
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1.5.3 The Modern Period The modern
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A B AND C D E AND Y Fig. 1.12: A Pe
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improve reliability by realizing fr
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in AI problems, it supports massive
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[7] Dean, T., Allen, J. and Aloimon
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[34] Rajendra, C. and Chaudhury, D.
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2 The Psychological Perspective of
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The scope of realization of the pro
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2.2.3 Feature-based Approach for Pa
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From Sensory Registers Echoic Reg.
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cognitive memory and its utilizatio
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Tulving’s model bridges a potenti
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A " (a) (b) Fig. 2.6: The character
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his friend s facial image in memory
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sum need not be 180 degrees. Thus a
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same evening. Then he started medit
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construction of knowledge and its o
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saved in a magnetic media, which he
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set-point + error Controller Plant
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Cognitive science has emerged as a
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Further show that the Euclidean lea
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[4] Biswas, B., Mukherjee, A. K. an
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[28] Peterson, M. A., Kihlstrom, J.
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Production Systems, Logical Calculu
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against the data items of the WM. I
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Suppose the WM contains the data Bi
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To demonstrate the working principl
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i) Prefer rules for firing, where u
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In fig. 3.3, the antecedent clauses
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possible offspring states are gener
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Backward Reasoning: The backward re
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p q r s denotes joint premises t w
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3.11.1 Isolation of Knowledge and C
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state, as the predecessor states of
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[3] Buchanan, B. G. and Shortliffe,
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issues: first ‘what to search’
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We will now present two typical alg
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Further, the first state within the
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n1 n2 n3 n8 (a) (b) (c) (d) (e) (f
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The iterative deepening search thus
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legal next states will also lie on
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End. Then push those children into
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Procedure Best-First-Search Begin 1
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End. have multiple parents; Under t
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node. Under this circumstance, the
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6. Pn- Γ the set of paths from nod
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Therefore, f * (n’) = g* (n’) +
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0 +10 B 1+3 C +4 D +5 E F G 2+2 3+1
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The above algorithm considers two d
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Step 1: X (7) Step 2: Step 3: U X (
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(iv) If all of the nodes connected
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If the values are propagated up to
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ii) the alpha value of any MAX node
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αmin =2 βmax =1 C αmin =1 e(n) =
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eductions. Constraint satisfaction
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5 The Logic of Propositions and Pre
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5.2 Formal Definitions The followin
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Definition 5.7: A propositional for
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5.4.1 Semantic Method for Theorem P
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8. p \/ (q Λ ¬ q) ⇔ p 9. p Λ (
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) AND, OR Removal: If the L.H.S. co
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Since all the terminals of the tree
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p v q ¬ q v r p v r ¬ p v s r v s
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Let AS = {A1, A2,, …..,An} be the
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4. If P is a WFF and X is not a qua
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original expression: ( ¬ P11 ∨
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S: = S ∪ {variable or constant /
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¬ Loves (john, mary) ¬Child (X)
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which implies that if q is true the
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ii) C is a ground instance of C [9]
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) p→ (q→ r) ⇔ (p Λ q) → r
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[6] Leinweber, D., “Knowledge-bas
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of a ‘classroom scene’ interpre
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Sits-on (Y, bench, classhour), Pers
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Sits-on (mita, bench, classhour). 3
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Teacher (X) Sub-goal fails Person (
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Definition 6.5: A definite goal is
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Example 6.5, presented below, illus
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6.6.1 Risk of Using CUT It is to be
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In the SLD tree for the Taxpayer pr
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6.9 Fixed Points in Non-Horn Clause
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6.11 Conclusions The main advantage
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preferred X50 >= 2, default X100
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7.1 Introduction Predicate Logic is
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proposed this logic and called it n
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We now present a few modal properti
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Expert Reasoning System New data Dy
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PR5: ¬Visits (Wife, market, Monday
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IN (n1) + IN (n2) + (n11) retracts
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may hold (and not must hold). An ex
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The resulting stable consequences t
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In this example, we call ψ = Bird
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important factor in non-monotonic r
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a) Boy(X) ∧ ¬ L ¬ Likes (X, cho
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8 Structured Approach to Knowledge
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predicate boy (jim). This can be re
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Consequently, we represent the abov
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Is-a Is-a Is-a Protozoa Bacteria Is
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Example 8.2: This example illustrat
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A common question that may be raise
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∴ q is true. 2) Defeasible deriva
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Examination Hall Seat Arrangement I
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Petri nets [5]. It, however, does n
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Since p1 and p 2 contain tokens and
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For building dependency structures,
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The most significant drawback of CD
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Once a script structure for a given
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[6] Nilsson, N. J., Principles of A
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techniques, we first introduce the
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of precision of data and certainty
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where D and S stand for disease and
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To illustrate the reasoning process
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2. It might happen that none of the
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One interesting property of Bayesia
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We now compute the messages that no
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The main steps [8], [12] of the bel
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Updating a node X thus involves upd
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j ↓ i→ round oval-shaped and MB
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Formally, the set of all possible o
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The orthogonal summation of belief
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The orthogonal summation operations
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integers, then we can definitely sa
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where a / b in fast-runner subset r
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where the “o” denotes a fuzzy A
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This may be formally written as µ
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cumulative maximum of the two succe
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“If you can manage to evaluate th
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[7] Shenoy, P. P. and Shafer, G.,
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10.1 Introduction Databases associa
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a finite set of places, D= {d1, d2
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µfast runner(x) 0.9 0.6 0.2 0.1 5
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present in the consequent part; Aug
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Procedure Cycle-detection (P, Q, m,
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Procedure Put-transition (Newlistk,
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The trace of the algorithm cycle- d
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p1 p2 pm n1 n2 nm Fig. 10.4: A tran
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where the elements qw / ∈ {0, 1}
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Q' m and Rm matrices. It may be not
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Definition 10.9: An arc is called p
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The procedure forward reasoning is
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In the above example, A, B, and C a
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n V (q k j ∧r j k ) to be close t
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Let us assume for the sake of simpl
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It is evident from the above distri
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= R b m o ((P ' f m) T o Nf c (t+1)
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Since such choice of Rfm and Rbm sa
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one step of belief revision in the
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10.9 Conclusions The chapter presen
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References [1] Bugarin, A. J. and B
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[24] Looney, C. G., "Fuzzy Petri ne
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11 Reasoning with Space and Time Th
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The other one is an extension by ne
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touches another, the common points
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Procedure Move-optimal (R , Startin
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at Jadavpur University verified thi
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µ above(θ) =sin 2 θ , when -∏
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c c a b q r a b (b) (a) d d p q r p
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11.5 Temporal Reasoning by Situatio
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Holds( now, rain-ceased) Axiom (3)
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Interpretation I Interpretation II
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Example 11.3: Prove that Ā(p) ≡
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11.6.3 Some Elementary Proofs in PT
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The second rule is generally called
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11.9 Conclusions The chapter demons
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12 Intelligent Planning This chapte
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sets opposite to the TV. So they oc
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where On (X, Y) means the object X
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this, let us consider the last prob
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(B), we generate the old state 3, w
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12.3 Least Commitment Planning The
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To start solving the problem, we fi
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We now have three partially ordered
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12.4 Hierarchical Task Network Plan
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d=0 d=1 d=2 d=3 Fig. 12.16: A hiera
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J1 J2 M1 M2 M3 or Job schedules J2
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job k from time 0. For example, the
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3. Design a hierarchical plan for t
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trainer, who supplies the input-out
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Definition 13.1: An object is an en
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The restriction of hypotheses can b
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Positive instances: 1. Object (larg
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To illustrate how LEX performs inte
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alive dead Z=A/W 2 1 0 0 1 2 Living
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where Pi denotes the proportion of
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3. Apply Mapping Function: Apply th
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determine the animals because it do
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simplest form of reinforcement lear
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Let us now assume that the game is
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How can we compute the utility valu
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13.5 Learning by Inductive Logic Pr
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know f ? These questions can be ans
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|H| (1-ε) m ≤ δ ⇒ m ≥ (1/ε
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[7] Michalski, R. S., “A theory a
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However, most biologists are of the
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The most common non-linear function
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(d) b1 a1 (e) c1 cj cq bi ah Fig.14
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transverse diameter, etc. as the in
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x 1 x 2 x n Penultimate layer ∑+F
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Drawbacks of back-propagation algor
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x1 x2 xn 14.6 Widrow-Hoff's Multi-l
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x 1 x 2 x 1 x 2 The training proces
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• If not, select another ADALINE,
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ni 1 = '1' output ( firing ). Each
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designed the weights (W) of a singl
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AND z1 s11 OR w11 w12 w 1n v 11 v12
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− ~ w X k, l ∼ = ⎡ ⎤ ⎢
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V x ∑ w O j = Sgn ( j+ ji i + A
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neural nets in the fields mentioned
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where the first summation is over a
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circuit,” IEEE Trans. on Circuits
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their joint usage in many problems,
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1 0 1 0 11 0 1 0 1 0 1 1 0 10 1 1 1
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15.2 Deterministic Explanation of H
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Thus, in a total of N selections wi
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and population size = 2, which mean
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The behavior of GA without mutation
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a = ∑ Lt (π 0 P n ) i i=1 n→
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Eθ GA Program Z1, Z2, Z3, Z4, X1,
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e [W1 W2 W3 W4 W5 W6] T and [ G1 G2
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function. So, GA in each evolution
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f(x) = Sin (x) + [x * x + y] 1/2 ca
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(EQ (DU(MT CS) (NOT CS)) (DU(MS NN)
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[4] Chakraborty, U. K. and Muehlenb
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16 Realizing Cognition Using Fuzzy
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Cognitive maps are generally used f
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Theorem 16.1: With initial values o
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Here W T is a transposed weight mat
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example, for moving one’s arm, th
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and the weight adaptation equation
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Xk+1 ∆Wk = α Ek οXk+1 T / ((Xk+
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d5 d9 d1 d6 d7 d8 d10 d11 d1=Front
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16.10 Conclusions and Future Direct
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[6] Kosko, B., “Fuzzy cognitive m
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however, have a narrow spectrum of
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contaminated with it. However, for
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1. Move the mask over the image, so
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∇ 2 (g * f ) = ( ∇ 2 g ) * f. W
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It may be noted that segmenting an
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machine has to narrate the game onl
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schematic diagram, briefly outlinin
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same process for all clusters, we g
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Level 1 Level 2 Level 0 Neuron Fig.
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I (u, v) YI Fig. 17.12: Camera mode
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epipolar planes meet the image plan
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(a, b, 1) Fig. 17.15: A 3-D represe
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case III: Planes not parallel to th
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yi = -fi (xi,* ai - 1*) + ( fi / a
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Sample Execution: This is a program
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U t11 t12 t13 t14 x V = t21 t22 t23
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obtained after linearizing measurem
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co-ordinate systems. The correspond
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18 Linguistic Perception Building p
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psychological aspects of understand
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S NP VP ART N VP the man VP V NP ki
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( S ( NP ( ( ART the ) (N man ) ) (
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Sentence: NP VP 1 6 NP: ART N 2 ART
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18.2.3.1 Learning For adaptation of
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context sensitive grammar is thus m
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Generally, we start the sentence S
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VP: V NP S0 S1 Tagged procedure V:=
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Laugh agent animal enjoyer entity p
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18.5 Discourse and Pragmatic Analys
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Thus the elements of communicative
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19 Problem Solving by Constraint Sa
Page 576 and 577: The recognition problem is the pres
Page 578 and 579: CSP deals with two classical proble
Page 580 and 581: = 10 10 = 10 = * Fig. 19.2 (b): Pro
Page 582 and 583: + _ V1 R1 V 2 V3 I1 I2 I3 V R2 R3 S
Page 584 and 585: V = Now assuming the values of V, R
Page 586 and 587: On (A, Ta) ∧ On (B, Ta) ∧On (C,
Page 588 and 589: We now represent the neighboring re
Page 590 and 591: Step 1: (x
Page 592 and 593: such variable xj , xk, etc., attach
Page 594 and 595: of C3. If the constraints are liste
Page 596 and 597: The possible labels of the junction
Page 598 and 599: The main part of CSP is the formula
Page 600 and 601: 20 Acquisition of Knowledge Acquisi
Page 602 and 603: invite the experts to attend a meet
Page 604 and 605: B E A= test ?, B = test results, C
Page 606 and 607: The database in fig. 20.2 is extrac
Page 608 and 609: 20.5 Knowledge Refinement by Hebbia
Page 610 and 611: * * * n i ( t + 1) = n i ( t ) at t
Page 612 and 613: L(r,s) L(s,r) Y(r) Y(s) OS(r,s) n1
Page 614 and 615: The FPN of fig. 20.5 is formed with
Page 616 and 617: L(a,l) L(l,a) Y(a) Y(l) OS(a,l) n1
Page 618 and 619: [3] Cooke, N. and Macdonald, J.,
Page 620 and 621: 21 Validation, Verification and Mai
Page 622 and 623: Here the problem characteristic is
Page 624 and 625: The chapter will cover various issu
Page 628 and 629: Paired t-test: Suppose xi ∈ X and
Page 630 and 631: Ancestor P = {p1,p2} Tr = {tr1,tr2,
Page 632 and 633: 21.3.2 Inconsistencies in Knowledge
Page 634 and 635: q tr1 tr2 p1 p2 …………… pn
Page 636 and 637: A rule may also include a number of
Page 638 and 639: 21.4 Maintenance of Knowledge Based
Page 640 and 641: control of data and knowledge is un
Page 642 and 643: 8. Stefik, M., Introduction to Know
Page 644 and 645: are not suitable for AI application
Page 646 and 647: Because of non-determinism in the s
Page 648 and 649: Procedure Partial-Expansion Begin W
Page 650 and 651: Step: Fig.22.4: A tree expanded by
Page 652 and 653: Procedure Generous-Sharing Begin Fl
Page 654 and 655: To evaluate the performance of the
Page 656 and 657: Definition 22.2: If the antecedents
Page 658 and 659: a) AND -Parallelism Consider a logi
Page 660 and 661: However, given sufficient computing
Page 662 and 663: EPN = { P, Tr, D, f, m, A, a, I, o
Page 664 and 665: It is to be noted that if- then ope
Page 666 and 667: After the bindings of the variables
Page 668 and 669: F(x) ← A(X) , B(Y) (1) A(1) ← (
Page 670 and 671: the result produced by clause (2) m
Page 672 and 673: Else Flag:= true; End; Until no-of
Page 674 and 675: transition for the PTVVM. The PTVVM
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For the sake of convenience, the pi
Page 678 and 679:
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
Page 746 and 747:
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
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
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Predicate: LSR Argument1: S Argumen
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ANTECEDENT 1: Predicate: END conclu
Page 766 and 767:
Rules properly arranged MAIN MENU:
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Level POSITION_FROM_LEFT NODE 1 1 H
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calculating membership values enter
Page 772 and 773:
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
Page 778 and 779:
(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) (
Page 784 and 785:
Proof of theorem10.2: For an oscill
Page 786 and 787:
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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