A framework is presented for the acquisition of domain-specific knowledge from experts. This framework is referred to as the ENVIRONMENTAL HINTING (ENVHINT) framework. ENVHINT attempts to steer the expert's focus to the derivation of expert knowledge by embedding acquisition of expert knowledge in the dynamics of the environment which influenced expertise development. Within this framework, the research focuses on the development of cognitive structures which can be used to develop probing domain-specific questions. / Cognitive structures are developed from urban residents' repertory grids which are based on personal construct theory. A cognitive structure reveals dependencies in the form of construct equivalence classes and implications from one equivalence class to another. / Weights are assigned to the implication lines of a cognitive structure. They are obtained from a fuzzy grid, from which the cognitive structure is derived. The weights allow paths to be accessed according to relevancy of urban concerns. The relevancy strengths of paths are used to derive hinting domain-specific questions for experts. / Source: Dissertation Abstracts International, Volume: 54-04, Section: B, page: 2071. / Major Professor: Wyllis Bandler. / Thesis (Ph.D.)--The Florida State University, 1993.

Much of human reasoning deals with imprecise, incomplete or vague information. However, the currently available commercial data base management systems handle only "exact" data items. Therefore, there is a need for an information system that allows representation and manipulation of imprecise information. Furthermore, it is desirable that an information system provides means for "individualization" of data to reflect the user's perception of data. / The proposed Fuzzy Relational Data Base (FRDB) model based on the research in the fields of relational data bases and theories of fuzzy sets and possibility is designed to satisfy the need for individualization and imprecise information processing. A commercial relational data base (RIM) is used as a host for the implementation of a FRDB system to demonstrate the feasibility of the model. / Source: Dissertation Abstracts International, Volume: 44-11, Section: B, page: 3460. / Thesis (Ph.D.)--The Florida State University, 1983.

The main aim of this dissertation has been to mimic natural language processing capabilities of human beings in a natural language processing system. The design and the development of the Syntax-Semantics analyzer (SS-analyzer) and the use of fuzzy in various language processing stages form the main crux of this dissertation. / The SS-analyzer is made up of two main modules: the syntax module and the semantics module. The SS-analyzer processes the input natural language sentences in an incremental fashion. The syntax and the semantics analyzer work in a coordinated manner to extract the meaning out of the input natural language sentences. This extracted meaning is then represented in a fuzzy relational representation structure. / The semantic interpreter complements the SS-analyzer in determining the meaning of input sentences when they are grammatically incorrect or do not make sense semantically. If the SS-analyzer is unable to determine the meaning of the input sentences, the semantic interpreter uses the contextual knowledge to determine the meaning. A prototype natural language processing system has been developed to test these theories. / Source: Dissertation Abstracts International, Volume: 53-09, Section: B, page: 4783. / Major Professor: L. J. Kohout. / Thesis (Ph.D.)--The Florida State University, 1992.

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, fuzzy implication operators, and the fuzzy relation matrix, that are usually decided upon subjectively by an expert operator. The purpose of this dissertation is to develop an intelligent fuzzy control system that combines fuzzy controller and learning mechanism in a hybrid system. Such hybrid system, which allows for imprecise information and/or uncertain environments, is imperative to the process of developing effective robust control systems for a large number of important real-time industrial processes. It is shown in this dissertation that the performance of fuzzy control systems can be improved considerably if the fuzzy reasoning model is supplemented by learning mechanisms. Two learning mechanisms are proposed in this research: one that uses genetic algorithms and the other is based on the utilization of neural networks. The genetic algorithm enables us to generate an optimal set of parameters for the fuzzy reasoning model based on their initial subjective selection. The exploitation of this initial selection, i.e., knowledge of the domain, by the genetic algorithm leads to an improved performance of the fuzzy controller. The neural-fuzzy reasoning model combines the computational paradigms of neural network and fuzzy rule-based reasoning in a hybrid system that also leads to an improved performance of the fuzzy control system. / Source: Dissertation Abstracts International, Volume: 53-07, Section: B, page: 3605. / Major Professor: Abraham Kandel. / Thesis (Ph.D.)--The Florida State University, 1992.

Neural network based word embeddings have demonstrated outstanding results in a variety of tasks, and become a standard input for Natural Language Processing (NLP) related deep learning methods. Despite these representations are able to capture semantic regularities in languages, some general questions, e.g., "what kinds of semantic relations do the embeddings represent?" and "how could the semantic relations be retrieved from an embedding?" are not clear and very little relevant work has been done. In this study, we propose a new approach to exploring the semantic relations represented in neural embeddings based on WordNet and Unified Medical Language System (UMLS). Our study demonstrates that neural embeddings do prefer some semantic relations and that the neural embeddings also represent diverse semantic relations. Our study also finds that the Named Entity Recognition (NER)-based phrase composition outperforms Word2phrase and the word variants do not affect the performance on analogy and semantic relation tasks. / A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science. / Summer Semester 2017. / July 17, 2017. / semantic relation, word2vec, word embedding, WordNet / Includes bibliographical references. / Xiuwen Liu, Professor Directing Thesis; Zhe He, Committee Member; Peixiang Zhao, Committee Member.

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.