<p> In this dissertation, I introduce modularization as a means of efficiently solving problems represented by dynamic Bayesian networks and study the properties and effects of modularization relative to traditional solutions. Modularizing a Bayesian filter allows its results to be calculated faster than a traditional Bayesian filter. Traditional Bayesian filters can have issues when large problems must be solved within a short period of time. Modularization addresses this issue by dividing the full problem into a set of smaller problems that can then be solved with separate Bayesian filters. Since the time complexity of Bayesian filters is greater than linear, solving several smaller problems is cheaper than solving a single large problem. The cost of reassembling the results from the smaller problems is comparable to the cost of the smaller problems. This document introduces the concept of both exact and approximate modular Bayesian filters and describes how to design each of the elements of a modular Bayesian filters. These concepts are clarified by using a series of examples from the realm of vehicle state estimation and include the results of each stage of the algorithm creation in a simulated environment. A final section shows the implementation of a modular Bayesian filter in a real-world problem tasked with addressing the problem of vehicle state estimation in the face of transitory sensor failure. This section also includes all of the attending algorithms that allow the problem to be solved accurately and in real-time.</p>

M. Tech. Computer Science / This dissertation reports on the development of a new game playing technique based on Probabilistic Distance clustering (pd-clustering) method to evolve an Awale game player. Game playing is one classic and complex problems of artificial intelligence that has attracted the attention of researchers in computer science field of study.

Knowledge is used as a focal factor for competitive advantage, through effective and efficient performances by employees in many organisations. As a result, knowledgeable employees are expected to share their knowledge with others to increase innovation within the organisation. Unfortunately, this is not always the case. Generally, employees behave differently within an organisation. The main challenge is that no organisation has total control of its employees’ behaviour and actions. The behaviour and action has impact on how information systems are deployed for innovation, in creating competitive advantage. As a result, many systems have been deployed by different organisations in attempt to address this challenge for the interest. Others have deployed competitive intelligence products and services. This is primarily intended to provide decision - makers with information that can contribute to the innovative process in order to meet customer needs. For an organisation to survive, it must be able to innovate and market its innovations. Also, innovation creates uncertainty about its consequences in the mind of potential adopters. There exists a discrepancy between what customers perceive as their problems or needs and what organisations understand these problems to be. This study was conducted with the primary aim to understand the impact of Competitive Intelligence (CI) on Information systems (IS) innovation products and services in organisations. The case study research method was employed, using a financial organisation. The Innovation-decision process, from the perspective of Diffusion of Innovation theory (DOI) was applied in the data analysis.

In this study, the researchers investigate the relationship between thinking style preference, emotional intelligence and leadership effectiveness in an institution of higher education. The measuring instruments used were the Neethling Brain Preference Profile (NBPP) and the Mayer, Salovey and Caruso Emotional Intelligence Test (MSCEIT), as well as the Kouzes and Posner Leadership Practices Inventory (LPI). The sample comprised 138 managers within a higher education institution. The researchers found some evidence to support the relationship between thinking style, emotional intelligence (EI) and leadership effectiveness. The researchers concluded that facets of brain dominance and emotional intelligence may be potentially useful predictors of transformational leadership behaviours.

This dissertation is concerned with the development of neural network-based methods to the control of robot manipulators and focusses on three different approaches for this purpose. In the first approach, an implementation of an intelligent adaptive control strategy in the execution of complex trajectory tracking tasks by using multilayer neural networks is demonstrated by exploiting the pattern classification capability of these nets. The network training is provided by a rule-based controller which is programmed to switch an appropriate adaptive control algorithm for each component type of motion constituting the overall trajectory tracking task. The second approach is based on the capability of trained neural networks for approximating input-output mappings. The use of dynamical networks with recurrent connections and efficient supervised training policies for the identification and adaptive control of a nonlinear process are discussed and a decentralized adaptive control strategy for a class of nonlinear dynamical systems with specific application to robotic manipulators is presented. An effective integration of the modelling of inverse dynamics property of neural nets with the robustness to unknown disturbances property of variable structure control systems is considered as the third approach. This methodology yields a viable procedure for selecting the control parameters adaptively and for designing a model-following adaptive control scheme for a class of nonlinear dynamical systems with application to robot manipulators.

published_or_final_version / Computer Science / Master / Master of Philosophy

Artificial Intelligence Lab, Department of MIS, University of Arizona / The basic building block of a multilingual information retrieval system is the input system. Chinese and Japanese characters pose great challenges for the conventional 101 -key alphabet-based keyboard, because they are radical-based and number in the thousands. This paper reviews the development of various approaches and then presents a framework and working demonstrations of Chinese and Japanese input methods implemented in Java, which allow open deployment over the web to any platform, The demo includes both popular keyboard input methods and neural network handwriting recognition using a mouse or pen. This framework is able to accommodate future extension to other input mediums and languages of interest.

Artificial Intelligence Lab, Department of MIS, University of Arizona / The results of a study that involved the creation of knowledge bases of concepts from large, operational textual databases are reported. Two East-bloc computing knowledge bases, both based on a semantic network structure, were created automatically using two statistical algorithms. With the help of four East-bloc computing experts, we evaluated the two knowledge bases in detail in a concept-association experiment based on recall and recognition tests. In the experiment, one of the knowledge bases that exhibited the asymmetric link property out-performed all four experts in recalling relevant concepts in East-bloc computing. The knowledge base, which contained about 20,O00 concepts (nodes) and 280,O00 weighted relationships (links), was incorporated as a thesaurus-like component into an intelligent retrieval system. The system allowed users to perform semantics-based information management and information retrieval via interactive, conceptual relevance feedback.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This paper describes the development and testing of the Medical Concept Mapper, a tool designed to facilitate access to online medical information sources by providing users with appropriate medical search terms for their personal queries. Our system is valuable for patients whose knowledge of medical vocabularies is inadequate to find the desired information, and for medical experts who search for information outside their field of expertise. The Medical Concept Mapper maps synonyms and semantically related concepts to a user's query. The system is unique because it integrates our natural language processing tool, i.e., the Arizona (AZ) Noun Phraser, with human-created ontologies, the Unified Medical Language System (UMLS) and WordNet, and our computer generated Concept Space, into one system. Our unique contribution results from combining the UMLS Semantic Net with Concept Space in our deep semantic parsing (DSP) algorithm. This algorithm establishes a medical query context based on the UMLS Semantic Net, which allows Concept Space terms to be filtered so as to isolate related terms relevant to the query. We performed two user studies in which Medical Concept Mapper terms were compared against human experts' terms. We conclude that the AZ Noun Phraser is well suited to extract medical phrases from user queries, that WordNet is not well suited to provide strictly medical synonyms, that the UMLS Metathesaurus is well suited to provide medical synonyms, and that Concept Space is well suited to provide related medical terms, especially when these terms are limited by our DSP algorithm.

Artificial Intelligence Lab, Department of MIS, University of Arizona / We propose a methodology to collect, quantify and visualize qualitative consumer data. We employ a Web-based Group Support System (GSS), GSw,b, to elicit free-form comments and a prototype comment analysis support system to facilitate comment classification, categorization and visualization to measure attitudes. We argue that such a methodology is needed due to the proliferation of qualitative data, the limitations of qualitative data analysis and the dearth of methods to measure attitudes contained within free-form comments. We conducted two experiments to compare our methodology with two long-established traditional methods, Likert scale evaluations and first-week box office sales records. We found that our methodology provides equivalent and superior affective and evaluative attitude information, compared to Likert scale ratings. We also found that comment analysis more accurately reflected actual first-week box office sales than did Likert scale ratings. Comment analysis with the prototype tool was seventy-five percent more efficient than manual coding. We designed the prototype to generate visualizations to make sense of multiple attitude dimensions through at-a-glance understanding and comparative presentation. The methodology we propose overcomes drawbacks often associated with qualitative data analysis and offers marketers and researchers a method to measure attitudes from free-form comments. The results indicate that qualitative data in the form of freeform comments may be quantified and visualized to provide meaningful attitude assessment. Finally, we present future research directions to enhance data collection and the comment analysis support system.