Modularity in the design and implementation of expert systems relies upon cooperation among the expert systems and communication of knowledge between them. A prerequisite for an effective modular approach is some standard for knowledge representation to be used by the developers of the different modules. In this work we present a schema for knowledge representation, and apply this schema in the design of a rule-based expert system. We also implement a cooperative expert system using the proposed knowledge representation method. / A knowledge representation schema is a formal specification of the internal, conceptual, and external components of a knowledge base, each specified in a separate schema. The internal schema defines the structure of a knowledge base, the conceptual schema defines the concepts, and the external schema formalizes the pragmatics of a knowledge base. The schema is the basis for standardizing knowledge representation systems and it is used in the various phases of design and specification of the knowledge base. Its main tasks are to govern the interface and communication of knowledge between expert systems as well as to support a modular approach in the design of a cooperative expert system. The schema is also used in the stages of testing, validation, and maintenance of a knowledge base. / The conceptual schema is the formal specifications of the domain-dependent semantics and can be implemented using fuzzy semantics. For this purpose, an axiomatic theory of fuzzy semantics is developed and formal methods of specification of concepts using fuzzy semantics are proposed. / A new model of knowledge representation based on a pattern recognition interpretation of implications is developed. This model implements the concept of linguistic variables and can, therefore, emulate human reasoning with linguistic imprecision. / The test case for the proposed schema of knowledge representation is a system for computer-aided design of a man-machine interface. The core of the system is a cooperative expert system composed of two expert systems. This system applies a pattern recognition interpretation of a generalized one-variable implication with linguistic variables. / A process of validation of the system is performed, including testing of the system and verification that the system is acyclic, consistent, and in compliance with its specifications. / Source: Dissertation Abstracts International, Volume: 50-08, Section: B, page: 3580. / Major Professor: Abraham Kandel. / Thesis (Ph.D.)--The Florida State University, 1989.

The process of redistricting involves the division of a land surface into two or more pieces. In a political setting, the districts thus formed provide groups of voters that elect the same public officials. Other types of redistricting applications include the formation of school districts, transportation districts, or water management districts. / In this work we propose a knowledge-based expert system prototype as a solution to the redistricting problem. A number of key issues are addressed by the solution, including equality of population, contiguity, and graphical display of possible districts. In addition, we explore the need for dynamic user interaction within knowledge-based systems and outline a method (the Grouper approach) for dramatically reducing the complexity of the redistricting problem by restricting activity to a specific level of detail. / The prototype solution, a PC-based system implemented using Tecknowledge's M.1 expert system shell, is described in depth with particular emphasis on techniques for minimizing search. An annotated Grouper session is included, as are listings of the knowledge base and supporting C functions. Lastly, there is a discussion of the far-reaching significance of the redistricting problem and promising uses or extensions of the Grouper system in this regard. / Source: Dissertation Abstracts International, Volume: 51-12, Section: B, page: 5975. / Major Professor: Abe Kandel. / Thesis (Ph.D.)--The Florida State University, 1990.

A theory of mind or intelligence that derives from elements of philosophical, biological, linguistic, and psychological thought as well as of physics and information theory is presented. The hypothesis is defended that intelligence is not a thing but a composite of activities and attributes that must be described in terms of the evolution and interactions of systems, emerging into the environments in which they are embedded. It is proposed that a machine intelligence that emulates human intelligence must conform to certain restrictions that derive from accepting this hypothesis. In particular an implication is that for machine intelligence to be accepted as human-like intelligence it must be produced by a machine that functions in a manner substantially similar to a man and that interacts, grows or learns in and with an environment similar to that in which a man grows and learns. It is proposed that it should be possible to create machines and programs capable of this and that they can achieve intelligence with a large, but not arbitrarily large, degree of human-like characteristics. One system (of many possible systems), in development, based on grammars, and that satisfies some of those requirements is described. It consists of an artificial environment in which a grammar like program based on augmented transition networks, interacts with a human teacher. The purpose of KARA is to learn about the environment by being told and by imitation. / Source: Dissertation Abstracts International, Volume: 50-12, Section: B, page: 5732. / Major Professor: Lois Hawkes. / Thesis (Ph.D.)--The Florida State University, 1989.

A general method is described for translating an expert system into a functionally equivalent neural network. The neural network may be retranslated back into a functionally equivalent expert system. An example of the translation methodology is applied to the expert system shell M.1. The primary motivation for the development of the translation process is to aid in knowledge acquisition, however, there may be other applications such as the migration of a finished expert system to a neural network chip. The expert system and neural network knowledge base may be modified by either traditional knowledge engineering methods, associative learning techniques, or a synthesis of the two methods. Two learning algorithms are described that are appropriate for learning in the hybrid system--a modified backpropagation method and Goal-Directed Monte Carlo learning. These methods are used to demonstrate the repair of damaged certainty factors in several small rule bases. / Source: Dissertation Abstracts International, Volume: 52-03, Section: B, page: 1547. / Major Professor: R. C. Lacher. / Thesis (Ph.D.)--The Florida State University, 1991.

Verification and validation are essential to providing quality assurance for expert systems. The same level of quality assurance is expected of expert systems as has been demanded in conventional software. Verification and validation provide a systematic, comprehensive approach to quality assurance for conventional software. Such quality assurance is lacking in the development of expert systems. Current practice in verification and validation consists of the informal, haphazard testing of a few test cases. A lesson from the software crisis of conventional software is that quality can not be measured by such testing. Therefore, a systematic, comprehensive approach to verification and validation is necessary if quality assurance is to be provided for expert systems. / A methodology for the verification and validation of rule-based expert systems is developed in this research. The research objectives are to define verification and validation for expert systems, to delineate the activities required for thorough verification and validation of rule-based expert systems, and to develop a comprehensive set of techniques and tools for validation of rule-based expert systems. To this end, the contributions of this research are the recommendations for a systematic, comprehensive approach to the verification and validation of rule-based expert systems and the development of a complete set of techniques and tools for effective validation of rule-based expert systems. / Source: Dissertation Abstracts International, Volume: 52-03, Section: B, page: 1557. / Major Professor: Abraham Kandel. / Thesis (Ph.D.)--The Florida State University, 1991.

Siu Fai Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 79-83). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- (Weighted) Constraint Satisfaction --- p.1 / Chapter 1.2 --- Set Variables --- p.2 / Chapter 1.3 --- Motivations and Goals --- p.3 / Chapter 1.4 --- Overview of the Thesis --- p.4 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Constraint Satisfaction Problems --- p.6 / Chapter 2.1.1 --- Backtracking Tree Search --- p.8 / Chapter 2.1.2 --- Consistency Notions --- p.10 / Chapter 2.2 --- Weighted Constraint Satisfaction Problems --- p.14 / Chapter 2.2.1 --- Branch and Bound Search --- p.17 / Chapter 2.2.2 --- Consistency Notions --- p.19 / Chapter 2.3 --- Classical CSPs with Set Variables --- p.23 / Chapter 2.3.1 --- Set Variables and Set Domains --- p.24 / Chapter 2.3.2 --- Set Constraints --- p.24 / Chapter 2.3.3 --- Searching with Set Variables --- p.26 / Chapter 2.3.4 --- Set Bounds Consistency --- p.27 / Chapter 3 --- Weighted Constraint Satisfaction with Set Variables --- p.30 / Chapter 3.1 --- Set Variables --- p.30 / Chapter 3.2 --- Set Domains --- p.31 / Chapter 3.3 --- Set Constraints --- p.31 / Chapter 3.3.1 --- Zero-arity Constraint --- p.33 / Chapter 3.3.2 --- Unary Constraints --- p.33 / Chapter 3.3.3 --- Binary Constraints --- p.36 / Chapter 3.3.4 --- Ternary Constraints --- p.36 / Chapter 3.3.5 --- Cardinality Constraints --- p.37 / Chapter 3.4 --- Characteristics --- p.37 / Chapter 3.4.1 --- Space Complexity --- p.37 / Chapter 3.4.2 --- Generalization --- p.38 / Chapter 4 --- Consistency Notions and Algorithms for Set Variables --- p.41 / Chapter 4.1 --- Consistency Notions --- p.41 / Chapter 4.1.1 --- Element Node Consistency --- p.41 / Chapter 4.1.2 --- Element Arc Consistency --- p.43 / Chapter 4.1.3 --- Element Hyper-arc Consistency --- p.43 / Chapter 4.1.4 --- Weighted Cardinality Consistency --- p.45 / Chapter 4.1.5 --- Weighted Set Bounds Consistency --- p.46 / Chapter 4.2 --- Consistency Enforcing Algorithms --- p.47 / Chapter 4.2.1 --- "Enforcing Element, Node Consistency" --- p.48 / Chapter 4.2.2 --- Enforcing Element Arc Consistency --- p.51 / Chapter 4.2.3 --- Enforcing Element Hyper-arc Consistency --- p.52 / Chapter 4.2.4 --- Enforcing Weighted Cardinality Consistency --- p.54 / Chapter 4.2.5 --- Enforcing Weighted Set Bounds Consistency --- p.56 / Chapter 5 --- Experiments --- p.59 / Chapter 5.1 --- Modeling Set Variables Using 0-1 Variables --- p.60 / Chapter 5.2 --- Softening the Problems --- p.61 / Chapter 5.3 --- Steiner Triple System --- p.62 / Chapter 5.4 --- Social Golfer Problem --- p.63 / Chapter 5.5 --- Discussions --- p.66 / Chapter 6 --- Related Work --- p.68 / Chapter 6.1 --- Other Consistency Notions in WCSPs --- p.68 / Chapter 6.1.1 --- Full Directional Arc Consistency --- p.68 / Chapter 6.1.2 --- Existential Directional Arc Consistency --- p.69 / Chapter 6.2 --- Classical CSPs with Set Variables --- p.70 / Chapter 6.2.1 --- Bounds Reasoning --- p.70 / Chapter 6.2.2 --- Cardinality Reasoning --- p.70 / Chapter 7 --- Concluding Remarks --- p.72 / Chapter 7.1 --- Contributions --- p.72 / Chapter 7.2 --- Future Work --- p.74 / List of Symbols --- p.76 / Bibliography --- p.79

Woo Hiu Chun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 91-99). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Constraint Satisfaction Problems --- p.1 / Chapter 1.2 --- Weighted Constraint Satisfaction Problems --- p.3 / Chapter 1.3 --- Redundant Modeling --- p.4 / Chapter 1.4 --- Motivations and Goals --- p.5 / Chapter 1.5 --- Outline of the Thesis --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Constraint Satisfaction Problems --- p.8 / Chapter 2.1.1 --- Backtracking Tree Search --- p.9 / Chapter 2.1.2 --- Local Consistencies --- p.12 / Chapter 2.1.3 --- Local Consistencies in Backtracking Search --- p.17 / Chapter 2.1.4 --- Permutation CSPs --- p.19 / Chapter 2.2 --- Weighted Constraint Satisfaction Problems --- p.20 / Chapter 2.2.1 --- Branch and Bound Search --- p.23 / Chapter 2.2.2 --- Local Consistencies --- p.26 / Chapter 2.2.3 --- Local Consistencies in Branch and Bound Search --- p.32 / Chapter 2.3 --- Redundant Modeling --- p.34 / Chapter 3 --- Generating Redundant WCSP Models --- p.37 / Chapter 3.1 --- Model Induction for CSPs --- p.38 / Chapter 3.1.1 --- Stated Constraints --- p.39 / Chapter 3.1.2 --- No-Double-Assignment Constraints --- p.39 / Chapter 3.1.3 --- At-Least-One-Assignment Constraints --- p.40 / Chapter 3.2 --- Generalized Model Induction for WCSPs --- p.43 / Chapter 4 --- Combining Mutually Redundant WCSPs --- p.47 / Chapter 4.1 --- Naive Approach --- p.47 / Chapter 4.2 --- Node Consistency Revisited --- p.51 / Chapter 4.2.1 --- Refining Node Consistency Definition --- p.52 / Chapter 4.2.2 --- Enforcing m-NC* c Algorithm --- p.55 / Chapter 4.3 --- Arc Consistency Revisited --- p.58 / Chapter 4.3.1 --- Refining Arc Consistency Definition --- p.60 / Chapter 4.3.2 --- Enforcing m-AC*c Algorithm --- p.62 / Chapter 5 --- Experiments --- p.67 / Chapter 5.1 --- Langford's Problem --- p.68 / Chapter 5.2 --- Latin Square Problem --- p.72 / Chapter 5.3 --- Discussion --- p.75 / Chapter 6 --- Related Work --- p.77 / Chapter 6.1 --- Soft Constraint Satisfaction Problems --- p.77 / Chapter 6.2 --- Other Local Consistencies in WCSPs --- p.79 / Chapter 6.2.1 --- Full Arc Consistency --- p.79 / Chapter 6.2.2 --- Pull Directional Arc Consistency --- p.81 / Chapter 6.2.3 --- Existential Directional Arc Consistency --- p.82 / Chapter 6.3 --- Redundant Modeling and Channeling Constraints --- p.83 / Chapter 7 --- Concluding Remarks --- p.85 / Chapter 7.1 --- Contributions --- p.85 / Chapter 7.2 --- Future Work --- p.87 / List of Symbols --- p.88 / Bibliography

Lam Yee Gordon. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 109-117). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Constraint Satisfaction Problems --- p.1 / Chapter 1.2 --- Motivations and Goals --- p.2 / Chapter 1.3 --- Outline of the Thesis --- p.4 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- CSP --- p.5 / Chapter 2.2 --- Classes of Variable --- p.6 / Chapter 2.3 --- Solution of a CSP --- p.7 / Chapter 2.4 --- Constraint Solving Techniques --- p.8 / Chapter 2.4.1 --- Local Consistencies --- p.8 / Chapter 2.4.2 --- Constraint Tightness --- p.10 / Chapter 2.4.3 --- Tree Search --- p.10 / Chapter 2.5 --- Graph --- p.14 / Chapter 3 --- Common Channeling Constraints --- p.16 / Chapter 3.1 --- Models --- p.16 / Chapter 3.2 --- Channeling Constraints --- p.17 / Chapter 3.2.1 --- Int-Int Channeling Constraint (II) --- p.18 / Chapter 3.2.2 --- Set-Int Channeling Constraint (SI) --- p.21 / Chapter 3.2.3 --- Set-Set Channeling Constraint (SS) --- p.24 / Chapter 3.2.4 --- Int-Bool Channeling Constraint (IB) --- p.25 / Chapter 3.2.5 --- Set-Bool Channeling Constraint (SB) --- p.27 / Chapter 3.2.6 --- Discussions --- p.29 / Chapter 4 --- Realization in Existing Solvers --- p.31 / Chapter 4.1 --- Implementation by if-and-only-if constraint --- p.32 / Chapter 4.1.1 --- "Realization of iff in CHIP, ECLiPSe, and SICStus Prolog" --- p.32 / Chapter 4.1.2 --- Realization of iff in Oz and ILOG Solver --- p.32 / Chapter 4.2 --- Implementations by Element Constraint --- p.38 / Chapter 4.2.1 --- "Realization of ele in CHIP, ECLiPSe, and SICStus Prolog" --- p.40 / Chapter 4.2.2 --- Realization of ele in Oz and ILOG Solver --- p.40 / Chapter 4.3 --- Global Constraint Implementations --- p.41 / Chapter 4.3.1 --- "Realization of glo in CHIP, SICStus Prolog, and ILOG Solver" --- p.42 / Chapter 5 --- Consistency Levels --- p.43 / Chapter 5.1 --- Int-Int Channeling (II) --- p.44 / Chapter 5.2 --- Set-Int Channeling (SI) --- p.49 / Chapter 5.3 --- Set-Set Channeling Constraints (SS) --- p.53 / Chapter 5.4 --- Int-Bool Channeling (IB) --- p.55 / Chapter 5.5 --- Set-Bool Channeling (SB) --- p.57 / Chapter 5.6 --- Discussion --- p.59 / Chapter 6 --- Algorithms and Implementation --- p.61 / Chapter 6.1 --- Source of Inefficiency --- p.62 / Chapter 6.2 --- Generalized Element Constraint Propagators --- p.63 / Chapter 6.3 --- Global Channeling Constraint --- p.66 / Chapter 6.3.1 --- Generalization of Existing Global Channeling Constraints --- p.66 / Chapter 6.3.2 --- Maintaining GAC on Int-Int Channeling Constraint --- p.68 / Chapter 7 --- Experiments --- p.72 / Chapter 7.1 --- Int-Int Channeling Constraint --- p.73 / Chapter 7.1.1 --- Efficient AC implementations --- p.74 / Chapter 7.1.2 --- GAC Implementations --- p.75 / Chapter 7.2 --- Set-Int Channeling Constraint --- p.83 / Chapter 7.3 --- Set-Set Channeling Constraint --- p.89 / Chapter 7.4 --- Int-Bool Channeling Constraint --- p.89 / Chapter 7.5 --- Set-Bool Channeling Constraint --- p.91 / Chapter 7.6 --- Discussion --- p.93 / Chapter 8 --- Related Work --- p.101 / Chapter 8.1 --- Empirical Studies --- p.101 / Chapter 8.2 --- Theoretical Studies --- p.102 / Chapter 8.3 --- Applications --- p.103 / Chapter 8.4 --- Other Kinds of Channeling Constraints --- p.104 / Chapter 9 --- Concluding Remarks --- p.106 / Chapter 9.1 --- Contributions --- p.106 / Chapter 9.2 --- Future Work --- p.108 / Bibliography --- p.109

by Law Hing Man, Hudson. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 109-112. / Acknowledgements / Abstract / Chapter Chapter I. --- Introduction --- p.1 / Chapter 1.1 --- Overview of the thesis --- p.2 / Chapter 1.2 --- Organization of the thesis --- p.7 / Chapter Chapter II. --- Artificial Neural Networks --- p.9 / Chapter 2.1 --- Architectures of Artificial Neural Networks --- p.10 / Chapter 2.1.1 --- Neuron Models --- p.10 / Chapter 2.1.2 --- Network Models --- p.12 / Chapter 2.2 --- Learning algorithms --- p.13 / Chapter Chapter III. --- From Logic Neuron to Non-Overlapped Trees --- p.15 / Chapter 3.1 --- Deterministic Logic Neuron (DLN) --- p.15 / Chapter 3.2 --- Probabilistic Logic Neuron (PLN) --- p.20 / Chapter 3.2.1 --- Well-behaved learning of orthogonal patterns in PLN network --- p.23 / Chapter 3.2.2 --- Well-behaved learning algorithm for non-orthogonal patterns --- p.23 / Chapter 3.3 --- Non-Overlapped Trees --- p.28 / Chapter 3.3.1 --- Homogeneous learning algorithm --- p.30 / Chapter 3.3.2 --- An external comparator --- p.34 / Chapter 3.3.3 --- Problems solved by NOTPLN --- p.35 / Chapter Chapter IV. --- Properties of NOTPLN --- p.37 / Chapter 4.1 --- Noise Insensitivity --- p.37 / Chapter 4.1.1 --- Noise insensitivity with one bit noise --- p.38 / Chapter 4.1.2 --- Noise insensitivity under different noise distributions --- p.40 / Chapter 4.2 --- Functionality --- p.46 / Chapter 4.3 --- Capacity --- p.49 / Chapter 4.4 --- Distributed representation --- p.50 / Chapter 4.5 --- Generalization --- p.51 / Chapter 4.5.1 --- Text-to-Phoneme Problem --- p.52 / Chapter 4.5.2 --- Automobile Learning --- p.53 / Chapter Chapter V. --- Learning Algorithms --- p.54 / Chapter 5.1 --- Presentation methods --- p.54 / Chapter 5.2 --- Learning algorithms --- p.56 / Chapter 5.2.1 --- Heterogeneous algorithm --- p.57 / Chapter 5.2.2 --- Conflict reduction agorithm --- p.61 / Chapter 5.3 --- Side effects of learning algorithms --- p.68 / Chapter 5.3.1 --- Existence of Side Effects --- p.68 / Chapter 5.3.2 --- Removal of Side Effects --- p.69 / Chapter Chapter VI. --- Practical Considerations --- p.71 / Chapter 6.1 --- Input size constraint --- p.71 / Chapter 6.2 --- Limitations of functionality --- p.72 / Chapter 6.3 --- Thermometer code --- p.72 / Chapter 6.4 --- Output definitions --- p.73 / Chapter 6.5 --- More trees for one bit --- p.74 / Chapter 6.6 --- Repeated recall --- p.75 / Chapter Chapter VII. --- Implementation and Simulations --- p.78 / Chapter 7.1 --- Implementation --- p.78 / Chapter 7.2 --- Simulations --- p.81 / Chapter 7.2.1 --- Parity learning --- p.81 / Chapter 7.2.2 --- Performance of learning algorithms under different hamming distances --- p.82 / Chapter 7.2.3 --- Performance of learning algorithms with different output size --- p.83 / Chapter 7.2.4 --- Numerals recognition and noise insensitivity --- p.84 / Chapter 7.2.5 --- Automobile learning and generalization --- p.86 / Chapter Chapter VIII. --- Spoken Numerals Recognition System based on NOTPLN --- p.89 / Chapter 8.1 --- End-point detection --- p.90 / Chapter 8.2 --- Linear Predictive Analysis --- p.91 / Chapter 8.3 --- Formant Frequency Extraction --- p.93 / Chapter 8.4 --- Coding --- p.95 / Chapter 8.5 --- Results and discussion --- p.96 / Chapter Chapter IX. --- Concluding Remarks --- p.97 / Chapter 9.1 --- Revisit of the contributions of the thesis --- p.97 / Chapter 9.2 --- Further researches --- p.99 / Chapter Appendix A --- Equation for calculating the probability of random selection --- p.102 / Chapter Appendix B --- Training sets with different hamming distances --- p.103 / Chapter Appendix C --- Set of numerals with their associated binary values --- p.107 / References --- p.109

by Wong, Yin Pong Anthony. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 101-107). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Constraint Satisfaction Problems --- p.2 / Chapter 1.2 --- Solution Stability in Dynamic Constraint Satisfaction Problems --- p.3 / Chapter 1.3 --- Motivation of the Research --- p.5 / Chapter 1.4 --- Overview of the Thesis --- p.5 / Chapter 2 --- Related Work --- p.7 / Chapter 2.1 --- Complete Search Algorithms --- p.7 / Chapter 2.1.1 --- DnAC-4 --- p.8 / Chapter 2.1.2 --- ac --- p.9 / Chapter 2.1.3 --- DnAC-6 --- p.9 / Chapter 2.2 --- Algorithms for Stability --- p.10 / Chapter 2.2.1 --- Bellicha --- p.10 / Chapter 2.2.2 --- Dynamic Dynamic Backtracking --- p.11 / Chapter 2.2.3 --- Wallace and Freuder --- p.12 / Chapter 2.2.4 --- Unimodular Probing --- p.13 / Chapter 2.2.5 --- Train Rescheduling --- p.14 / Chapter 2.3 --- Constrained Optimization Algorithms --- p.14 / Chapter 2.3.1 --- Guided Local Search --- p.14 / Chapter 2.3.2 --- Anytime CSA with Iterative Deepening --- p.15 / Chapter 2.4 --- A Real-life Application --- p.16 / Chapter 3 --- Background --- p.17 / Chapter 3.1 --- Fuzzy Constraint Satisfaction Problems --- p.17 / Chapter 3.2 --- Fuzzy GENET --- p.19 / Chapter 3.2.1 --- Network Architecture --- p.19 / Chapter 3.2.2 --- Convergence Procedure --- p.21 / Chapter 3.3 --- Deficiency in Fuzzy GENET --- p.24 / Chapter 3.4 --- Rectification of Fuzzy GENET --- p.26 / Chapter 4 --- Using Fuzzy GENET for Solving Stability Problems --- p.30 / Chapter 4.1 --- Modelling Stability Problems as FCSPs --- p.30 / Chapter 4.2 --- Extending Fuzzy GENET for Solving Stability Problems --- p.36 / Chapter 4.3 --- Experiments --- p.38 / Chapter 4.3.1 --- Dynamic CSP Generation --- p.39 / Chapter 4.3.2 --- Problems Using Hamming Distance Function --- p.41 / Chapter 4.3.2.1 --- Variation in Number of Variables --- p.42 / Chapter 4.3.2.2 --- Variation in Domain Size --- p.45 / Chapter 4.3.2.3 --- Variation in Density and Tightness --- p.47 / Chapter 4.3.3 --- Comparison in Using Different Thresholds --- p.47 / Chapter 4.3.4 --- Problems Using Manhattan Distance Function --- p.50 / Chapter 5 --- Enhancement of the Modelling Scheme --- p.56 / Chapter 5.1 --- Distance Bound --- p.56 / Chapter 5.2 --- Enhancement of Convergence Procedure --- p.57 / Chapter 5.3 --- Comparison with Optimal Solutions --- p.60 / Chapter 5.4 --- Comparison with Fuzzy GENET(dcsp) --- p.64 / Chapter 5.4.1 --- Medium-sized Problems --- p.64 / Chapter 5.4.2 --- The 150-10-15-15 Problem --- p.67 / Chapter 5.4.3 --- Variation in Density and Tightness --- p.73 / Chapter 5.4.4 --- Variation in Domain Size --- p.76 / Chapter 5.5 --- Analysis of Fuzzy GENET(dcsp2) --- p.94 / Chapter 6 --- Conclusion --- p.98 / Chapter 6.1 --- Contributions --- p.98 / Chapter 6.2 --- Future Work --- p.99 / Bibliography --- p.101