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E-grāmata: Handbook on Reasoning-Based Intelligent Systems [World Scientific e-book]

Edited by (Univ Of South Australia, Australia), Edited by (Univ Of Hyogo, Japan)
  • Formāts: 680 pages
  • Izdošanas datums: 18-Mar-2013
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • ISBN-13: 9789814329484
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Handbook on Reasoning-Based Intelligent SystemsPalielināt
  • Formāts: 680 pages
  • Izdošanas datums: 18-Mar-2013
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • ISBN-13: 9789814329484
Citas grāmatas par šo tēmu:
World Scientific e-booksMore info about World Scientific e-books
Rokasgrāmatas mājas lapa: http://www.worldscientific.com/worldscibooks/10.1142/7994#t=toc
This book consists of various contributions in conjunction with the keywords “reasoning” and “intelligent systems”, which widely covers theoretical aspects to practical ones of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally.
Preface xxi
1 Advances in Intelligent Systems
1(30)
Lakhmi C. Jain
Kazumi Nakamatsu
1.1 Introduction
1(1)
1.2
Chapters Included in the Book
2(1)
1.3 Conclusion
3(1)
1.4 References
3(1)
1.5 Resources
4(27)
2 Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems
31(30)
Aruna Chakraborty
Amit Konar
Pavel Bhowmik
Atulya K. Nagar
2.1 Introduction
31(2)
2.2 Stable Points in Propositional Temporal Dynamics
33(3)
2.2.1 Stability of propositional temporal system using Lyapunov energy function
34(1)
2.2.1.1 The Lyapunov energy function
35(1)
2.2.1.2 Asymptotic stability analysis of the propositional temporal system
35(1)
2.3 Stability Analysis of Fuzzy Temporal Dynamics
36(2)
2.4 Reasoning with Fuzzy Cognitive Map
38(5)
2.5 Chaos and Limit Cycles in Emotion Based Cognitive Reasoning System
43(14)
2.5.1 Effect of parameter variation on the response of the cognitive dynamics of emotion
45(7)
2.5.2 Stability analysis of the proposed emotional dynamics by Lyapunov energy function
52(2)
2.5.3 A stabilization scheme for the mixed emotional dynamics
54(3)
2.6 Conclusions
57(4)
References
57(4)
3 Some Studies on Data Mining
61(20)
Dilip Kumar Pratihar
3.1 Introduction
61(2)
3.2 Classification Tools
63(1)
3.3 Statistical Regression Analysis
64(4)
3.3.1 Design of experiments
64(1)
3.3.1.1 Pull-factorial design of experiments
64(1)
3.3.1.2 Central composite design of experiments
65(1)
3.3.2 Regression analysis
66(1)
3.3.2.1 Linear regression analysis
67(1)
3.3.2.2 Non-linear regression analysis
67(1)
3.3.3 Adequacy of the model
67(1)
3.3.4 Drawbacks
67(1)
3.4 Dimensionality Reduction Techniques
68(7)
3.4.1 Sammon's Non-linear Mapping (Sammon, 1969)
68(1)
3.4.2 VISOR Algorithm (Konig, 1994)
69(2)
3.4.3 Self-organizing map (Kohenen, 1995)
71(1)
3.4.4 GA-like approach (Dutta and Pratihar, 2006)
72(1)
3.4.5 Comparisons
73(2)
3.4.6 Dimensionality reduction approaches for large data sets
75(1)
3.5 Clustering Techniques
75(4)
3.5.1 Fuzzy C-means algorithm (Bezdek, 1973)
75(1)
3.5.2 Entropy-based fuzzy clustering (Yao et al, 2000)
76(1)
3.5.3 Comparisons
77(1)
3.5.4 Clustering of large spatial data sets
78(1)
3.6 Cluster-wise Regression Analysis
79(1)
3.7 Intelligent Data Mining
79(1)
3.8 Summary
79(2)
Acknowledgement
80(1)
References
80(1)
4 Rough Non-deterministic Information Analysis for Uncertain Information
81(38)
Hiroshi Sakai
Hitomi Okuma
Mao Wu
Michinori Nakata
4.1 Introduction
81(1)
4.2 An Overview of RNIA
82(7)
4.2.1 Basic Definitions
83(1)
4.2.2 Two Modalities in RNIA
84(1)
4.2.3 Properties and Obtained Results in RNIA
85(4)
4.3 Issue 1: Rule Generation on the Basis of the Consistency in NISs (Certain and Possible Rule Generation)
89(6)
4.3.1 Certain Rule Generation by the Order of Attributes
89(2)
4.3.2 Minimal Certain Rules
91(1)
4.3.3 Discernibility Functions and Minimal Certain Rule Generation
91(2)
4.3.4 Enumeration Method for Obtaining Minimal Solutions
93(1)
4.3.5 Interactive Selection Method for Obtaining Minimal Solutions
93(1)
4.3.6 Interactive Selection and Enumeration Method with a Threshold Value for Obtaining Minimal Solutions
94(1)
4.3.7 Programs for ISETV-method
94(1)
4.3.8 Possible Rule Generation
95(1)
4.4 Issue 2: Rule Generation on the Basis of the Criterion Values in NISs
95(7)
4.4.1 Some Definitions and the Second Issue
95(1)
4.4.2 Definitions of descinf and descsup Instead of inf and sup
96(1)
4.4.3 Possible Implication and Minsupp, Minacc Values
97(1)
4.4.4 Possible Implications and Maxsupp, Maxacc Values
98(1)
4.4.5 An Example of Rule Generation on the Basis of the Criterion Values
98(2)
4.4.6 Algorithms for Rule Generation on the Basis of the Criterion Values
100(1)
4.4.7 An Attempt of Applying Utility Programs to Data in UCI Machine Learning Repository
101(1)
4.5 Issue 3: Rule Generation in Tables with Numerical Values
102(5)
4.5.1 An Exemplary Data with Numerical Values
102(1)
4.5.2 A Proposal of Meaningful Figures in Numerical Values
103(1)
4.5.3 Numerical Patterns and Equivalence Relations
103(2)
4.5.4 Rule Generation in Numerical Data
105(1)
4.5.5 An Application of Utility Programs
106(1)
4.5.6 Comparison with Previous Research Results
106(1)
4.6 Concluding Remarks
107(12)
Acknowledgements
107(1)
References
107(12)
5 Metamathematical Limits to Computation
119(24)
N. C. A. da Costa
F. A. Doria
5.1 Prologue
119(3)
5.2 Preliminary Results
122(5)
5.3 More Comments About Undecidability and Incompleteness in Strong Theories
127(1)
5.4 An Axiomatization for (Theoretical) Computer Science
128(4)
5.5 Can We Handle Arbitrary Infinite Sets of Poly Machines in ZFC?
132(1)
5.6 More Examples of Incompleteness for Computer Science in S
133(2)
5.7 Function F and Function G
135(2)
5.8 The P vs. NP Question
137(6)
Acknowledgments
139(1)
References
140(3)
6 Hypothesis Refinement: Building Hypotheses in an Intelligent Agent System
143(36)
Gauvain Bourgne
Nicolas Maudet
Suzanne Pinson
6.1 Introduction
143(1)
6.2 Hypothesis Refinement Problem
144(11)
6.2.1 Knowledge representation
144(3)
6.2.2 Consistency relation
147(1)
6.2.2.1 Consistency of an hypothesis
147(1)
6.2.2.2 Group Consistency
148(2)
6.2.2.3 Equivalence and homogeneity
150(1)
6.2.3 Internal hypothesis formation
150(1)
6.2.3.1 Full Determinism
151(1)
6.2.3.2 Individualism
152(1)
6.2.4 Assumptions
152(1)
6.2.4.1 Consistent world
153(1)
6.2.4.2 Assumptions on agents
153(1)
6.2.4.3 Compositionality of the consistency relation
153(1)
6.2.4.4 Assumptions on observations
153(1)
6.2.5 Problem description
154(1)
6.2.5.1 Reasoning
154(1)
6.2.5.2 Homogeneity vs heterogeneity
154(1)
6.2.5.3 Communicational constraints
155(1)
6.2.5.4 Dynamicity
155(1)
6.3 Learner/Critic Revision Mechanisms
155(8)
6.3.1 Revision mechanisms and protocols
156(1)
6.3.2 Local communication protocols
157(1)
6.3.2.1 Unilateral hypothesis exchange
157(2)
6.3.2.2 Bilateral hypothesis exchange
159(1)
6.3.3 From local to global
160(1)
6.3.3.1 Static links: full propagation
160(1)
6.3.3.2 Rumor-like propagation
160(1)
6.3.4 Complete global communication protocols
161(1)
6.3.4.1 Clock-wise hypothesis exchange for fully connected societies
161(1)
6.3.4.2 Heterogeneous variants
162(1)
6.3.4.3 Revision mechanism with propagation
162(1)
6.4 Instantiating the Framework
163(8)
6.4.1 Reasoning and representation
163(1)
6.4.1.1 Logical abduction
164(1)
6.4.1.2 Cover-set abduction
165(1)
6.4.1.3 Inductive incremental learning
165(1)
6.4.2 Application level
166(1)
6.4.2.1 Semantic specification
166(1)
6.4.2.2 Other considerations
166(1)
6.4.2.3 Instance of a problem
167(1)
6.4.3 Example application: Fire simulation
167(1)
6.4.3.1 Description
167(1)
6.4.3.2 Syntaxical instantiation
168(1)
6.4.3.3 Semantical instantiation
168(1)
6.4.3.4 Other considerations
169(1)
6.4.3.5 Problem instances
170(1)
6.4.3.6 A word on experimentations
171(1)
6.5 Related Works
171(4)
6.5.1 Distributed abduction
172(1)
6.5.2 Distributed inductive learning
173(1)
6.5.3 Other type of distributed hypothetical reasoning
174(1)
6.5.3.1 Distributed consequence finding
174(1)
6.5.3.2 Distributed diagnosis
174(1)
6.5.4 Consensus in (dynamic) networks
175(1)
6.6 Conclusion
175(4)
References
176(3)
7 A Heuristic Algorithmic Procedure to Solve Allocation Problems with Fuzzy Evaluations
179(10)
R. Bartholo
C. A. N. Cosenza
F. A. Doria
M. R. Doria
7.1 Introduction
179(1)
7.2 Sketch of the Technique
180(1)
7.3 Main Concepts
181(3)
7.4 The Proposed Algorithm
184(1)
7.5 Example: the Brazilian Biodiesel Program
185(1)
7.6 Example: Diagnosing Temporal Lobe Epilepsy
185(1)
7.7 Example: Groundwater Vulnerability
186(1)
7.8 Comments
187(2)
Acknowledgements
187(1)
References
188(1)
8 Non-Classical Logics and Intelligent Systems
189(18)
Seiki Akama
8.1 Introduction
189(1)
8.2 Non-Classical Logics
190(1)
8.3 Modal Logic
191(3)
8.4 Intuitionistic Logic
194(1)
8.5 Many-Valued Logic
195(5)
8.6 Paraconsistent Logic
200(2)
8.7 How to Use Non-Classical Logics
202(1)
8.8 Conclusions
203(4)
References
203(4)
9 A Paraconsistent Annotated Logic Program Before-after EVALPSN and its Application
207(36)
Kazumi Nakamatsu
Jair Minoro Abe
9.1 Introduction and Background
207(2)
9.2 Paraconsistent Annotated Logic Program
209(5)
9.2.1 Paraconsistent Annotated Logic PT
209(2)
9.2.2 EVALPSN(Extended Vector Annotated Logic Program with Strong Negation)
211(3)
9.3 Before-after EVALPSN
214(7)
9.4 Reasoning System in Bf-EVALPSN
221(13)
9.4.1 Examples of Bf-relation Reasoning
221(2)
9.4.2 Basic Before-after Inference Rule
223(4)
9.4.3 Transitive Before-after Inference Rule
227(6)
9.4.4 Example of Transitive Bf-relation Reasoning
233(1)
9.5 Application of Bf-EVALPSN to Process Order Verification
234(5)
9.6 Conclusion and Remark
239(4)
References
239(4)
10 Inspecting and Preferring Abductive Models
243(32)
Luis Moniz Pereira
Pierangelo Dell'Acqua
Alexandre Miguel Pinto
Goncalo Lopes
10.1 Introduction
243(1)
10.2 Abductive Framework
244(5)
10.2.1 Basic Abductive Language
244(1)
10.2.1.1 Hypotheses Generation
245(1)
10.2.1.2 Enforced Abduction
246(1)
10.2.1.3 Conditional Abduction
246(1)
10.2.1.4 Cardinality Constrained Abduction
247(1)
10.2.2 Declarative Semantics
247(2)
10.3 Pragmatics
249(6)
10.3.1 Constraining Abduction
249(1)
10.3.2 Preferring Abducibles
250(1)
10.3.3 Abducible Sets
251(1)
10.3.4 Modeling Inspection Points
252(3)
10.4 Procedural Semantics
255(6)
10.4.1 Framework
255(1)
10.4.2 Program Transformation
256(2)
10.4.3 Properties
258(3)
10.5 A Posteriori Preferences
261(6)
10.5.1 The consequences of abduction
261(1)
10.5.2 Utility Theory
262(2)
10.5.3 Oracles
264(3)
10.6 Sophie's Choice
267(1)
10.7 Implementation
268(4)
10.7.1 XSB-XASP Interface
268(2)
10.7.2 Top-Down Proof Procedure
270(1)
10.7.3 Computation of Abductive Stable Models
270(1)
10.7.4 Inspection Points
271(1)
10.7.5 A Posteriori Choice Mechanisms
271(1)
10.8 Conclusions
272(3)
References
273(2)
11 Supervised Neural Network Learning: from Vectors to Graphs
275(32)
Monica Bianchini
Marco Maggini
Lorenzo Sarti
11.1 Introduction
275(2)
11.2 Neural Network Models
277(8)
11.2.1 Input data types
278(1)
11.2.2 The neural network N
278(7)
11.3 Learning with Neural Networks
285(9)
11.4 Processing Graphs: Application Domains
294(13)
References
303(4)
12 Paraconsistent Artificial Neural Networks and Applications
307(24)
Jair Minoro Abe
Kazumi Nakamatsu
12.1 Introduction
307(1)
12.2 Background
308(1)
12.3 The Paraconsistent Artificial Neural Cells
308(3)
12.4 The Paraconsistent Artificial Neural Cell of Learning
311(1)
12.5 The Learning of a PANC-1
311(1)
12.6 Unlearning of a PANC-1
312(1)
12.7 Operating PANN
312(3)
12.8 Why PANN Can be Useful
315(1)
12.9 Methodology
316(1)
12.10 Data analysis, Expert System, and Wave Morphology
317(5)
12.11 PANN and Speech Recognition
322(1)
12.12 PANN and Craniofacial Variables
323(5)
12.13 Conclusions
328(3)
References
328(3)
13 Paraconsistent Annotated Evidential Logic ET and Applications in Automation and Robotics
331(22)
Jair Minoro Abe
Kazumi Nakamatsu
13.1 Introduction
331(1)
13.2 Paraconsistent Annotated Logics
332(1)
13.3 Paraconsistent Annotated Evidential Logic Et
333(2)
13.4 The Paraconsistent Logical Controller --- Paracontrol
335(1)
13.5 The Autonomous Mobile Robot Emmy
335(3)
13.6 Robot Emmy II
338(6)
13.7 Autonomous Mobile Robot Emmy III
344(2)
13.8 Paraconsistent Autonomous Mobile Robot Hephaestus
346(4)
13.9 Keller --- Electronic Device for Blind and/or Deaf People Locomotion
350(1)
13.10 Conclusions
351(2)
References
351(2)
14 Adaptive Intelligent Learning System for Online Learning Environments
353(36)
Fatma Cemile Serce
Ferda Nur Alpaslan
Lakhmi C. Jain
14.1 Introduction
353(2)
14.2 Agents
355(1)
14.3 AILS: Adaptive Intelligent Learning System
356(7)
14.3.1 Components of AILS
356(1)
14.3.2 The Architecture
357(1)
14.3.3 The Agents
358(1)
14.3.3.1 The Agent's Roles
359(2)
14.3.3.2 Interactions among Agents
361(1)
14.3.3.3 Services associated with Agent Roles
362(1)
14.3.3.4 The Acquaintances of Agents
363(1)
14.4 Implementation
363(14)
14.4.1 Learner Modeling
364(1)
14.4.1.1 Behavioral Factors
364(1)
14.4.1.2 Knowledge Factors
365(1)
14.4.1.3 Personality Factors
365(2)
14.4.2 Agent Behaviors: Scenarios
367(1)
14.4.2.1 Login Operation
367(1)
14.4.2.2 View Lecture Notes
367(1)
14.4.2.3 Search Keywords
368(2)
14.4.3 The AILS Ontologies
370(1)
14.4.4 The AILS Adaptation Strategies
371(2)
14.4.4.1 Content Adaptation
373(1)
14.4.4.2 Presentation Adaptation
373(1)
14.4.4.3 Participation Adaptation
374(1)
14.4.4.4 Perspective Adaptation
375(1)
14.4.5 AILS-LMS Interface
375(2)
14.5 A Sample Session of AILS
377(3)
14.5.1 Login Tool
379(1)
14.5.2 Search Tool
379(1)
14.5.3 Lecture Notes Tool
380(1)
14.6 Conclusion
380(2)
Acknowledgement
380(1)
References
381(1)
14.7 Resources
382(7)
15 Automatic Test Program Generation: How Artificial Evolution may Outperform Experience
389(44)
Danilo Ravotto
Ernesto Sanchez
Giovanni Squillero
15.1 Introduction
389(2)
15.2 Background
391(2)
15.2.1 Verification, Validation and Test methodologies
391(2)
15.3 Test Program Generation for Microprocessor Validation
393(22)
15.3.1 Motivation
393(2)
15.3.2 Background
395(1)
15.3.2.1 Design Validation
395(1)
15.3.2.2 Basics on OpenSPARC processor cores
396(1)
15.3.3 Proposed Approach
396(3)
15.3.3.1 The feedback-based generation algorithm
399(1)
15.3.3.2 New multithread-oriented features
400(1)
15.3.4 Case Study 1 --- The OpenSPARC T2 processor
401(1)
15.3.4.1 OpenSPARC T2
401(1)
15.3.4.2 Module Selection
402(2)
15.3.4.3 Metric Selection
404(1)
15.3.5 Evolutionary tool
404(1)
15.3.5.1 Constraints
405(1)
15.3.5.2 Fitness
405(1)
15.3.5.3 Evolutionary Scheme
405(1)
15.3.5.4 Test program generation environment
406(1)
15.3.5.5 Experimental results
406(2)
15.3.5.6 Covered Corner case
408(1)
15.3.6 Case Study 2 --- The OpenSPARC T1 processor
408(1)
15.3.6.1 OpenSPARC T1
408(1)
15.3.6.2 Module and metric selection
409(1)
15.3.6.3 Experimental results
409(1)
15.3.7 Case Study 3 --- The OpenSPARC T1 processor with hardware acceleration
410(1)
15.3.7.1 Evaluation Environment
410(1)
15.3.7.2 FPGA board
411(1)
15.3.7.3 Evolutionary tool
412(1)
15.3.7.4 Internal information gathering scheme
412(1)
15.3.7.5 Experimental results
413(2)
15.4 Test of peripheral cores in SoCs
415(14)
15.4.1 Motivation
415(1)
15.4.2 System-on-Chip Architecture
416(2)
15.4.3 Previous Work and Communication Peripherals Test Challenges
418(4)
15.4.4 Proposed Test Program Generation Methodology for Communication Peripherals
422(2)
15.4.4.1 Test block for configuration modes
424(1)
15.4.4.2 Test block for FIFOs testing
424(1)
15.4.4.3 Error Handling Activation
425(1)
15.4.4.4 Bus Interface Logic Testing
425(1)
15.4.5 Evolutionary Tool
426(1)
15.4.6 Experimental Evaluation
426(3)
15.5 Conclusions
429(4)
References
430(3)
16 Discovery of Communications Patterns by the Use of Intelligent Reasoning
433(34)
J. Fulcher
M. Zhang
Q. Bai
F. Ren
16.1 Data Ming and Knowledge Discovery in Databases
433(2)
16.1.1 Communications Data
435(1)
16.2 Social Network Analysis
435(1)
16.3 Intelligent Reasoning Methods
436(2)
16.3.1 Link Mining
436(1)
16.3.2 Software Agents
437(1)
16.3.3 Swarms
437(1)
16.3.4 Artificial Neural Networks
438(1)
16.4 Multi-Agent System (MAS) Network Model
438(3)
16.5 WetShow Software
441(7)
16.5.1 WetShow 2.0 Software
442(1)
16.5.2 Network Visualization
443(2)
16.5.3 Pattern Discovery from Contact Lists
445(3)
16.5.4 Familiarity Analysis
448(1)
16.6 Communications Analysis
448(8)
16.6.1 First Communications Data Set
448(4)
16.6.2 Second Communications Data Set
452(4)
16.7 Network Dynamics
456(3)
16.7.1 Adding Meaningful Link and Path Weights to a Transaction Network
456(2)
16.7.2 Building SWARM Simulations to Display Network Dynamics
458(1)
16.8 Public Domain Data
459(2)
16.9 Conclusion and Suggestions for Further Work
461(6)
Acknowledgment
462(1)
References
462(5)
17 Adaptive Approach to Quality Enhancement and Storage of Signatures and Fingerprint Images
467(28)
Roumen Kountchev
17.1 Introduction
467(2)
17.2 Image Histogram Modification
469(6)
17.3 Image Filtration and Segmentation
475(5)
17.3.1 Adaptive noise filtration
475(2)
17.3.2 Equalization of the image background illumination
477(2)
17.3.3 Image segmentation
479(1)
17.4 Lossless Compression of Biometric Images
480(2)
17.5 Experimental Results
482(10)
17.5.1 Histogram modification and segmentation
482(6)
17.5.2 Comparison to other similar techniques
488(1)
17.5.3 Lossless compression
488(4)
17.6 Conclusion
492(3)
Acknowledgement
493(1)
References
493(2)
18 Knowledge Representation for Electronic Circuits in Logic Programming
495(30)
Takushi Tanaka
18.1 Introduction
495(1)
18.2 Circuit Representation in Prolog
496(7)
18.2.1 Facts
496(1)
18.2.2 Goal
497(1)
18.2.3 Rules
498(1)
18.2.4 Predicates for circuit structures
499(2)
18.2.5 Difficulties in circuit representation using predicates
501(1)
18.2.6 Changing circuit representation
502(1)
18.2.7 Lists
503(1)
18.3 Logic Grammar DCSG
503(5)
18.3.1 Word-order free language
503(1)
18.3.2 DCSG conversion
504(1)
18.3.3 Backward chaining and top down parsing
505(1)
18.3.4 The looping problem
505(2)
18.3.5 Solution of the looping problem
507(1)
18.4 Finding Structures in Circuits
508(3)
18.4.1 Circuits represented as sentences
508(1)
18.4.2 Grammar rules without recursion
508(1)
18.4.3 All elements connected to a node
509(1)
18.4.4 Paths and loops
509(2)
18.5 Circuit Grammar for Knowledge Representation
511(2)
18.5.1 Semantic field in left-hand side
511(1)
18.5.2 Semantic field in right-hand side
512(1)
18.5.3 Terminal symbols with semantic fields
512(1)
18.5.4 English interface for semantic term
512(1)
18.6 Grammar Rules
513(7)
18.6.1 Circuits as Functional Blocks
513(1)
18.6.2 Terminal Symbols
514(2)
18.6.3 Non-Terminal Symbols
516(4)
18.7 Parsing Circuits
520(1)
18.8 Functional Explanations in English
521(1)
18.9 Conclusions
522(3)
References
523(2)
19 An Intelligent CBR Model for Predicting Changes in Tropical Cyclones Intensities
525(30)
James N.K. Liu
Simon C.K. Shiu
Jane You
Leon S.K. Law
19.1 Introduction
525(1)
19.2 Categories of Tropical Cyclones
526(5)
19.2.1 Classic Moving Track Patterns in the North-Western Pacific Ocean
527(3)
19.2.2 TC Best Tracks
530(1)
19.3 Case Selection and Experimental Data Sets
531(2)
19.4 Design of the Intelligent CBR Intensity Prediction Model
533(7)
19.4.1 The Case-Based Reasoning (CBR) Cycle
533(1)
19.4.2 Data pre-processing
533(3)
19.4.3 Case base building and data mining
536(2)
19.4.4 Checking the accuracy of exported rules from data mining and adjustments
538(2)
19.5 Experimental Results
540(11)
19.5.1 Accuracy of the three location groups
540(3)
19.5.2 Effectiveness of the location groups adjustment
543(1)
19.5.3 Data Analysis and Discussion
544(1)
19.5.3.1 2002 Best track data
544(1)
19.5.3.2 2003 Best track data
545(2)
19.5.3.3 2004 Best track data
547(1)
19.5.3.4 2005 Best track data
548(1)
19.5.3.5 2006 Best track data
549(1)
19.5.4 Comparisons with other models
550(1)
19.6 Conclusion and Future Work
551(4)
Acknowledgement
552(1)
References
552(3)
20 Analysis of Sequential Data in Tool Manufacturing of Volkswagen AG
555(20)
Kemal Ince
Thomas Schneider
Frank Klawonn
20.1 Introduction
555(2)
20.1.1 Knowledge discovery and data mining
555(1)
20.1.2 The application area
556(1)
20.2 The Work Sequence in the Components-Toolshop
557(5)
20.2.1 NC, CNC, DNC and how it works
558(1)
20.2.2 Components in manufacturing
558(2)
20.2.3 Sequences of operations
560(2)
20.3 Data Preprocessing
562(4)
20.3.1 Step 1: Standardization of the domain
562(2)
20.3.2 Step 2: Selection of the data set
564(1)
20.3.3 Step 3: Data structure of the data set to be analysed
565(1)
20.4 Analysis of Sequences
566(7)
20.4.1 The probabilistic state machine
567(1)
20.4.2 Building the model
568(1)
20.4.2.1 The predecessor and the prepredecessor sequence state
568(1)
20.4.2.2 The probability matrix
568(3)
20.4.3 Verifying the model
571(1)
20.4.4 Generating Rules
572(1)
20.5 Conclusions and Outlook
573(2)
References
573(2)
21 Reasoning-Based Artificial Agents in Agent-Based Computational Economics
575(28)
Shu-Heng Chen
21.1 Introduction
575(3)
21.2 Zero-Intelligence Agents
578(1)
21.3 Generalized Reinforcement Learning
579(5)
21.3.1 Reinforcement Learning
580(1)
21.3.2 Belief Learning
581(2)
21.3.3 Cognitive Capability of Generalized Reinforcement Learning
583(1)
21.4 Level-k Reasoning and Sophisticated EWA
584(4)
21.4.1 Beauty Contest Games
584(1)
21.4.2 Level-k Reasoning
585(1)
21.4.3 Sophisticated EWA Learning
585(1)
21.4.4 Agents with Incremental Cognitive Capacity
586(1)
21.4.5 Cognitive Heterogeneity of Agents
587(1)
21.5 Artificial Financial Agents
588(3)
21.5.1 Regime-Switching Agents
589(1)
21.5.2 Cognitive Capacity of Regime-Switching Agents
589(1)
21.5.3 Intelligence Quotients of Intelligent Algorithms
590(1)
21.6 Novelties-Discovering Agents
591(5)
21.6.1 Origin: Tournament Automation
591(1)
21.6.2 Outsmarting Opponents
592(2)
21.6.3 Cognitive Capacity Hypothesis
594(1)
21.6.4 Novelties-Discovering Agents with Cognitive Capacity
594(2)
21.7 Concluding Remarks
596(7)
Acknowledgements
596(2)
References
598(5)
22 Reasoning and Knowledge Acquisition from Medical Database using Lattice SOM and Tree Structure SOM
603(30)
Takumi Ichimura
Takashi Yamaguchi
Kenneth James Mackin
22.1 Introduction
603(2)
22.2 Planar Lattice Neural Networks
605(8)
22.2.1 An overview of Planar Lattice Neural Network
605(4)
22.2.2 Neuron generation/elimination
609(1)
22.2.2.1 Neuron generation
609(2)
22.2.2.2 Neuron elimination
611(1)
22.2.2.3 Neuron generation/elimination in PLNN
612(1)
22.3 Tree Structured SOM
613(3)
22.4 Adaptive Learning Algorithm in TS-SOM
616(3)
22.4.1 NN structure adaptation
616(2)
22.4.2 Gaussian type neighborhood learning model
618(1)
22.5 Adaptive Tree Structured Clustering
619(4)
22.5.1 AHCA using SOM
621(1)
22.5.2 Node generation
622(1)
22.5.3 Re-clustering
622(1)
22.6 Coronary Heart Disease Database [ Suka et al. (2004)]
623(3)
22.6.1 An overview of Framingham Heart Study
623(1)
22.6.2 Six-year follow-up experience
623(1)
22.6.3 Database design
624(2)
22.7 Experimental Results
626(4)
22.7.1 Benchmark tests
626(1)
22.7.1.1 Iris data set
626(1)
22.7.1.2 Wine data set
627(1)
22.7.2 Experimental results for benchmark test
627(2)
22.7.3 Classification and knowledge in TS-SOM
629(1)
22.8 Conclusive Discussion
630(3)
References
631(2)
23 Approximate Processing in Medical Diagnosis by Means of Deductive Agents
633(22)
G. Fenza
D. Fumo
V. Lona
S. Senators
23.1 Introduction
633(1)
23.2 Related Works
634(1)
23.3 Software Development Model
635(8)
23.3.1 Medical Context Analysis
637(1)
23.3.1.1 The Medical Diseases Ontologies
637(1)
23.3.2 Medical Knowledge Extraction
638(1)
23.3.2.1 Fuzzy Clinical Data Analysis
639(1)
23.3.2.2 Knowledge Extraction Implementation
640(1)
23.3.3 Knowledge Usage
641(1)
23.3.3.1 Dynamic Fuzzy Control Design
641(2)
23.4 Distributed Medical Diagnosis (SOA)
643(5)
23.4.1 Medical Diagnosis Services
644(2)
23.4.2 Medical Diagnosis Agents
646(1)
23.4.3 Medical Diagnosis Service Register Agent
646(1)
23.4.4 Workflow of the system architecture
647(1)
23.5 Further Remarks on the Cases Study
648(6)
23.5.1 Additional Results
653(1)
23.6 Conclusions
654(1)
Acknowledgment 655(1)
References 655
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